From cea42ca470b1dbeecfeee31bf14cce4ad8a621b2 Mon Sep 17 00:00:00 2001
From: Muhammad Shakir <46898829+mshakirDr@users.noreply.github.com>
Date: Sat, 4 Mar 2023 19:12:13 +0100
Subject: [PATCH 01/48] Fix hugging face error

Model should be loaded with an id to avoid this error:
huggingface_hub.utils._validators.HFValidationError: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: 'pyannote\segmentation'.
---
 whisperx/transcribe.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 88e348d..2efec7a 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -639,9 +639,10 @@ def cli():
     if vad_filter:
         if hf_token is None:
             print("Warning, no huggingface token used, needs to be saved in environment variable, otherwise will throw error loading VAD model...")
-        from pyannote.audio import Inference
+        from pyannote.audio import Inference, Model
         vad_pipeline = Inference(
-            "pyannote/segmentation",
+            Model.from_pretrained("pyannote/segmentation",
+                                  use_auth_token=hf_token),
             pre_aggregation_hook=lambda segmentation: segmentation,
             use_auth_token=hf_token,
             device=torch.device(device),

From 33dd3b9bcddb23e6f7d92b6fd61d342e5f63d7a1 Mon Sep 17 00:00:00 2001
From: "Fernando O. Gallego" <f.ortega.gallego@gmail.com>
Date: Fri, 24 Mar 2023 11:56:41 +0100
Subject: [PATCH 02/48] Update decoding.py

Changes from https://github.com/openai/whisper/pull/914/
---
 whisperx/decoding.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/whisperx/decoding.py b/whisperx/decoding.py
index bb70cc0..ca608ca 100644
--- a/whisperx/decoding.py
+++ b/whisperx/decoding.py
@@ -413,7 +413,8 @@ class ApplyTimestampRules(LogitFilter):
 
         # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
         for k in range(tokens.shape[0]):
-            seq = [t for t in tokens[k, self.sample_begin :].tolist()]
+            sampled_tokens = tokens[k, self.sample_begin :]
+            seq = [t for t in sampled_tokens.tolist()]
             last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
             penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
 
@@ -422,6 +423,11 @@ class ApplyTimestampRules(LogitFilter):
                     logits[k, self.tokenizer.timestamp_begin :] = -np.inf
                 else:  # cannot be normal text tokens
                     logits[k, : self.tokenizer.eot] = -np.inf
+                    
+            timestamps = sampled_tokens[sampled_tokens.ge(self.tokenizer.timestamp_begin)]
+            if timestamps.numel() > 0:
+                # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last
+                logits[k, self.tokenizer.timestamp_begin : timestamps[-1]] = -np.inf
 
         if tokens.shape[1] == self.sample_begin:
             # suppress generating non-timestamp tokens at the beginning

From 18b63d46e209a8e1806e68aaae61283de40c33f0 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Thu, 30 Mar 2023 05:31:57 +0100
Subject: [PATCH 03/48] skeleton v2

---
 examples/sample01.mp4                         |   Bin 744682 -> 0 bytes
 examples/sample01.wav                         |   Bin 1920184 -> 0 bytes
 examples/sample_de_01.wav                     |   Bin 3138026 -> 0 bytes
 examples/sample_fr_01.wav                     |   Bin 3781950 -> 0 bytes
 examples/sample_it_01.wav                     |   Bin 4081538 -> 0 bytes
 examples/sample_ja_01.wav                     |   Bin 5568958 -> 0 bytes
 examples/whisper/sample01.mp4                 |   Bin 450121 -> 0 bytes
 examples/whisper/sample01.wav.srt             |   140 -
 examples/whisper/sample_fr_01.wav.srt         |    92 -
 examples/whisper/sample_fr_01.wav.txt         |    23 -
 examples/whisper/sample_fr_01.wav.vtt         |    71 -
 examples/whisperx/sample01.mp4                |   Bin 683572 -> 0 bytes
 examples/whisperx/sample01.wav.ass            |   290 -
 examples/whisperx/sample01.wav.srt            |   140 -
 examples/whisperx/sample01.wav.word.srt       |   624 -
 examples/whisperx/sample_de_01.wav.ass        |   184 -
 examples/whisperx/sample_de_01.wav.srt        |    44 -
 examples/whisperx/sample_fr_01.wav.ass        |   199 -
 examples/whisperx/sample_fr_01.wav.srt        |   120 -
 examples/whisperx/sample_it_01.wav.ass        |   177 -
 examples/whisperx/sample_it_01.wav.srt        |    32 -
 requirements.txt                              |     7 +-
 setup.py                                      |     2 +-
 tests/jfk.flac                                |   Bin 1152693 -> 0 bytes
 tests/test_audio.py                           |    19 -
 tests/test_normalizer.py                      |    92 -
 tests/test_tokenizer.py                       |    14 -
 tests/test_transcribe.py                      |    20 -
 whisperx/__init__.py                          |   116 +-
 whisperx/alignment.py                         |     2 +-
 whisperx/asr.py                               |   428 +
 whisperx/assets/gpt2/merges.txt               | 50001 ----------------
 whisperx/assets/gpt2/special_tokens_map.json  |     1 -
 whisperx/assets/gpt2/tokenizer_config.json    |     1 -
 whisperx/assets/gpt2/vocab.json               |     1 -
 whisperx/assets/mel_filters.npz               |   Bin 2048 -> 0 bytes
 .../assets/multilingual/added_tokens.json     |     1 -
 whisperx/assets/multilingual/merges.txt       | 50000 ---------------
 .../multilingual/special_tokens_map.json      |     1 -
 .../assets/multilingual/tokenizer_config.json |     1 -
 whisperx/assets/multilingual/vocab.json       |     1 -
 whisperx/audio.py                             |   124 -
 whisperx/decoding.py                          |   716 -
 whisperx/diarize.py                           |    17 +-
 whisperx/model.py                             |   268 -
 whisperx/normalizers/__init__.py              |     2 -
 whisperx/normalizers/basic.py                 |    71 -
 whisperx/normalizers/english.json             |  1741 -
 whisperx/normalizers/english.py               |   543 -
 whisperx/tokenizer.py                         |   331 -
 whisperx/transcribe.py                        |   776 +-
 whisperx/utils.py                             |   140 +-
 whisperx/vad.py                               |   128 +-
 53 files changed, 752 insertions(+), 106949 deletions(-)
 delete mode 100644 examples/sample01.mp4
 delete mode 100644 examples/sample01.wav
 delete mode 100644 examples/sample_de_01.wav
 delete mode 100644 examples/sample_fr_01.wav
 delete mode 100644 examples/sample_it_01.wav
 delete mode 100644 examples/sample_ja_01.wav
 delete mode 100644 examples/whisper/sample01.mp4
 delete mode 100644 examples/whisper/sample01.wav.srt
 delete mode 100644 examples/whisper/sample_fr_01.wav.srt
 delete mode 100644 examples/whisper/sample_fr_01.wav.txt
 delete mode 100644 examples/whisper/sample_fr_01.wav.vtt
 delete mode 100644 examples/whisperx/sample01.mp4
 delete mode 100644 examples/whisperx/sample01.wav.ass
 delete mode 100644 examples/whisperx/sample01.wav.srt
 delete mode 100644 examples/whisperx/sample01.wav.word.srt
 delete mode 100644 examples/whisperx/sample_de_01.wav.ass
 delete mode 100644 examples/whisperx/sample_de_01.wav.srt
 delete mode 100644 examples/whisperx/sample_fr_01.wav.ass
 delete mode 100644 examples/whisperx/sample_fr_01.wav.srt
 delete mode 100644 examples/whisperx/sample_it_01.wav.ass
 delete mode 100644 examples/whisperx/sample_it_01.wav.srt
 delete mode 100644 tests/jfk.flac
 delete mode 100644 tests/test_audio.py
 delete mode 100644 tests/test_normalizer.py
 delete mode 100644 tests/test_tokenizer.py
 delete mode 100644 tests/test_transcribe.py
 create mode 100644 whisperx/asr.py
 delete mode 100644 whisperx/assets/gpt2/merges.txt
 delete mode 100644 whisperx/assets/gpt2/special_tokens_map.json
 delete mode 100644 whisperx/assets/gpt2/tokenizer_config.json
 delete mode 100644 whisperx/assets/gpt2/vocab.json
 delete mode 100755 whisperx/assets/mel_filters.npz
 delete mode 100644 whisperx/assets/multilingual/added_tokens.json
 delete mode 100644 whisperx/assets/multilingual/merges.txt
 delete mode 100644 whisperx/assets/multilingual/special_tokens_map.json
 delete mode 100644 whisperx/assets/multilingual/tokenizer_config.json
 delete mode 100644 whisperx/assets/multilingual/vocab.json
 delete mode 100644 whisperx/audio.py
 delete mode 100644 whisperx/decoding.py
 delete mode 100644 whisperx/model.py
 delete mode 100644 whisperx/normalizers/__init__.py
 delete mode 100644 whisperx/normalizers/basic.py
 delete mode 100644 whisperx/normalizers/english.json
 delete mode 100644 whisperx/normalizers/english.py
 delete mode 100644 whisperx/tokenizer.py

diff --git a/examples/sample01.mp4 b/examples/sample01.mp4
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diff --git a/examples/sample01.wav b/examples/sample01.wav
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diff --git a/examples/whisper/sample01.wav.srt b/examples/whisper/sample01.wav.srt
deleted file mode 100644
index 8d15c3e..0000000
--- a/examples/whisper/sample01.wav.srt
+++ /dev/null
@@ -1,140 +0,0 @@
-1
-00:00:00,000 --> 00:00:03,000
-Bella, Gloria, love.
-
-2
-00:00:03,000 --> 00:00:04,000
-Oh.
-
-3
-00:00:04,000 --> 00:00:05,000
-How are you?
-
-4
-00:00:05,000 --> 00:00:07,000
-Oh, I'm OK.
-
-5
-00:00:07,000 --> 00:00:08,000
-I will be.
-
-6
-00:00:08,000 --> 00:00:09,000
-I said she could stay with us tomorrow
-
-7
-00:00:09,000 --> 00:00:10,000
-just until she feels better.
-
-8
-00:00:10,000 --> 00:00:11,000
-Yeah.
-
-9
-00:00:11,000 --> 00:00:12,000
-Of course she can.
-
-10
-00:00:12,000 --> 00:00:14,000
-No, things won't be for long.
-
-11
-00:00:14,000 --> 00:00:16,000
-Well, you can stay as long as you want, my love.
-
-12
-00:00:16,000 --> 00:00:18,000
-I've really missed you.
-
-13
-00:00:18,000 --> 00:00:19,000
-Pops.
-
-14
-00:00:19,000 --> 00:00:20,000
-Great to see you, love.
-
-15
-00:00:20,000 --> 00:00:22,000
-Oh.
-
-16
-00:00:22,000 --> 00:00:23,000
-All right, shall we get you off to bed then?
-
-17
-00:00:23,000 --> 00:00:25,000
-You should have given me some warm.
-
-18
-00:00:25,000 --> 00:00:26,000
-I know.
-
-19
-00:00:26,000 --> 00:00:27,000
-I'll have to put the electric blanket on.
-
-20
-00:00:27,000 --> 00:00:28,000
-I'm sorry.
-
-21
-00:00:28,000 --> 00:00:29,000
-All right, Bella.
-
-22
-00:00:29,000 --> 00:00:31,000
-Freezing up there.
-
-23
-00:00:31,000 --> 00:00:34,000
-In a bedroom, Peter unpacks her suitcase.
-
-24
-00:00:34,000 --> 00:00:38,000
-The middle-aged woman opens her green case.
-
-25
-00:00:38,000 --> 00:00:39,000
-Do you want your PJs?
-
-26
-00:00:39,000 --> 00:00:40,000
-Yeah.
-
-27
-00:00:40,000 --> 00:00:42,000
-Yeah.
-
-28
-00:00:42,000 --> 00:00:45,000
-Lifting a bundle of pajamas, Peter finds a sheet of paper
-
-29
-00:00:45,000 --> 00:00:50,000
-labeled Lancaster North Hospital discharge sheet.
-
-30
-00:00:50,000 --> 00:00:52,000
-He closes the suitcase and brings Gloria the pajamas.
-
-31
-00:00:52,000 --> 00:00:54,000
-There you go.
-
-32
-00:00:54,000 --> 00:00:55,000
-Thank you.
-
-33
-00:00:55,000 --> 00:00:57,000
-He picks up the locket.
-
-34
-00:00:57,000 --> 00:00:59,000
-He kept it.
-
-35
-00:00:59,000 --> 00:01:28,000
-Oh, cool.
-
diff --git a/examples/whisper/sample_fr_01.wav.srt b/examples/whisper/sample_fr_01.wav.srt
deleted file mode 100644
index 9732d9a..0000000
--- a/examples/whisper/sample_fr_01.wav.srt
+++ /dev/null
@@ -1,92 +0,0 @@
-1
-00:00:00,000 --> 00:00:01,240
-Lâchez, c'est bon.
-
-2
-00:00:01,240 --> 00:00:02,240
-Ça va?
-
-3
-00:00:02,240 --> 00:00:03,240
-Oui.
-
-4
-00:00:03,240 --> 00:00:04,240
-Merci beaucoup.
-
-5
-00:00:04,240 --> 00:00:05,240
-Chèque ou espèce?
-
-6
-00:00:05,240 --> 00:00:08,640
-J'ai un chèque sur la commode, il est signé.
-
-7
-00:00:08,640 --> 00:00:09,640
-Je vais le repirer.
-
-8
-00:00:09,640 --> 00:00:10,640
-Ok.
-
-9
-00:00:10,640 --> 00:00:11,640
-Ouh là!
-
-10
-00:00:11,640 --> 00:00:12,640
-Venez.
-
-11
-00:00:12,640 --> 00:00:13,640
-Merci.
-
-12
-00:00:13,640 --> 00:00:14,640
-Ah! C'est qui?
-
-13
-00:00:14,640 --> 00:00:21,640
-C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-
-14
-00:00:21,640 --> 00:00:26,640
-Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-
-15
-00:00:26,640 --> 00:00:27,640
-Ça va?
-
-16
-00:00:27,640 --> 00:00:44,200
-Attendez, tout à l'heure là, c'était vous? Vous? Pas lui? Vous?
-
-17
-00:00:44,200 --> 00:00:48,360
-Vous avez tout à fait raison, M. Xanaquis, Malek est à l'interne brillant qui apprend
-
-18
-00:00:48,360 --> 00:00:49,360
-le métier avec moi.
-
-19
-00:00:49,360 --> 00:00:50,360
-Ah!
-
-20
-00:00:50,360 --> 00:00:51,360
-Bien.
-
-21
-00:00:51,360 --> 00:00:55,520
-Justement, il y a la famille Boboune qui m'attend pour une consultation.
-
-22
-00:00:55,520 --> 00:00:56,520
-Qui?
-
-23
-00:00:56,520 --> 00:00:57,760
-Faisons pas attendre les Boboune, allez.
-
diff --git a/examples/whisper/sample_fr_01.wav.txt b/examples/whisper/sample_fr_01.wav.txt
deleted file mode 100644
index 5e8c797..0000000
--- a/examples/whisper/sample_fr_01.wav.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-Lâchez, c'est bon.
-Ça va?
-Oui.
-Merci beaucoup.
-Chèque ou espèce?
-J'ai un chèque sur la commode, il est signé.
-Je vais le repirer.
-Ok.
-Ouh là!
-Venez.
-Merci.
-Ah! C'est qui?
-C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Ça va?
-Attendez, tout à l'heure là, c'était vous? Vous? Pas lui? Vous?
-Vous avez tout à fait raison, M. Xanaquis, Malek est à l'interne brillant qui apprend
-le métier avec moi.
-Ah!
-Bien.
-Justement, il y a la famille Boboune qui m'attend pour une consultation.
-Qui?
-Faisons pas attendre les Boboune, allez.
diff --git a/examples/whisper/sample_fr_01.wav.vtt b/examples/whisper/sample_fr_01.wav.vtt
deleted file mode 100644
index 3c5911d..0000000
--- a/examples/whisper/sample_fr_01.wav.vtt
+++ /dev/null
@@ -1,71 +0,0 @@
-WEBVTT
-
-00:00.000 --> 00:01.240
-Lâchez, c'est bon.
-
-00:01.240 --> 00:02.240
-Ça va?
-
-00:02.240 --> 00:03.240
-Oui.
-
-00:03.240 --> 00:04.240
-Merci beaucoup.
-
-00:04.240 --> 00:05.240
-Chèque ou espèce?
-
-00:05.240 --> 00:08.640
-J'ai un chèque sur la commode, il est signé.
-
-00:08.640 --> 00:09.640
-Je vais le repirer.
-
-00:09.640 --> 00:10.640
-Ok.
-
-00:10.640 --> 00:11.640
-Ouh là!
-
-00:11.640 --> 00:12.640
-Venez.
-
-00:12.640 --> 00:13.640
-Merci.
-
-00:13.640 --> 00:14.640
-Ah! C'est qui?
-
-00:14.640 --> 00:21.640
-C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-
-00:21.640 --> 00:26.640
-Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-
-00:26.640 --> 00:27.640
-Ça va?
-
-00:27.640 --> 00:44.200
-Attendez, tout à l'heure là, c'était vous? Vous? Pas lui? Vous?
-
-00:44.200 --> 00:48.360
-Vous avez tout à fait raison, M. Xanaquis, Malek est à l'interne brillant qui apprend
-
-00:48.360 --> 00:49.360
-le métier avec moi.
-
-00:49.360 --> 00:50.360
-Ah!
-
-00:50.360 --> 00:51.360
-Bien.
-
-00:51.360 --> 00:55.520
-Justement, il y a la famille Boboune qui m'attend pour une consultation.
-
-00:55.520 --> 00:56.520
-Qui?
-
-00:56.520 --> 00:57.760
-Faisons pas attendre les Boboune, allez.
-
diff --git a/examples/whisperx/sample01.mp4 b/examples/whisperx/sample01.mp4
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diff --git a/examples/whisperx/sample01.wav.ass b/examples/whisperx/sample01.wav.ass
deleted file mode 100644
index 35cc37f..0000000
--- a/examples/whisperx/sample01.wav.ass
+++ /dev/null
@@ -1,290 +0,0 @@
-[Script Info]
-ScriptType: v4.00+
-PlayResX: 384
-PlayResY: 288
-ScaledBorderAndShadow: yes
-
-[V4+ Styles]
-Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
-Style: Default,Arial,24,&Hffffff,&Hffffff,&H0,&H0,0,0,0,0,100,100,0,0,1,1,0,2,10,10,10,0
-
-[Events]
-Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
-
-Dialogue: 0,0:00:1.18,0:00:1.67,Default,,0,0,0,,{\1c&HFF00&\u1}Bella,{\r} Gloria, love.
-Dialogue: 0,0:00:1.67,0:00:2.65,Default,,0,0,0,,Bella, Gloria, love.
-Dialogue: 0,0:00:2.65,0:00:3.05,Default,,0,0,0,,Bella, {\1c&HFF00&\u1}Gloria,{\r} love.
-Dialogue: 0,0:00:3.05,0:00:3.07,Default,,0,0,0,,Bella, Gloria, love.
-Dialogue: 0,0:00:3.07,0:00:3.27,Default,,0,0,0,,Bella, Gloria, {\1c&HFF00&\u1}love.{\r}
-Dialogue: 0,0:00:3.75,0:00:3.85,Default,,0,0,0,,{\1c&HFF00&\u1}Oh.{\r}
-Dialogue: 0,0:00:4.50,0:00:4.72,Default,,0,0,0,,{\1c&HFF00&\u1}How{\r} are you?
-Dialogue: 0,0:00:4.72,0:00:5.78,Default,,0,0,0,,How are you?
-Dialogue: 0,0:00:5.78,0:00:5.90,Default,,0,0,0,,How {\1c&HFF00&\u1}are{\r} you?
-Dialogue: 0,0:00:5.90,0:00:5.94,Default,,0,0,0,,How are you?
-Dialogue: 0,0:00:5.94,0:00:6.22,Default,,0,0,0,,How are {\1c&HFF00&\u1}you?{\r}
-Dialogue: 0,0:00:6.72,0:00:6.80,Default,,0,0,0,,{\1c&HFF00&\u1}Oh,{\r} I'm OK.
-Dialogue: 0,0:00:6.80,0:00:6.88,Default,,0,0,0,,Oh, I'm OK.
-Dialogue: 0,0:00:6.88,0:00:7.04,Default,,0,0,0,,Oh, {\1c&HFF00&\u1}I'm{\r} OK.
-Dialogue: 0,0:00:7.04,0:00:7.09,Default,,0,0,0,,Oh, I'm OK.
-Dialogue: 0,0:00:7.09,0:00:7.13,Default,,0,0,0,,Oh, I'm {\1c&HFF00&\u1}OK.{\r}
-Dialogue: 0,0:00:8.41,0:00:8.45,Default,,0,0,0,,{\1c&HFF00&\u1}I{\r} will be.
-Dialogue: 0,0:00:8.45,0:00:8.49,Default,,0,0,0,,I will be.
-Dialogue: 0,0:00:8.49,0:00:8.73,Default,,0,0,0,,I {\1c&HFF00&\u1}will{\r} be.
-Dialogue: 0,0:00:8.73,0:00:8.77,Default,,0,0,0,,I will be.
-Dialogue: 0,0:00:8.77,0:00:8.91,Default,,0,0,0,,I will {\1c&HFF00&\u1}be.{\r}
-Dialogue: 0,0:00:9.22,0:00:9.30,Default,,0,0,0,,{\1c&HFF00&\u1}I{\r} said she could stay with us tomorrow
-Dialogue: 0,0:00:9.30,0:00:9.34,Default,,0,0,0,,I said she could stay with us tomorrow
-Dialogue: 0,0:00:9.34,0:00:9.48,Default,,0,0,0,,I {\1c&HFF00&\u1}said{\r} she could stay with us tomorrow
-Dialogue: 0,0:00:9.48,0:00:9.52,Default,,0,0,0,,I said she could stay with us tomorrow
-Dialogue: 0,0:00:9.52,0:00:9.60,Default,,0,0,0,,I said {\1c&HFF00&\u1}she{\r} could stay with us tomorrow
-Dialogue: 0,0:00:9.60,0:00:9.62,Default,,0,0,0,,I said she could stay with us tomorrow
-Dialogue: 0,0:00:9.62,0:00:9.76,Default,,0,0,0,,I said she {\1c&HFF00&\u1}could{\r} stay with us tomorrow
-Dialogue: 0,0:00:9.76,0:00:9.78,Default,,0,0,0,,I said she could stay with us tomorrow
-Dialogue: 0,0:00:9.78,0:00:9.94,Default,,0,0,0,,I said she could {\1c&HFF00&\u1}stay{\r} with us tomorrow
-Dialogue: 0,0:00:9.94,0:00:9.96,Default,,0,0,0,,I said she could stay with us tomorrow
-Dialogue: 0,0:00:9.96,0:00:10.08,Default,,0,0,0,,I said she could stay {\1c&HFF00&\u1}with{\r} us tomorrow
-Dialogue: 0,0:00:10.08,0:00:10.10,Default,,0,0,0,,I said she could stay with us tomorrow
-Dialogue: 0,0:00:10.10,0:00:10.14,Default,,0,0,0,,I said she could stay with {\1c&HFF00&\u1}us{\r} tomorrow
-Dialogue: 0,0:00:10.14,0:00:10.16,Default,,0,0,0,,I said she could stay with us tomorrow
-Dialogue: 0,0:00:10.16,0:00:10.44,Default,,0,0,0,,I said she could stay with us {\1c&HFF00&\u1}tomorrow{\r}
-Dialogue: 0,0:00:10.46,0:00:10.54,Default,,0,0,0,,{\1c&HFF00&\u1}just{\r} until she feels better.
-Dialogue: 0,0:00:10.54,0:00:10.56,Default,,0,0,0,,just until she feels better.
-Dialogue: 0,0:00:10.56,0:00:10.68,Default,,0,0,0,,just {\1c&HFF00&\u1}until{\r} she feels better.
-Dialogue: 0,0:00:10.68,0:00:10.70,Default,,0,0,0,,just until she feels better.
-Dialogue: 0,0:00:10.70,0:00:10.80,Default,,0,0,0,,just until {\1c&HFF00&\u1}she{\r} feels better.
-Dialogue: 0,0:00:10.80,0:00:10.82,Default,,0,0,0,,just until she feels better.
-Dialogue: 0,0:00:10.82,0:00:11.05,Default,,0,0,0,,just until she {\1c&HFF00&\u1}feels{\r} better.
-Dialogue: 0,0:00:11.05,0:00:11.09,Default,,0,0,0,,just until she feels better.
-Dialogue: 0,0:00:11.09,0:00:11.35,Default,,0,0,0,,just until she feels {\1c&HFF00&\u1}better.{\r}
-Dialogue: 0,0:00:11.73,0:00:11.95,Default,,0,0,0,,{\1c&HFF00&\u1}Yeah.{\r}
-Dialogue: 0,0:00:12.09,0:00:12.17,Default,,0,0,0,,{\1c&HFF00&\u1}Of{\r} course she can.
-Dialogue: 0,0:00:12.17,0:00:12.20,Default,,0,0,0,,Of course she can.
-Dialogue: 0,0:00:12.20,0:00:12.32,Default,,0,0,0,,Of {\1c&HFF00&\u1}course{\r} she can.
-Dialogue: 0,0:00:12.32,0:00:12.38,Default,,0,0,0,,Of course she can.
-Dialogue: 0,0:00:12.38,0:00:12.64,Default,,0,0,0,,Of course {\1c&HFF00&\u1}she{\r} can.
-Dialogue: 0,0:00:12.64,0:00:12.72,Default,,0,0,0,,Of course she can.
-Dialogue: 0,0:00:12.72,0:00:13.24,Default,,0,0,0,,Of course she {\1c&HFF00&\u1}can.{\r}
-Dialogue: 0,0:00:13.36,0:00:13.70,Default,,0,0,0,,{\1c&HFF00&\u1}No,{\r} things won't be for long.
-Dialogue: 0,0:00:13.70,0:00:13.82,Default,,0,0,0,,No, things won't be for long.
-Dialogue: 0,0:00:13.82,0:00:14.12,Default,,0,0,0,,No, {\1c&HFF00&\u1}things{\r} won't be for long.
-Dialogue: 0,0:00:14.12,0:00:14.19,Default,,0,0,0,,No, things won't be for long.
-Dialogue: 0,0:00:14.19,0:00:14.39,Default,,0,0,0,,No, things {\1c&HFF00&\u1}won't{\r} be for long.
-Dialogue: 0,0:00:14.39,0:00:14.43,Default,,0,0,0,,No, things won't be for long.
-Dialogue: 0,0:00:14.43,0:00:14.53,Default,,0,0,0,,No, things won't {\1c&HFF00&\u1}be{\r} for long.
-Dialogue: 0,0:00:14.53,0:00:14.59,Default,,0,0,0,,No, things won't be for long.
-Dialogue: 0,0:00:14.59,0:00:14.73,Default,,0,0,0,,No, things won't be {\1c&HFF00&\u1}for{\r} long.
-Dialogue: 0,0:00:14.73,0:00:14.81,Default,,0,0,0,,No, things won't be for long.
-Dialogue: 0,0:00:14.81,0:00:15.01,Default,,0,0,0,,No, things won't be for {\1c&HFF00&\u1}long.{\r}
-Dialogue: 0,0:00:15.17,0:00:15.41,Default,,0,0,0,,{\1c&HFF00&\u1}Well,{\r} you can stay as long as you want, my love.
-Dialogue: 0,0:00:15.41,0:00:15.43,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:15.43,0:00:15.51,Default,,0,0,0,,Well, {\1c&HFF00&\u1}you{\r} can stay as long as you want, my love.
-Dialogue: 0,0:00:15.51,0:00:15.55,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:15.55,0:00:15.69,Default,,0,0,0,,Well, you {\1c&HFF00&\u1}can{\r} stay as long as you want, my love.
-Dialogue: 0,0:00:15.69,0:00:15.75,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:15.75,0:00:15.89,Default,,0,0,0,,Well, you can {\1c&HFF00&\u1}stay{\r} as long as you want, my love.
-Dialogue: 0,0:00:15.89,0:00:15.95,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:15.95,0:00:16.01,Default,,0,0,0,,Well, you can stay {\1c&HFF00&\u1}as{\r} long as you want, my love.
-Dialogue: 0,0:00:16.01,0:00:16.05,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:16.05,0:00:16.20,Default,,0,0,0,,Well, you can stay as {\1c&HFF00&\u1}long{\r} as you want, my love.
-Dialogue: 0,0:00:16.20,0:00:16.24,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:16.24,0:00:16.28,Default,,0,0,0,,Well, you can stay as long {\1c&HFF00&\u1}as{\r} you want, my love.
-Dialogue: 0,0:00:16.28,0:00:16.30,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:16.30,0:00:16.42,Default,,0,0,0,,Well, you can stay as long as {\1c&HFF00&\u1}you{\r} want, my love.
-Dialogue: 0,0:00:16.42,0:00:16.46,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:16.46,0:00:16.66,Default,,0,0,0,,Well, you can stay as long as you {\1c&HFF00&\u1}want,{\r} my love.
-Dialogue: 0,0:00:16.66,0:00:16.72,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:16.72,0:00:16.92,Default,,0,0,0,,Well, you can stay as long as you want, {\1c&HFF00&\u1}my{\r} love.
-Dialogue: 0,0:00:16.92,0:00:16.96,Default,,0,0,0,,Well, you can stay as long as you want, my love.
-Dialogue: 0,0:00:16.96,0:00:17.34,Default,,0,0,0,,Well, you can stay as long as you want, my {\1c&HFF00&\u1}love.{\r}
-Dialogue: 0,0:00:17.62,0:00:17.86,Default,,0,0,0,,{\1c&HFF00&\u1}I've{\r} really missed you.
-Dialogue: 0,0:00:17.86,0:00:17.88,Default,,0,0,0,,I've really missed you.
-Dialogue: 0,0:00:17.88,0:00:18.14,Default,,0,0,0,,I've {\1c&HFF00&\u1}really{\r} missed you.
-Dialogue: 0,0:00:18.14,0:00:18.19,Default,,0,0,0,,I've really missed you.
-Dialogue: 0,0:00:18.19,0:00:18.59,Default,,0,0,0,,I've really {\1c&HFF00&\u1}missed{\r} you.
-Dialogue: 0,0:00:18.59,0:00:18.63,Default,,0,0,0,,I've really missed you.
-Dialogue: 0,0:00:18.63,0:00:18.81,Default,,0,0,0,,I've really missed {\1c&HFF00&\u1}you.{\r}
-Dialogue: 0,0:00:19.49,0:00:19.79,Default,,0,0,0,,{\1c&HFF00&\u1}Pops.{\r}
-Dialogue: 0,0:00:20.40,0:00:20.64,Default,,0,0,0,,{\1c&HFF00&\u1}Great{\r} to see you, love.
-Dialogue: 0,0:00:20.64,0:00:20.66,Default,,0,0,0,,Great to see you, love.
-Dialogue: 0,0:00:20.66,0:00:20.78,Default,,0,0,0,,Great {\1c&HFF00&\u1}to{\r} see you, love.
-Dialogue: 0,0:00:20.78,0:00:20.82,Default,,0,0,0,,Great to see you, love.
-Dialogue: 0,0:00:20.82,0:00:21.14,Default,,0,0,0,,Great to {\1c&HFF00&\u1}see{\r} you, love.
-Dialogue: 0,0:00:21.14,0:00:21.16,Default,,0,0,0,,Great to see you, love.
-Dialogue: 0,0:00:21.16,0:00:21.28,Default,,0,0,0,,Great to see {\1c&HFF00&\u1}you,{\r} love.
-Dialogue: 0,0:00:21.28,0:00:21.32,Default,,0,0,0,,Great to see you, love.
-Dialogue: 0,0:00:21.32,0:00:21.68,Default,,0,0,0,,Great to see you, {\1c&HFF00&\u1}love.{\r}
-Dialogue: 0,0:00:21.90,0:00:23.21,Default,,0,0,0,,{\1c&HFF00&\u1}Oh.{\r}
-Dialogue: 0,0:00:23.23,0:00:23.29,Default,,0,0,0,,{\1c&HFF00&\u1}All{\r} right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.29,0:00:23.31,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.31,0:00:23.41,Default,,0,0,0,,All {\1c&HFF00&\u1}right,{\r} shall we get you off to bed then?
-Dialogue: 0,0:00:23.41,0:00:23.43,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.43,0:00:23.55,Default,,0,0,0,,All right, {\1c&HFF00&\u1}shall{\r} we get you off to bed then?
-Dialogue: 0,0:00:23.55,0:00:23.57,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.57,0:00:23.65,Default,,0,0,0,,All right, shall {\1c&HFF00&\u1}we{\r} get you off to bed then?
-Dialogue: 0,0:00:23.65,0:00:23.67,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.67,0:00:23.74,Default,,0,0,0,,All right, shall we {\1c&HFF00&\u1}get{\r} you off to bed then?
-Dialogue: 0,0:00:23.74,0:00:23.76,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.76,0:00:23.82,Default,,0,0,0,,All right, shall we get {\1c&HFF00&\u1}you{\r} off to bed then?
-Dialogue: 0,0:00:23.82,0:00:23.84,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.84,0:00:23.94,Default,,0,0,0,,All right, shall we get you {\1c&HFF00&\u1}off{\r} to bed then?
-Dialogue: 0,0:00:23.94,0:00:23.96,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:23.96,0:00:24.04,Default,,0,0,0,,All right, shall we get you off {\1c&HFF00&\u1}to{\r} bed then?
-Dialogue: 0,0:00:24.04,0:00:24.06,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:24.06,0:00:24.22,Default,,0,0,0,,All right, shall we get you off to {\1c&HFF00&\u1}bed{\r} then?
-Dialogue: 0,0:00:24.22,0:00:24.24,Default,,0,0,0,,All right, shall we get you off to bed then?
-Dialogue: 0,0:00:24.24,0:00:24.38,Default,,0,0,0,,All right, shall we get you off to bed {\1c&HFF00&\u1}then?{\r}
-Dialogue: 0,0:00:24.58,0:00:24.72,Default,,0,0,0,,{\1c&HFF00&\u1}You{\r} should have given me some warm.
-Dialogue: 0,0:00:24.72,0:00:24.78,Default,,0,0,0,,You should have given me some warm.
-Dialogue: 0,0:00:24.78,0:00:24.98,Default,,0,0,0,,You {\1c&HFF00&\u1}should{\r} have given me some warm.
-Dialogue: 0,0:00:24.98,0:00:25.02,Default,,0,0,0,,You should have given me some warm.
-Dialogue: 0,0:00:25.02,0:00:25.12,Default,,0,0,0,,You should {\1c&HFF00&\u1}have{\r} given me some warm.
-Dialogue: 0,0:00:25.12,0:00:25.16,Default,,0,0,0,,You should have given me some warm.
-Dialogue: 0,0:00:25.16,0:00:25.29,Default,,0,0,0,,You should have {\1c&HFF00&\u1}given{\r} me some warm.
-Dialogue: 0,0:00:25.29,0:00:25.35,Default,,0,0,0,,You should have given me some warm.
-Dialogue: 0,0:00:25.35,0:00:25.45,Default,,0,0,0,,You should have given {\1c&HFF00&\u1}me{\r} some warm.
-Dialogue: 0,0:00:25.45,0:00:25.49,Default,,0,0,0,,You should have given me some warm.
-Dialogue: 0,0:00:25.49,0:00:25.67,Default,,0,0,0,,You should have given me {\1c&HFF00&\u1}some{\r} warm.
-Dialogue: 0,0:00:25.67,0:00:25.81,Default,,0,0,0,,You should have given me some warm.
-Dialogue: 0,0:00:25.81,0:00:26.05,Default,,0,0,0,,You should have given me some {\1c&HFF00&\u1}warm.{\r}
-Dialogue: 0,0:00:26.31,0:00:26.37,Default,,0,0,0,,{\1c&HFF00&\u1}I{\r} know.
-Dialogue: 0,0:00:26.37,0:00:26.39,Default,,0,0,0,,I know.
-Dialogue: 0,0:00:26.39,0:00:26.49,Default,,0,0,0,,I {\1c&HFF00&\u1}know.{\r}
-Dialogue: 0,0:00:26.61,0:00:26.69,Default,,0,0,0,,{\1c&HFF00&\u1}I'll{\r} have to put the electric blanket on.
-Dialogue: 0,0:00:26.69,0:00:26.71,Default,,0,0,0,,I'll have to put the electric blanket on.
-Dialogue: 0,0:00:26.71,0:00:26.81,Default,,0,0,0,,I'll {\1c&HFF00&\u1}have{\r} to put the electric blanket on.
-Dialogue: 0,0:00:26.81,0:00:26.83,Default,,0,0,0,,I'll have to put the electric blanket on.
-Dialogue: 0,0:00:26.83,0:00:27.02,Default,,0,0,0,,I'll have {\1c&HFF00&\u1}to{\r} put the electric blanket on.
-Dialogue: 0,0:00:27.02,0:00:27.06,Default,,0,0,0,,I'll have to put the electric blanket on.
-Dialogue: 0,0:00:27.06,0:00:27.42,Default,,0,0,0,,I'll have to {\1c&HFF00&\u1}put{\r} the electric blanket on.
-Dialogue: 0,0:00:27.42,0:00:27.48,Default,,0,0,0,,I'll have to put the electric blanket on.
-Dialogue: 0,0:00:27.48,0:00:27.56,Default,,0,0,0,,I'll have to put {\1c&HFF00&\u1}the{\r} electric blanket on.
-Dialogue: 0,0:00:27.56,0:00:27.70,Default,,0,0,0,,I'll have to put the electric blanket on.
-Dialogue: 0,0:00:27.70,0:00:28.08,Default,,0,0,0,,I'll have to put the {\1c&HFF00&\u1}electric{\r} blanket on.
-Dialogue: 0,0:00:28.08,0:00:28.62,Default,,0,0,0,,I'll have to put the electric blanket on.
-Dialogue: 0,0:00:28.62,0:00:28.84,Default,,0,0,0,,I'll have to put the electric {\1c&HFF00&\u1}blanket{\r} on.
-Dialogue: 0,0:00:28.84,0:00:28.90,Default,,0,0,0,,I'll have to put the electric blanket on.
-Dialogue: 0,0:00:28.90,0:00:28.94,Default,,0,0,0,,I'll have to put the electric blanket {\1c&HFF00&\u1}on.{\r}
-Dialogue: 0,0:00:29.49,0:00:29.55,Default,,0,0,0,,{\1c&HFF00&\u1}I'm{\r} sorry.
-Dialogue: 0,0:00:29.55,0:00:29.57,Default,,0,0,0,,I'm sorry.
-Dialogue: 0,0:00:29.57,0:00:29.82,Default,,0,0,0,,I'm {\1c&HFF00&\u1}sorry.{\r}
-Dialogue: 0,0:00:29.98,0:00:30.08,Default,,0,0,0,,{\1c&HFF00&\u1}All{\r} right, Bella.
-Dialogue: 0,0:00:30.08,0:00:30.10,Default,,0,0,0,,All right, Bella.
-Dialogue: 0,0:00:30.10,0:00:30.29,Default,,0,0,0,,All {\1c&HFF00&\u1}right,{\r} Bella.
-Dialogue: 0,0:00:30.29,0:00:30.43,Default,,0,0,0,,All right, Bella.
-Dialogue: 0,0:00:30.43,0:00:30.63,Default,,0,0,0,,All right, {\1c&HFF00&\u1}Bella.{\r}
-Dialogue: 0,0:00:31.37,0:00:31.58,Default,,0,0,0,,{\1c&HFF00&\u1}Freezing{\r} up there.
-Dialogue: 0,0:00:31.58,0:00:31.62,Default,,0,0,0,,Freezing up there.
-Dialogue: 0,0:00:31.62,0:00:31.66,Default,,0,0,0,,Freezing {\1c&HFF00&\u1}up{\r} there.
-Dialogue: 0,0:00:31.66,0:00:31.68,Default,,0,0,0,,Freezing up there.
-Dialogue: 0,0:00:31.68,0:00:31.90,Default,,0,0,0,,Freezing up {\1c&HFF00&\u1}there.{\r}
-Dialogue: 0,0:00:31.90,0:00:31.94,Default,,0,0,0,,{\1c&HFF00&\u1}In{\r} a bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:31.94,0:00:31.96,Default,,0,0,0,,In a bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:31.96,0:00:31.98,Default,,0,0,0,,In {\1c&HFF00&\u1}a{\r} bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:31.98,0:00:32.00,Default,,0,0,0,,In a bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:32.00,0:00:32.14,Default,,0,0,0,,In a {\1c&HFF00&\u1}bedroom,{\r} Peter unpacks her suitcase.
-Dialogue: 0,0:00:32.14,0:00:32.20,Default,,0,0,0,,In a bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:32.20,0:00:32.50,Default,,0,0,0,,In a bedroom, {\1c&HFF00&\u1}Peter{\r} unpacks her suitcase.
-Dialogue: 0,0:00:32.50,0:00:32.58,Default,,0,0,0,,In a bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:32.58,0:00:32.98,Default,,0,0,0,,In a bedroom, Peter {\1c&HFF00&\u1}unpacks{\r} her suitcase.
-Dialogue: 0,0:00:32.98,0:00:33.00,Default,,0,0,0,,In a bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:33.00,0:00:33.10,Default,,0,0,0,,In a bedroom, Peter unpacks {\1c&HFF00&\u1}her{\r} suitcase.
-Dialogue: 0,0:00:33.10,0:00:33.16,Default,,0,0,0,,In a bedroom, Peter unpacks her suitcase.
-Dialogue: 0,0:00:33.16,0:00:33.65,Default,,0,0,0,,In a bedroom, Peter unpacks her {\1c&HFF00&\u1}suitcase.{\r}
-Dialogue: 0,0:00:34.27,0:00:34.35,Default,,0,0,0,,{\1c&HFF00&\u1}The{\r} middle-aged woman opens her green case.
-Dialogue: 0,0:00:34.35,0:00:34.39,Default,,0,0,0,,The middle-aged woman opens her green case.
-Dialogue: 0,0:00:34.39,0:00:34.91,Default,,0,0,0,,The {\1c&HFF00&\u1}middle-aged{\r} woman opens her green case.
-Dialogue: 0,0:00:34.91,0:00:34.99,Default,,0,0,0,,The middle-aged woman opens her green case.
-Dialogue: 0,0:00:34.99,0:00:35.27,Default,,0,0,0,,The middle-aged {\1c&HFF00&\u1}woman{\r} opens her green case.
-Dialogue: 0,0:00:35.27,0:00:35.39,Default,,0,0,0,,The middle-aged woman opens her green case.
-Dialogue: 0,0:00:35.39,0:00:35.67,Default,,0,0,0,,The middle-aged woman {\1c&HFF00&\u1}opens{\r} her green case.
-Dialogue: 0,0:00:35.67,0:00:35.71,Default,,0,0,0,,The middle-aged woman opens her green case.
-Dialogue: 0,0:00:35.71,0:00:35.81,Default,,0,0,0,,The middle-aged woman opens {\1c&HFF00&\u1}her{\r} green case.
-Dialogue: 0,0:00:35.81,0:00:35.85,Default,,0,0,0,,The middle-aged woman opens her green case.
-Dialogue: 0,0:00:35.85,0:00:36.19,Default,,0,0,0,,The middle-aged woman opens her {\1c&HFF00&\u1}green{\r} case.
-Dialogue: 0,0:00:36.19,0:00:36.23,Default,,0,0,0,,The middle-aged woman opens her green case.
-Dialogue: 0,0:00:36.23,0:00:36.53,Default,,0,0,0,,The middle-aged woman opens her green {\1c&HFF00&\u1}case.{\r}
-Dialogue: 0,0:00:38.13,0:00:38.25,Default,,0,0,0,,{\1c&HFF00&\u1}Do{\r} you want your PJs?
-Dialogue: 0,0:00:38.25,0:00:38.28,Default,,0,0,0,,Do you want your PJs?
-Dialogue: 0,0:00:38.28,0:00:38.36,Default,,0,0,0,,Do {\1c&HFF00&\u1}you{\r} want your PJs?
-Dialogue: 0,0:00:38.36,0:00:38.38,Default,,0,0,0,,Do you want your PJs?
-Dialogue: 0,0:00:38.38,0:00:38.54,Default,,0,0,0,,Do you {\1c&HFF00&\u1}want{\r} your PJs?
-Dialogue: 0,0:00:38.54,0:00:38.56,Default,,0,0,0,,Do you want your PJs?
-Dialogue: 0,0:00:38.56,0:00:38.74,Default,,0,0,0,,Do you want {\1c&HFF00&\u1}your{\r} PJs?
-Dialogue: 0,0:00:38.74,0:00:38.88,Default,,0,0,0,,Do you want your PJs?
-Dialogue: 0,0:00:38.88,0:00:39.30,Default,,0,0,0,,Do you want your {\1c&HFF00&\u1}PJs?{\r}
-Dialogue: 0,0:00:39.88,0:00:40.18,Default,,0,0,0,,{\1c&HFF00&\u1}Yeah.{\r}
-Dialogue: 0,0:00:42.39,0:00:42.69,Default,,0,0,0,,{\1c&HFF00&\u1}Lifting{\r} a bundle of pajamas,
-Dialogue: 0,0:00:42.69,0:00:42.73,Default,,0,0,0,,Lifting a bundle of pajamas,
-Dialogue: 0,0:00:42.73,0:00:42.75,Default,,0,0,0,,Lifting {\1c&HFF00&\u1}a{\r} bundle of pajamas,
-Dialogue: 0,0:00:42.75,0:00:42.81,Default,,0,0,0,,Lifting a bundle of pajamas,
-Dialogue: 0,0:00:42.81,0:00:43.11,Default,,0,0,0,,Lifting a {\1c&HFF00&\u1}bundle{\r} of pajamas,
-Dialogue: 0,0:00:43.11,0:00:43.13,Default,,0,0,0,,Lifting a bundle of pajamas,
-Dialogue: 0,0:00:43.13,0:00:43.19,Default,,0,0,0,,Lifting a bundle {\1c&HFF00&\u1}of{\r} pajamas,
-Dialogue: 0,0:00:43.19,0:00:43.25,Default,,0,0,0,,Lifting a bundle of pajamas,
-Dialogue: 0,0:00:43.25,0:00:43.77,Default,,0,0,0,,Lifting a bundle of {\1c&HFF00&\u1}pajamas,{\r}
-Dialogue: 0,0:00:44.07,0:00:44.31,Default,,0,0,0,,{\1c&HFF00&\u1}Peter{\r} finds a sheet of paper labeled
-Dialogue: 0,0:00:44.31,0:00:44.37,Default,,0,0,0,,Peter finds a sheet of paper labeled
-Dialogue: 0,0:00:44.37,0:00:44.63,Default,,0,0,0,,Peter {\1c&HFF00&\u1}finds{\r} a sheet of paper labeled
-Dialogue: 0,0:00:44.63,0:00:44.67,Default,,0,0,0,,Peter finds a sheet of paper labeled
-Dialogue: 0,0:00:44.67,0:00:44.69,Default,,0,0,0,,Peter finds {\1c&HFF00&\u1}a{\r} sheet of paper labeled
-Dialogue: 0,0:00:44.69,0:00:44.75,Default,,0,0,0,,Peter finds a sheet of paper labeled
-Dialogue: 0,0:00:44.75,0:00:44.95,Default,,0,0,0,,Peter finds a {\1c&HFF00&\u1}sheet{\r} of paper labeled
-Dialogue: 0,0:00:44.95,0:00:44.99,Default,,0,0,0,,Peter finds a sheet of paper labeled
-Dialogue: 0,0:00:44.99,0:00:45.05,Default,,0,0,0,,Peter finds a sheet {\1c&HFF00&\u1}of{\r} paper labeled
-Dialogue: 0,0:00:45.05,0:00:45.11,Default,,0,0,0,,Peter finds a sheet of paper labeled
-Dialogue: 0,0:00:45.11,0:00:45.46,Default,,0,0,0,,Peter finds a sheet of {\1c&HFF00&\u1}paper{\r} labeled
-Dialogue: 0,0:00:45.46,0:00:45.54,Default,,0,0,0,,Peter finds a sheet of paper labeled
-Dialogue: 0,0:00:45.54,0:00:45.88,Default,,0,0,0,,Peter finds a sheet of paper {\1c&HFF00&\u1}labeled{\r}
-Dialogue: 0,0:00:46.34,0:00:47.04,Default,,0,0,0,,{\1c&HFF00&\u1}Lancaster{\r} North Hospital discharge sheet.
-Dialogue: 0,0:00:47.04,0:00:47.12,Default,,0,0,0,,Lancaster North Hospital discharge sheet.
-Dialogue: 0,0:00:47.12,0:00:47.38,Default,,0,0,0,,Lancaster {\1c&HFF00&\u1}North{\r} Hospital discharge sheet.
-Dialogue: 0,0:00:47.38,0:00:47.44,Default,,0,0,0,,Lancaster North Hospital discharge sheet.
-Dialogue: 0,0:00:47.44,0:00:47.94,Default,,0,0,0,,Lancaster North {\1c&HFF00&\u1}Hospital{\r} discharge sheet.
-Dialogue: 0,0:00:47.94,0:00:48.27,Default,,0,0,0,,Lancaster North Hospital discharge sheet.
-Dialogue: 0,0:00:48.27,0:00:48.93,Default,,0,0,0,,Lancaster North Hospital {\1c&HFF00&\u1}discharge{\r} sheet.
-Dialogue: 0,0:00:48.93,0:00:49.03,Default,,0,0,0,,Lancaster North Hospital discharge sheet.
-Dialogue: 0,0:00:49.03,0:00:49.25,Default,,0,0,0,,Lancaster North Hospital discharge {\1c&HFF00&\u1}sheet.{\r}
-Dialogue: 0,0:00:50.29,0:00:50.37,Default,,0,0,0,,{\1c&HFF00&\u1}He{\r} closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:50.37,0:00:50.41,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:50.41,0:00:50.77,Default,,0,0,0,,He {\1c&HFF00&\u1}closes{\r} the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:50.77,0:00:50.81,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:50.81,0:00:50.91,Default,,0,0,0,,He closes {\1c&HFF00&\u1}the{\r} suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:50.91,0:00:50.95,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:50.95,0:00:51.39,Default,,0,0,0,,He closes the {\1c&HFF00&\u1}suitcase{\r} and brings Gloria the pajamas.
-Dialogue: 0,0:00:51.39,0:00:51.43,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:51.43,0:00:51.51,Default,,0,0,0,,He closes the suitcase {\1c&HFF00&\u1}and{\r} brings Gloria the pajamas.
-Dialogue: 0,0:00:51.51,0:00:51.53,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:51.53,0:00:51.79,Default,,0,0,0,,He closes the suitcase and {\1c&HFF00&\u1}brings{\r} Gloria the pajamas.
-Dialogue: 0,0:00:51.79,0:00:51.83,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:51.83,0:00:52.23,Default,,0,0,0,,He closes the suitcase and brings {\1c&HFF00&\u1}Gloria{\r} the pajamas.
-Dialogue: 0,0:00:52.23,0:00:52.25,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:52.25,0:00:52.32,Default,,0,0,0,,He closes the suitcase and brings Gloria {\1c&HFF00&\u1}the{\r} pajamas.
-Dialogue: 0,0:00:52.32,0:00:52.36,Default,,0,0,0,,He closes the suitcase and brings Gloria the pajamas.
-Dialogue: 0,0:00:52.36,0:00:52.86,Default,,0,0,0,,He closes the suitcase and brings Gloria the {\1c&HFF00&\u1}pajamas.{\r}
-Dialogue: 0,0:00:54.19,0:00:54.49,Default,,0,0,0,,{\1c&HFF00&\u1}There{\r} you go.
-Dialogue: 0,0:00:54.49,0:00:54.55,Default,,0,0,0,,There you go.
-Dialogue: 0,0:00:54.55,0:00:54.77,Default,,0,0,0,,There {\1c&HFF00&\u1}you{\r} go.
-Dialogue: 0,0:00:54.77,0:00:54.79,Default,,0,0,0,,There you go.
-Dialogue: 0,0:00:54.79,0:00:54.83,Default,,0,0,0,,There you {\1c&HFF00&\u1}go.{\r}
-Dialogue: 0,0:00:55.65,0:00:55.77,Default,,0,0,0,,{\1c&HFF00&\u1}Thank{\r} you.
-Dialogue: 0,0:00:55.77,0:00:55.80,Default,,0,0,0,,Thank you.
-Dialogue: 0,0:00:55.80,0:00:55.90,Default,,0,0,0,,Thank {\1c&HFF00&\u1}you.{\r}
-Dialogue: 0,0:00:55.90,0:00:55.94,Default,,0,0,0,,{\1c&HFF00&\u1}He{\r} picks up the locket.
-Dialogue: 0,0:00:55.94,0:00:55.96,Default,,0,0,0,,He picks up the locket.
-Dialogue: 0,0:00:55.96,0:00:56.10,Default,,0,0,0,,He {\1c&HFF00&\u1}picks{\r} up the locket.
-Dialogue: 0,0:00:56.10,0:00:56.12,Default,,0,0,0,,He picks up the locket.
-Dialogue: 0,0:00:56.12,0:00:56.20,Default,,0,0,0,,He picks {\1c&HFF00&\u1}up{\r} the locket.
-Dialogue: 0,0:00:56.20,0:00:56.22,Default,,0,0,0,,He picks up the locket.
-Dialogue: 0,0:00:56.22,0:00:56.32,Default,,0,0,0,,He picks up {\1c&HFF00&\u1}the{\r} locket.
-Dialogue: 0,0:00:56.32,0:00:56.36,Default,,0,0,0,,He picks up the locket.
-Dialogue: 0,0:00:56.36,0:00:56.74,Default,,0,0,0,,He picks up the {\1c&HFF00&\u1}locket.{\r}
-Dialogue: 0,0:00:57.12,0:00:57.22,Default,,0,0,0,,{\1c&HFF00&\u1}You{\r} kept it.
-Dialogue: 0,0:00:57.22,0:00:57.27,Default,,0,0,0,,You kept it.
-Dialogue: 0,0:00:57.27,0:00:57.47,Default,,0,0,0,,You {\1c&HFF00&\u1}kept{\r} it.
-Dialogue: 0,0:00:57.47,0:00:57.55,Default,,0,0,0,,You kept it.
-Dialogue: 0,0:00:57.55,0:00:57.63,Default,,0,0,0,,You kept {\1c&HFF00&\u1}it.{\r}
-Dialogue: 0,0:00:58.87,0:00:58.99,Default,,0,0,0,,{\1c&HFF00&\u1}Oh,{\r} of course.
-Dialogue: 0,0:00:58.99,0:00:59.28,Default,,0,0,0,,Oh, of course.
-Dialogue: 0,0:00:59.28,0:00:59.58,Default,,0,0,0,,Oh, {\1c&HFF00&\u1}of{\r} course.
-Dialogue: 0,0:00:59.58,0:00:59.68,Default,,0,0,0,,Oh, of course.
-Dialogue: 0,0:00:59.68,0:00:59.96,Default,,0,0,0,,Oh, of {\1c&HFF00&\u1}course.{\r}
\ No newline at end of file
diff --git a/examples/whisperx/sample01.wav.srt b/examples/whisperx/sample01.wav.srt
deleted file mode 100644
index 2bce26e..0000000
--- a/examples/whisperx/sample01.wav.srt
+++ /dev/null
@@ -1,140 +0,0 @@
-1
-00:00:01,185 --> 00:00:03,273
-Bella, Gloria, love.
-
-2
-00:00:03,754 --> 00:00:03,855
-Oh.
-
-3
-00:00:04,496 --> 00:00:06,219
-How are you?
-
-4
-00:00:06,723 --> 00:00:07,126
-Oh, I'm OK.
-
-5
-00:00:08,412 --> 00:00:08,915
-I will be.
-
-6
-00:00:09,215 --> 00:00:10,439
-I said she could stay with us tomorrow
-
-7
-00:00:10,459 --> 00:00:11,351
-just until she feels better.
-
-8
-00:00:11,733 --> 00:00:11,954
-Yeah.
-
-9
-00:00:12,095 --> 00:00:13,238
-Of course she can.
-
-10
-00:00:13,359 --> 00:00:15,012
-No, things won't be for long.
-
-11
-00:00:15,173 --> 00:00:17,338
-Well, you can stay as long as you want, my love.
-
-12
-00:00:17,621 --> 00:00:18,810
-I've really missed you.
-
-13
-00:00:19,493 --> 00:00:19,795
-Pops.
-
-14
-00:00:20,396 --> 00:00:21,679
-Great to see you, love.
-
-15
-00:00:21,901 --> 00:00:23,213
-Oh.
-
-16
-00:00:23,233 --> 00:00:24,378
-All right, shall we get you off to bed then?
-
-17
-00:00:24,579 --> 00:00:26,052
-You should have given me some warm.
-
-18
-00:00:26,313 --> 00:00:26,494
-I know.
-
-19
-00:00:26,614 --> 00:00:28,940
-I'll have to put the electric blanket on.
-
-20
-00:00:29,490 --> 00:00:29,817
-I'm sorry.
-
-21
-00:00:29,980 --> 00:00:30,633
-All right, Bella.
-
-22
-00:00:31,375 --> 00:00:31,897
-Freezing up there.
-
-23
-00:00:31,897 --> 00:00:33,647
-In a bedroom, Peter unpacks her suitcase.
-
-24
-00:00:34,268 --> 00:00:36,533
-The middle-aged woman opens her green case.
-
-25
-00:00:38,135 --> 00:00:39,296
-Do you want your PJs?
-
-26
-00:00:39,879 --> 00:00:40,181
-Yeah.
-
-27
-00:00:42,388 --> 00:00:43,773
-Lifting a bundle of pajamas,
-
-28
-00:00:44,073 --> 00:00:45,876
-Peter finds a sheet of paper labeled
-
-29
-00:00:46,338 --> 00:00:49,249
-Lancaster North Hospital discharge sheet.
-
-30
-00:00:50,291 --> 00:00:52,856
-He closes the suitcase and brings Gloria the pajamas.
-
-31
-00:00:54,186 --> 00:00:54,831
-There you go.
-
-32
-00:00:55,654 --> 00:00:55,895
-Thank you.
-
-33
-00:00:55,895 --> 00:00:56,742
-He picks up the locket.
-
-34
-00:00:57,124 --> 00:00:57,627
-You kept it.
-
-35
-00:00:58,874 --> 00:00:59,960
-Oh, of course.
-
diff --git a/examples/whisperx/sample01.wav.word.srt b/examples/whisperx/sample01.wav.word.srt
deleted file mode 100644
index d7ec8fb..0000000
--- a/examples/whisperx/sample01.wav.word.srt
+++ /dev/null
@@ -1,624 +0,0 @@
-1
-00:00:01,185 --> 00:00:01,667
-Bella,
-
-2
-00:00:02,651 --> 00:00:03,052
-Gloria,
-
-3
-00:00:03,072 --> 00:00:03,273
-love.
-
-4
-00:00:03,754 --> 00:00:03,855
-Oh.
-
-5
-00:00:04,496 --> 00:00:04,716
-How
-
-6
-00:00:05,778 --> 00:00:05,898
-are
-
-7
-00:00:05,938 --> 00:00:06,219
-you?
-
-8
-00:00:06,723 --> 00:00:06,803
-Oh,
-
-9
-00:00:06,884 --> 00:00:07,045
-I'm
-
-10
-00:00:07,085 --> 00:00:07,126
-OK.
-
-11
-00:00:08,412 --> 00:00:08,452
-I
-
-12
-00:00:08,492 --> 00:00:08,734
-will
-
-13
-00:00:08,774 --> 00:00:08,915
-be.
-
-14
-00:00:09,215 --> 00:00:09,296
-I
-
-15
-00:00:09,336 --> 00:00:09,476
-said
-
-16
-00:00:09,516 --> 00:00:09,596
-she
-
-17
-00:00:09,616 --> 00:00:09,757
-could
-
-18
-00:00:09,777 --> 00:00:09,937
-stay
-
-19
-00:00:09,957 --> 00:00:10,078
-with
-
-20
-00:00:10,098 --> 00:00:10,138
-us
-
-21
-00:00:10,158 --> 00:00:10,439
-tomorrow
-
-22
-00:00:10,459 --> 00:00:10,540
-just
-
-23
-00:00:10,560 --> 00:00:10,682
-until
-
-24
-00:00:10,702 --> 00:00:10,804
-she
-
-25
-00:00:10,824 --> 00:00:11,047
-feels
-
-26
-00:00:11,087 --> 00:00:11,351
-better.
-
-27
-00:00:11,733 --> 00:00:11,954
-Yeah.
-
-28
-00:00:12,095 --> 00:00:12,175
-Of
-
-29
-00:00:12,195 --> 00:00:12,315
-course
-
-30
-00:00:12,376 --> 00:00:12,636
-she
-
-31
-00:00:12,716 --> 00:00:13,238
-can.
-
-32
-00:00:13,359 --> 00:00:13,702
-No,
-
-33
-00:00:13,823 --> 00:00:14,125
-things
-
-34
-00:00:14,185 --> 00:00:14,387
-won't
-
-35
-00:00:14,427 --> 00:00:14,528
-be
-
-36
-00:00:14,589 --> 00:00:14,730
-for
-
-37
-00:00:14,810 --> 00:00:15,012
-long.
-
-38
-00:00:15,173 --> 00:00:15,413
-Well,
-
-39
-00:00:15,433 --> 00:00:15,513
-you
-
-40
-00:00:15,554 --> 00:00:15,694
-can
-
-41
-00:00:15,754 --> 00:00:15,894
-stay
-
-42
-00:00:15,955 --> 00:00:16,015
-as
-
-43
-00:00:16,055 --> 00:00:16,195
-long
-
-44
-00:00:16,235 --> 00:00:16,275
-as
-
-45
-00:00:16,295 --> 00:00:16,416
-you
-
-46
-00:00:16,456 --> 00:00:16,656
-want,
-
-47
-00:00:16,717 --> 00:00:16,917
-my
-
-48
-00:00:16,957 --> 00:00:17,338
-love.
-
-49
-00:00:17,621 --> 00:00:17,863
-I've
-
-50
-00:00:17,883 --> 00:00:18,145
-really
-
-51
-00:00:18,185 --> 00:00:18,588
-missed
-
-52
-00:00:18,629 --> 00:00:18,810
-you.
-
-53
-00:00:19,493 --> 00:00:19,795
-Pops.
-
-54
-00:00:20,396 --> 00:00:20,637
-Great
-
-55
-00:00:20,657 --> 00:00:20,777
-to
-
-56
-00:00:20,817 --> 00:00:21,138
-see
-
-57
-00:00:21,158 --> 00:00:21,278
-you,
-
-58
-00:00:21,318 --> 00:00:21,679
-love.
-
-59
-00:00:21,901 --> 00:00:23,213
-Oh.
-
-60
-00:00:23,233 --> 00:00:23,293
-All
-
-61
-00:00:23,313 --> 00:00:23,414
-right,
-
-62
-00:00:23,434 --> 00:00:23,554
-shall
-
-63
-00:00:23,574 --> 00:00:23,655
-we
-
-64
-00:00:23,675 --> 00:00:23,735
-get
-
-65
-00:00:23,755 --> 00:00:23,815
-you
-
-66
-00:00:23,835 --> 00:00:23,936
-off
-
-67
-00:00:23,956 --> 00:00:24,036
-to
-
-68
-00:00:24,056 --> 00:00:24,217
-bed
-
-69
-00:00:24,237 --> 00:00:24,378
-then?
-
-70
-00:00:24,579 --> 00:00:24,720
-You
-
-71
-00:00:24,781 --> 00:00:24,983
-should
-
-72
-00:00:25,023 --> 00:00:25,124
-have
-
-73
-00:00:25,164 --> 00:00:25,285
-given
-
-74
-00:00:25,346 --> 00:00:25,447
-me
-
-75
-00:00:25,487 --> 00:00:25,669
-some
-
-76
-00:00:25,810 --> 00:00:26,052
-warm.
-
-77
-00:00:26,313 --> 00:00:26,373
-I
-
-78
-00:00:26,393 --> 00:00:26,494
-know.
-
-79
-00:00:26,614 --> 00:00:26,694
-I'll
-
-80
-00:00:26,714 --> 00:00:26,815
-have
-
-81
-00:00:26,835 --> 00:00:27,015
-to
-
-82
-00:00:27,055 --> 00:00:27,416
-put
-
-83
-00:00:27,476 --> 00:00:27,556
-the
-
-84
-00:00:27,697 --> 00:00:28,078
-electric
-
-85
-00:00:28,619 --> 00:00:28,840
-blanket
-
-86
-00:00:28,900 --> 00:00:28,940
-on.
-
-87
-00:00:29,490 --> 00:00:29,551
-I'm
-
-88
-00:00:29,572 --> 00:00:29,817
-sorry.
-
-89
-00:00:29,980 --> 00:00:30,082
-All
-
-90
-00:00:30,102 --> 00:00:30,286
-right,
-
-91
-00:00:30,429 --> 00:00:30,633
-Bella.
-
-92
-00:00:31,375 --> 00:00:31,576
-Freezing
-
-93
-00:00:31,616 --> 00:00:31,656
-up
-
-94
-00:00:31,676 --> 00:00:31,897
-there.
-
-95
-00:00:31,897 --> 00:00:31,937
-In
-
-96
-00:00:31,957 --> 00:00:31,977
-a
-
-97
-00:00:31,997 --> 00:00:32,138
-bedroom,
-
-98
-00:00:32,198 --> 00:00:32,500
-Peter
-
-99
-00:00:32,581 --> 00:00:32,983
-unpacks
-
-100
-00:00:33,003 --> 00:00:33,103
-her
-
-101
-00:00:33,164 --> 00:00:33,647
-suitcase.
-
-102
-00:00:34,268 --> 00:00:34,348
-The
-
-103
-00:00:34,388 --> 00:00:34,909
-middle-aged
-
-104
-00:00:34,989 --> 00:00:35,270
-woman
-
-105
-00:00:35,390 --> 00:00:35,671
-opens
-
-106
-00:00:35,711 --> 00:00:35,811
-her
-
-107
-00:00:35,851 --> 00:00:36,192
-green
-
-108
-00:00:36,232 --> 00:00:36,533
-case.
-
-109
-00:00:38,135 --> 00:00:38,255
-Do
-
-110
-00:00:38,275 --> 00:00:38,355
-you
-
-111
-00:00:38,375 --> 00:00:38,535
-want
-
-112
-00:00:38,555 --> 00:00:38,736
-your
-
-113
-00:00:38,876 --> 00:00:39,296
-PJs?
-
-114
-00:00:39,879 --> 00:00:40,181
-Yeah.
-
-115
-00:00:42,388 --> 00:00:42,689
-Lifting
-
-116
-00:00:42,729 --> 00:00:42,749
-a
-
-117
-00:00:42,809 --> 00:00:43,110
-bundle
-
-118
-00:00:43,131 --> 00:00:43,191
-of
-
-119
-00:00:43,251 --> 00:00:43,773
-pajamas,
-
-120
-00:00:44,073 --> 00:00:44,314
-Peter
-
-121
-00:00:44,374 --> 00:00:44,634
-finds
-
-122
-00:00:44,674 --> 00:00:44,694
-a
-
-123
-00:00:44,754 --> 00:00:44,955
-sheet
-
-124
-00:00:44,995 --> 00:00:45,055
-of
-
-125
-00:00:45,115 --> 00:00:45,456
-paper
-
-126
-00:00:45,536 --> 00:00:45,876
-labeled
-
-127
-00:00:46,338 --> 00:00:47,041
-Lancaster
-
-128
-00:00:47,121 --> 00:00:47,382
-North
-
-129
-00:00:47,442 --> 00:00:47,944
-Hospital
-
-130
-00:00:48,266 --> 00:00:48,928
-discharge
-
-131
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-sheet.
-
-132
-00:00:50,291 --> 00:00:50,371
-He
-
-133
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-
-134
-00:00:50,812 --> 00:00:50,912
-the
-
-135
-00:00:50,953 --> 00:00:51,393
-suitcase
-
-136
-00:00:51,433 --> 00:00:51,514
-and
-
-137
-00:00:51,534 --> 00:00:51,794
-brings
-
-138
-00:00:51,834 --> 00:00:52,235
-Gloria
-
-139
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-the
-
-140
-00:00:52,355 --> 00:00:52,856
-pajamas.
-
-141
-00:00:54,186 --> 00:00:54,488
-There
-
-142
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-you
-
-143
-00:00:54,791 --> 00:00:54,831
-go.
-
-144
-00:00:55,654 --> 00:00:55,775
-Thank
-
-145
-00:00:55,795 --> 00:00:55,895
-you.
-
-146
-00:00:55,895 --> 00:00:55,936
-He
-
-147
-00:00:55,956 --> 00:00:56,097
-picks
-
-148
-00:00:56,117 --> 00:00:56,198
-up
-
-149
-00:00:56,218 --> 00:00:56,319
-the
-
-150
-00:00:56,359 --> 00:00:56,742
-locket.
-
-151
-00:00:57,124 --> 00:00:57,225
-You
-
-152
-00:00:57,265 --> 00:00:57,466
-kept
-
-153
-00:00:57,547 --> 00:00:57,627
-it.
-
-154
-00:00:58,874 --> 00:00:58,994
-Oh,
-
-155
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-of
-
-156
-00:00:59,678 --> 00:00:59,960
-course.
-
diff --git a/examples/whisperx/sample_de_01.wav.ass b/examples/whisperx/sample_de_01.wav.ass
deleted file mode 100644
index 8442d18..0000000
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-Dialogue: 0,0:00:19.36,0:00:19.88,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen {\1c&HFF00&\u1}Filmwelt{\r} ausleihen, zweifelsohne
-Dialogue: 0,0:00:19.88,0:00:19.94,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
-Dialogue: 0,0:00:19.94,0:00:20.48,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt {\1c&HFF00&\u1}ausleihen,{\r} zweifelsohne
-Dialogue: 0,0:00:20.48,0:00:20.52,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
-Dialogue: 0,0:00:20.52,0:00:21.22,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, {\1c&HFF00&\u1}zweifelsohne{\r}
-Dialogue: 0,0:00:22.10,0:00:22.36,Default,,0,0,0,,{\1c&HFF00&\u1}Sparen{\r} Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.36,0:00:22.38,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.38,0:00:22.48,Default,,0,0,0,,Sparen {\1c&HFF00&\u1}Sie{\r} sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.48,0:00:22.50,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.50,0:00:22.62,Default,,0,0,0,,Sparen Sie {\1c&HFF00&\u1}sich{\r} Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.62,0:00:22.64,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.64,0:00:22.83,Default,,0,0,0,,Sparen Sie sich {\1c&HFF00&\u1}Ihre{\r} Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.83,0:00:22.87,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:22.87,0:00:23.43,Default,,0,0,0,,Sparen Sie sich Ihre {\1c&HFF00&\u1}Komplimente,{\r} Sie alter Hund
-Dialogue: 0,0:00:23.43,0:00:23.45,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:23.45,0:00:23.55,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, {\1c&HFF00&\u1}Sie{\r} alter Hund
-Dialogue: 0,0:00:23.55,0:00:23.59,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:23.59,0:00:23.89,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie {\1c&HFF00&\u1}alter{\r} Hund
-Dialogue: 0,0:00:23.89,0:00:23.93,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter Hund
-Dialogue: 0,0:00:23.93,0:00:24.05,Default,,0,0,0,,Sparen Sie sich Ihre Komplimente, Sie alter {\1c&HFF00&\u1}Hund{\r}
-Dialogue: 0,0:00:24.57,0:00:24.65,Default,,0,0,0,,{\1c&HFF00&\u1}Ich{\r} kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:24.65,0:00:24.67,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:24.67,0:00:24.97,Default,,0,0,0,,Ich {\1c&HFF00&\u1}kenne{\r} zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:24.97,0:00:25.01,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.01,0:00:25.19,Default,,0,0,0,,Ich kenne {\1c&HFF00&\u1}zu{\r} viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.19,0:00:25.23,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.23,0:00:25.49,Default,,0,0,0,,Ich kenne zu {\1c&HFF00&\u1}viele{\r} von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.49,0:00:25.51,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.51,0:00:25.65,Default,,0,0,0,,Ich kenne zu viele {\1c&HFF00&\u1}von{\r} ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.65,0:00:25.69,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.69,0:00:25.95,Default,,0,0,0,,Ich kenne zu viele von {\1c&HFF00&\u1}ihren{\r} früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.95,0:00:25.99,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:25.99,0:00:26.41,Default,,0,0,0,,Ich kenne zu viele von ihren {\1c&HFF00&\u1}früheren{\r} Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:26.41,0:00:26.45,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:26.45,0:00:27.01,Default,,0,0,0,,Ich kenne zu viele von ihren früheren {\1c&HFF00&\u1}Eroberungen,{\r} als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.01,0:00:27.06,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.06,0:00:27.24,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, {\1c&HFF00&\u1}als{\r} dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.24,0:00:27.26,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.26,0:00:27.46,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als {\1c&HFF00&\u1}dass{\r} ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.46,0:00:27.50,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.50,0:00:27.60,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass {\1c&HFF00&\u1}ich{\r} in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.60,0:00:27.64,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.64,0:00:27.74,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich {\1c&HFF00&\u1}in{\r} ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.74,0:00:27.78,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:27.78,0:00:28.00,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in {\1c&HFF00&\u1}ihren{\r} Honigtopf treten könnte.
-Dialogue: 0,0:00:28.00,0:00:28.04,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:28.04,0:00:28.64,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren {\1c&HFF00&\u1}Honigtopf{\r} treten könnte.
-Dialogue: 0,0:00:28.64,0:00:28.68,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:28.68,0:00:28.96,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf {\1c&HFF00&\u1}treten{\r} könnte.
-Dialogue: 0,0:00:28.96,0:00:29.00,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-Dialogue: 0,0:00:29.00,0:00:29.24,Default,,0,0,0,,Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten {\1c&HFF00&\u1}könnte.{\r}
-Dialogue: 0,0:00:29.24,0:00:29.30,Default,,0,0,0,,{\1c&HFF00&\u1}Na{\r} im Ernst, was ist passiert?
-Dialogue: 0,0:00:29.30,0:00:29.32,Default,,0,0,0,,Na im Ernst, was ist passiert?
-Dialogue: 0,0:00:29.32,0:00:29.36,Default,,0,0,0,,Na {\1c&HFF00&\u1}im{\r} Ernst, was ist passiert?
-Dialogue: 0,0:00:29.36,0:00:29.38,Default,,0,0,0,,Na im Ernst, was ist passiert?
-Dialogue: 0,0:00:29.38,0:00:29.48,Default,,0,0,0,,Na im {\1c&HFF00&\u1}Ernst,{\r} was ist passiert?
-Dialogue: 0,0:00:29.48,0:00:29.50,Default,,0,0,0,,Na im Ernst, was ist passiert?
-Dialogue: 0,0:00:29.50,0:00:29.56,Default,,0,0,0,,Na im Ernst, {\1c&HFF00&\u1}was{\r} ist passiert?
-Dialogue: 0,0:00:29.56,0:00:29.58,Default,,0,0,0,,Na im Ernst, was ist passiert?
-Dialogue: 0,0:00:29.58,0:00:29.64,Default,,0,0,0,,Na im Ernst, was {\1c&HFF00&\u1}ist{\r} passiert?
-Dialogue: 0,0:00:29.64,0:00:29.66,Default,,0,0,0,,Na im Ernst, was ist passiert?
-Dialogue: 0,0:00:29.66,0:00:29.82,Default,,0,0,0,,Na im Ernst, was ist {\1c&HFF00&\u1}passiert?{\r}
-Dialogue: 0,0:00:30.78,0:00:32.27,Default,,0,0,0,,{\1c&HFF00&\u1}Tja,{\r} ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:32.27,0:00:32.33,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:32.33,0:00:32.49,Default,,0,0,0,,Tja, {\1c&HFF00&\u1}ich{\r} habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:32.49,0:00:32.53,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:32.53,0:00:32.79,Default,,0,0,0,,Tja, ich {\1c&HFF00&\u1}habe{\r} mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:32.79,0:00:32.83,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:32.83,0:00:33.83,Default,,0,0,0,,Tja, ich habe {\1c&HFF00&\u1}mich,{\r} dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:33.83,0:00:33.85,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:33.85,0:00:34.57,Default,,0,0,0,,Tja, ich habe mich, {\1c&HFF00&\u1}dummerweise{\r} muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:34.57,0:00:34.59,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:34.59,0:00:34.73,Default,,0,0,0,,Tja, ich habe mich, dummerweise {\1c&HFF00&\u1}muss{\r} ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:34.73,0:00:34.77,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:34.77,0:00:34.89,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss {\1c&HFF00&\u1}ich{\r} eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:34.89,0:00:34.93,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:34.93,0:00:36.83,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich {\1c&HFF00&\u1}eingestehen,{\r} im Bergsteigen versucht.
-Dialogue: 0,0:00:36.83,0:00:36.87,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:36.87,0:00:36.99,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, {\1c&HFF00&\u1}im{\r} Bergsteigen versucht.
-Dialogue: 0,0:00:36.99,0:00:37.03,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:37.03,0:00:37.76,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im {\1c&HFF00&\u1}Bergsteigen{\r} versucht.
-Dialogue: 0,0:00:37.76,0:00:37.78,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-Dialogue: 0,0:00:37.78,0:00:38.22,Default,,0,0,0,,Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen {\1c&HFF00&\u1}versucht.{\r}
-Dialogue: 0,0:00:41.23,0:00:41.85,Default,,0,0,0,,{\1c&HFF00&\u1}Bergsteigen?{\r} Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:41.85,0:00:41.87,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:41.87,0:00:42.13,Default,,0,0,0,,Bergsteigen? {\1c&HFF00&\u1}Dabei{\r} haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.13,0:00:42.15,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.15,0:00:42.33,Default,,0,0,0,,Bergsteigen? Dabei {\1c&HFF00&\u1}haben{\r} sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.33,0:00:42.37,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.37,0:00:42.51,Default,,0,0,0,,Bergsteigen? Dabei haben {\1c&HFF00&\u1}sie{\r} ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.51,0:00:42.55,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.55,0:00:42.61,Default,,0,0,0,,Bergsteigen? Dabei haben sie {\1c&HFF00&\u1}ihr{\r} Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.61,0:00:42.63,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.63,0:00:42.77,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr {\1c&HFF00&\u1}Bein{\r} verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.77,0:00:42.81,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:42.81,0:00:43.23,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein {\1c&HFF00&\u1}verletzt{\r} beim Bergsteigen?
-Dialogue: 0,0:00:43.23,0:00:43.27,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:43.27,0:00:43.39,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt {\1c&HFF00&\u1}beim{\r} Bergsteigen?
-Dialogue: 0,0:00:43.39,0:00:43.45,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-Dialogue: 0,0:00:43.45,0:00:44.13,Default,,0,0,0,,Bergsteigen? Dabei haben sie ihr Bein verletzt beim {\1c&HFF00&\u1}Bergsteigen?{\r}
-Dialogue: 0,0:00:44.54,0:00:44.62,Default,,0,0,0,,{\1c&HFF00&\u1}Ob{\r} sie es glauben oder nicht.
-Dialogue: 0,0:00:44.62,0:00:44.66,Default,,0,0,0,,Ob sie es glauben oder nicht.
-Dialogue: 0,0:00:44.66,0:00:44.72,Default,,0,0,0,,Ob {\1c&HFF00&\u1}sie{\r} es glauben oder nicht.
-Dialogue: 0,0:00:44.72,0:00:44.74,Default,,0,0,0,,Ob sie es glauben oder nicht.
-Dialogue: 0,0:00:44.74,0:00:44.80,Default,,0,0,0,,Ob sie {\1c&HFF00&\u1}es{\r} glauben oder nicht.
-Dialogue: 0,0:00:44.80,0:00:44.82,Default,,0,0,0,,Ob sie es glauben oder nicht.
-Dialogue: 0,0:00:44.82,0:00:45.14,Default,,0,0,0,,Ob sie es {\1c&HFF00&\u1}glauben{\r} oder nicht.
-Dialogue: 0,0:00:45.14,0:00:45.16,Default,,0,0,0,,Ob sie es glauben oder nicht.
-Dialogue: 0,0:00:45.16,0:00:45.46,Default,,0,0,0,,Ob sie es glauben {\1c&HFF00&\u1}oder{\r} nicht.
-Dialogue: 0,0:00:45.46,0:00:45.50,Default,,0,0,0,,Ob sie es glauben oder nicht.
-Dialogue: 0,0:00:45.50,0:00:45.66,Default,,0,0,0,,Ob sie es glauben oder {\1c&HFF00&\u1}nicht.{\r}
\ No newline at end of file
diff --git a/examples/whisperx/sample_de_01.wav.srt b/examples/whisperx/sample_de_01.wav.srt
deleted file mode 100644
index 32b5776..0000000
--- a/examples/whisperx/sample_de_01.wav.srt
+++ /dev/null
@@ -1,44 +0,0 @@
-1
-00:00:00,563 --> 00:00:01,869
-Weinlein von Hammersmann
-
-2
-00:00:06,187 --> 00:00:08,013
-Oberst Lande, es ist lange her
-
-3
-00:00:08,013 --> 00:00:08,534
-Schneide ich wie eh und je
-
-4
-00:00:13,987 --> 00:00:16,234
-Also was ist mit Ihrem wunderschönen Bein geschehen?
-
-5
-00:00:17,035 --> 00:00:21,218
-Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
-
-6
-00:00:22,102 --> 00:00:24,051
-Sparen Sie sich Ihre Komplimente, Sie alter Hund
-
-7
-00:00:24,572 --> 00:00:29,238
-Ich kenne zu viele von ihren früheren Eroberungen, als dass ich in ihren Honigtopf treten könnte.
-
-8
-00:00:29,238 --> 00:00:29,821
-Na im Ernst, was ist passiert?
-
-9
-00:00:30,783 --> 00:00:38,217
-Tja, ich habe mich, dummerweise muss ich eingestehen, im Bergsteigen versucht.
-
-10
-00:00:41,226 --> 00:00:44,135
-Bergsteigen? Dabei haben sie ihr Bein verletzt beim Bergsteigen?
-
-11
-00:00:44,535 --> 00:00:45,657
-Ob sie es glauben oder nicht.
-
diff --git a/examples/whisperx/sample_fr_01.wav.ass b/examples/whisperx/sample_fr_01.wav.ass
deleted file mode 100644
index 64a4b9e..0000000
--- a/examples/whisperx/sample_fr_01.wav.ass
+++ /dev/null
@@ -1,199 +0,0 @@
-[Script Info]
-ScriptType: v4.00+
-PlayResX: 384
-PlayResY: 288
-ScaledBorderAndShadow: yes
-
-[V4+ Styles]
-Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
-Style: Default,Arial,24,&Hffffff,&Hffffff,&H0,&H0,0,0,0,0,100,100,0,0,1,1,0,2,10,10,10,0
-
-[Events]
-Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
-
-Dialogue: 0,0:00:0.77,0:00:1.07,Default,,0,0,0,,{\1c&HFF00&\u1}Lâchez,{\r} c'est bon.
-Dialogue: 0,0:00:1.07,0:00:1.11,Default,,0,0,0,,Lâchez, c'est bon.
-Dialogue: 0,0:00:1.11,0:00:1.21,Default,,0,0,0,,Lâchez, {\1c&HFF00&\u1}c'est{\r} bon.
-Dialogue: 0,0:00:1.21,0:00:1.23,Default,,0,0,0,,Lâchez, c'est bon.
-Dialogue: 0,0:00:1.23,0:00:1.31,Default,,0,0,0,,Lâchez, c'est {\1c&HFF00&\u1}bon.{\r}
-Dialogue: 0,0:00:1.31,0:00:1.81,Default,,0,0,0,,{\1c&HFF00&\u1}Ça{\r} va?
-Dialogue: 0,0:00:1.81,0:00:1.83,Default,,0,0,0,,Ça va?
-Dialogue: 0,0:00:1.83,0:00:1.89,Default,,0,0,0,,Ça {\1c&HFF00&\u1}va?{\r}
-Dialogue: 0,0:00:1.91,0:00:1.97,Default,,0,0,0,,{\1c&HFF00&\u1}Oui.{\r}
-Dialogue: 0,0:00:1.97,0:00:3.27,Default,,0,0,0,,{\1c&HFF00&\u1}Merci{\r} beaucoup.
-Dialogue: 0,0:00:3.27,0:00:3.29,Default,,0,0,0,,Merci beaucoup.
-Dialogue: 0,0:00:3.29,0:00:3.49,Default,,0,0,0,,Merci {\1c&HFF00&\u1}beaucoup.{\r}
-Dialogue: 0,0:00:4.36,0:00:4.58,Default,,0,0,0,,{\1c&HFF00&\u1}Chèque{\r} ou espèce?
-Dialogue: 0,0:00:4.58,0:00:4.64,Default,,0,0,0,,Chèque ou espèce?
-Dialogue: 0,0:00:4.64,0:00:4.72,Default,,0,0,0,,Chèque {\1c&HFF00&\u1}ou{\r} espèce?
-Dialogue: 0,0:00:4.72,0:00:4.78,Default,,0,0,0,,Chèque ou espèce?
-Dialogue: 0,0:00:4.78,0:00:5.04,Default,,0,0,0,,Chèque ou {\1c&HFF00&\u1}espèce?{\r}
-Dialogue: 0,0:00:6.54,0:00:6.70,Default,,0,0,0,,{\1c&HFF00&\u1}J'ai{\r} laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:6.70,0:00:6.74,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:6.74,0:00:6.99,Default,,0,0,0,,J'ai {\1c&HFF00&\u1}laissé{\r} un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:6.99,0:00:7.03,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:7.03,0:00:7.09,Default,,0,0,0,,J'ai laissé {\1c&HFF00&\u1}un{\r} chèque sur la commode, il est signé.
-Dialogue: 0,0:00:7.09,0:00:7.13,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:7.13,0:00:7.33,Default,,0,0,0,,J'ai laissé un {\1c&HFF00&\u1}chèque{\r} sur la commode, il est signé.
-Dialogue: 0,0:00:7.33,0:00:7.35,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:7.35,0:00:7.49,Default,,0,0,0,,J'ai laissé un chèque {\1c&HFF00&\u1}sur{\r} la commode, il est signé.
-Dialogue: 0,0:00:7.49,0:00:7.51,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:7.51,0:00:7.59,Default,,0,0,0,,J'ai laissé un chèque sur {\1c&HFF00&\u1}la{\r} commode, il est signé.
-Dialogue: 0,0:00:7.59,0:00:7.63,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:7.63,0:00:7.91,Default,,0,0,0,,J'ai laissé un chèque sur la {\1c&HFF00&\u1}commode,{\r} il est signé.
-Dialogue: 0,0:00:7.91,0:00:7.99,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:7.99,0:00:8.03,Default,,0,0,0,,J'ai laissé un chèque sur la commode, {\1c&HFF00&\u1}il{\r} est signé.
-Dialogue: 0,0:00:8.03,0:00:8.09,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:8.09,0:00:8.19,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il {\1c&HFF00&\u1}est{\r} signé.
-Dialogue: 0,0:00:8.19,0:00:8.21,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est signé.
-Dialogue: 0,0:00:8.21,0:00:8.39,Default,,0,0,0,,J'ai laissé un chèque sur la commode, il est {\1c&HFF00&\u1}signé.{\r}
-Dialogue: 0,0:00:8.39,0:00:8.81,Default,,0,0,0,,{\1c&HFF00&\u1}Vous{\r} le remplirez.
-Dialogue: 0,0:00:8.81,0:00:8.83,Default,,0,0,0,,Vous le remplirez.
-Dialogue: 0,0:00:8.83,0:00:8.95,Default,,0,0,0,,Vous {\1c&HFF00&\u1}le{\r} remplirez.
-Dialogue: 0,0:00:8.95,0:00:8.97,Default,,0,0,0,,Vous le remplirez.
-Dialogue: 0,0:00:8.97,0:00:9.26,Default,,0,0,0,,Vous le {\1c&HFF00&\u1}remplirez.{\r}
-Dialogue: 0,0:00:9.28,0:00:9.34,Default,,0,0,0,,{\1c&HFF00&\u1}OK.{\r}
-Dialogue: 0,0:00:9.36,0:00:9.40,Default,,0,0,0,,{\1c&HFF00&\u1}Oh!{\r}
-Dialogue: 0,0:00:12.41,0:00:12.51,Default,,0,0,0,,{\1c&HFF00&\u1}Ouh{\r} là!
-Dialogue: 0,0:00:12.51,0:00:12.53,Default,,0,0,0,,Ouh là!
-Dialogue: 0,0:00:12.53,0:00:12.59,Default,,0,0,0,,Ouh {\1c&HFF00&\u1}là!{\r}
-Dialogue: 0,0:00:12.59,0:00:12.73,Default,,0,0,0,,{\1c&HFF00&\u1}Venez.{\r}
-Dialogue: 0,0:00:14.45,0:00:14.63,Default,,0,0,0,,{\1c&HFF00&\u1}Merci.{\r}
-Dialogue: 0,0:00:14.65,0:00:14.76,Default,,0,0,0,,{\1c&HFF00&\u1}Ah!{\r}
-Dialogue: 0,0:00:15.64,0:00:16.51,Default,,0,0,0,,{\1c&HFF00&\u1}C'est{\r} qui?
-Dialogue: 0,0:00:16.51,0:00:16.53,Default,,0,0,0,,C'est qui?
-Dialogue: 0,0:00:16.53,0:00:16.63,Default,,0,0,0,,C'est {\1c&HFF00&\u1}qui?{\r}
-Dialogue: 0,0:00:20.00,0:00:22.85,Default,,0,0,0,,{\1c&HFF00&\u1}C'est{\r} pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:22.85,0:00:22.87,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:22.87,0:00:22.93,Default,,0,0,0,,C'est {\1c&HFF00&\u1}pas{\r} vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:22.93,0:00:22.95,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:22.95,0:00:23.15,Default,,0,0,0,,C'est pas {\1c&HFF00&\u1}vrai,{\r} qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:23.15,0:00:23.17,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:23.17,0:00:23.35,Default,,0,0,0,,C'est pas vrai, {\1c&HFF00&\u1}qu'est-ce{\r} qu'il fout ici, ce con?
-Dialogue: 0,0:00:23.35,0:00:23.37,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:23.37,0:00:23.51,Default,,0,0,0,,C'est pas vrai, qu'est-ce {\1c&HFF00&\u1}qu'il{\r} fout ici, ce con?
-Dialogue: 0,0:00:23.51,0:00:23.53,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:23.53,0:00:23.67,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il {\1c&HFF00&\u1}fout{\r} ici, ce con?
-Dialogue: 0,0:00:23.67,0:00:23.73,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:23.73,0:00:23.95,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout {\1c&HFF00&\u1}ici,{\r} ce con?
-Dialogue: 0,0:00:23.95,0:00:23.99,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:23.99,0:00:24.11,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, {\1c&HFF00&\u1}ce{\r} con?
-Dialogue: 0,0:00:24.11,0:00:24.15,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-Dialogue: 0,0:00:24.15,0:00:24.23,Default,,0,0,0,,C'est pas vrai, qu'est-ce qu'il fout ici, ce {\1c&HFF00&\u1}con?{\r}
-Dialogue: 0,0:00:24.51,0:00:24.96,Default,,0,0,0,,{\1c&HFF00&\u1}Excusez-moi,{\r} mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:24.96,0:00:24.98,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:24.98,0:00:25.06,Default,,0,0,0,,Excusez-moi, {\1c&HFF00&\u1}mais{\r} je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.06,0:00:25.08,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.08,0:00:25.12,Default,,0,0,0,,Excusez-moi, mais {\1c&HFF00&\u1}je{\r} crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.12,0:00:25.14,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.14,0:00:25.26,Default,,0,0,0,,Excusez-moi, mais je {\1c&HFF00&\u1}crois{\r} que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.26,0:00:25.28,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.28,0:00:25.34,Default,,0,0,0,,Excusez-moi, mais je crois {\1c&HFF00&\u1}que{\r} j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.34,0:00:25.36,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.36,0:00:25.42,Default,,0,0,0,,Excusez-moi, mais je crois que {\1c&HFF00&\u1}j'ai{\r} oublié mon sac chez vous.
-Dialogue: 0,0:00:25.42,0:00:25.44,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.44,0:00:25.60,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai {\1c&HFF00&\u1}oublié{\r} mon sac chez vous.
-Dialogue: 0,0:00:25.60,0:00:25.62,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.62,0:00:25.76,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié {\1c&HFF00&\u1}mon{\r} sac chez vous.
-Dialogue: 0,0:00:25.76,0:00:25.78,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.78,0:00:25.94,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon {\1c&HFF00&\u1}sac{\r} chez vous.
-Dialogue: 0,0:00:25.94,0:00:25.96,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:25.96,0:00:26.04,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac {\1c&HFF00&\u1}chez{\r} vous.
-Dialogue: 0,0:00:26.04,0:00:26.06,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-Dialogue: 0,0:00:26.06,0:00:26.18,Default,,0,0,0,,Excusez-moi, mais je crois que j'ai oublié mon sac chez {\1c&HFF00&\u1}vous.{\r}
-Dialogue: 0,0:00:26.18,0:00:26.30,Default,,0,0,0,,{\1c&HFF00&\u1}Ça{\r} va?
-Dialogue: 0,0:00:26.30,0:00:26.32,Default,,0,0,0,,Ça va?
-Dialogue: 0,0:00:26.32,0:00:26.36,Default,,0,0,0,,Ça {\1c&HFF00&\u1}va?{\r}
-Dialogue: 0,0:00:31.04,0:00:31.24,Default,,0,0,0,,{\1c&HFF00&\u1}Attendez.{\r}
-Dialogue: 0,0:00:36.81,0:00:36.97,Default,,0,0,0,,{\1c&HFF00&\u1}Tout{\r} à l'heure, là, c'était...
-Dialogue: 0,0:00:36.97,0:00:37.01,Default,,0,0,0,,Tout à l'heure, là, c'était...
-Dialogue: 0,0:00:37.01,0:00:37.05,Default,,0,0,0,,Tout {\1c&HFF00&\u1}à{\r} l'heure, là, c'était...
-Dialogue: 0,0:00:37.05,0:00:37.09,Default,,0,0,0,,Tout à l'heure, là, c'était...
-Dialogue: 0,0:00:37.09,0:00:37.35,Default,,0,0,0,,Tout à {\1c&HFF00&\u1}l'heure,{\r} là, c'était...
-Dialogue: 0,0:00:37.35,0:00:37.39,Default,,0,0,0,,Tout à l'heure, là, c'était...
-Dialogue: 0,0:00:37.39,0:00:37.98,Default,,0,0,0,,Tout à l'heure, {\1c&HFF00&\u1}là,{\r} c'était...
-Dialogue: 0,0:00:37.98,0:00:38.02,Default,,0,0,0,,Tout à l'heure, là, c'était...
-Dialogue: 0,0:00:38.02,0:00:38.28,Default,,0,0,0,,Tout à l'heure, là, {\1c&HFF00&\u1}c'était...{\r}
-Dialogue: 0,0:00:38.28,0:00:38.36,Default,,0,0,0,,{\1c&HFF00&\u1}Vous?{\r}
-Dialogue: 0,0:00:39.12,0:00:39.24,Default,,0,0,0,,{\1c&HFF00&\u1}Vous?{\r} Pas...
-Dialogue: 0,0:00:39.24,0:00:39.30,Default,,0,0,0,,Vous? Pas...
-Dialogue: 0,0:00:39.30,0:00:39.42,Default,,0,0,0,,Vous? {\1c&HFF00&\u1}Pas...{\r}
-Dialogue: 0,0:00:39.42,0:00:39.53,Default,,0,0,0,,{\1c&HFF00&\u1}Pas{\r} lui? Vous?
-Dialogue: 0,0:00:39.53,0:00:39.55,Default,,0,0,0,,Pas lui? Vous?
-Dialogue: 0,0:00:39.55,0:00:39.61,Default,,0,0,0,,Pas {\1c&HFF00&\u1}lui?{\r} Vous?
-Dialogue: 0,0:00:39.61,0:00:39.63,Default,,0,0,0,,Pas lui? Vous?
-Dialogue: 0,0:00:39.63,0:00:39.71,Default,,0,0,0,,Pas lui? {\1c&HFF00&\u1}Vous?{\r}
-Dialogue: 0,0:00:44.19,0:00:44.35,Default,,0,0,0,,{\1c&HFF00&\u1}Vous{\r} avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.35,0:00:44.39,Default,,0,0,0,,Vous avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.39,0:00:44.62,Default,,0,0,0,,Vous {\1c&HFF00&\u1}avez{\r} tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.62,0:00:44.64,Default,,0,0,0,,Vous avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.64,0:00:44.80,Default,,0,0,0,,Vous avez {\1c&HFF00&\u1}tout{\r} à fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.80,0:00:44.82,Default,,0,0,0,,Vous avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.82,0:00:44.84,Default,,0,0,0,,Vous avez tout {\1c&HFF00&\u1}à{\r} fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.84,0:00:44.90,Default,,0,0,0,,Vous avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:44.90,0:00:45.04,Default,,0,0,0,,Vous avez tout à {\1c&HFF00&\u1}fait{\r} raison, M. Xenakis.
-Dialogue: 0,0:00:45.04,0:00:45.08,Default,,0,0,0,,Vous avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:45.08,0:00:45.38,Default,,0,0,0,,Vous avez tout à fait {\1c&HFF00&\u1}raison,{\r} M. Xenakis.
-Dialogue: 0,0:00:45.38,0:00:45.42,Default,,0,0,0,,Vous avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:45.42,0:00:45.62,Default,,0,0,0,,Vous avez tout à fait raison, {\1c&HFF00&\u1}M.{\r} Xenakis.
-Dialogue: 0,0:00:45.62,0:00:45.68,Default,,0,0,0,,Vous avez tout à fait raison, M. Xenakis.
-Dialogue: 0,0:00:45.68,0:00:45.98,Default,,0,0,0,,Vous avez tout à fait raison, M. {\1c&HFF00&\u1}Xenakis.{\r}
-Dialogue: 0,0:00:46.75,0:00:47.13,Default,,0,0,0,,{\1c&HFF00&\u1}Malek{\r} est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.13,0:00:47.15,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.15,0:00:47.27,Default,,0,0,0,,Malek {\1c&HFF00&\u1}est{\r} à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.27,0:00:47.31,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.31,0:00:47.37,Default,,0,0,0,,Malek est {\1c&HFF00&\u1}à{\r} l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.37,0:00:47.39,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.39,0:00:47.75,Default,,0,0,0,,Malek est à {\1c&HFF00&\u1}l'interne{\r} brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.75,0:00:47.79,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:47.79,0:00:48.07,Default,,0,0,0,,Malek est à l'interne {\1c&HFF00&\u1}brillant,{\r} qui apprend le métier avec moi.
-Dialogue: 0,0:00:48.07,0:00:48.11,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:48.11,0:00:48.19,Default,,0,0,0,,Malek est à l'interne brillant, {\1c&HFF00&\u1}qui{\r} apprend le métier avec moi.
-Dialogue: 0,0:00:48.19,0:00:48.23,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:48.23,0:00:48.44,Default,,0,0,0,,Malek est à l'interne brillant, qui {\1c&HFF00&\u1}apprend{\r} le métier avec moi.
-Dialogue: 0,0:00:48.44,0:00:48.46,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:48.46,0:00:48.52,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend {\1c&HFF00&\u1}le{\r} métier avec moi.
-Dialogue: 0,0:00:48.52,0:00:48.54,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:48.54,0:00:48.74,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le {\1c&HFF00&\u1}métier{\r} avec moi.
-Dialogue: 0,0:00:48.74,0:00:48.76,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:48.76,0:00:48.88,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier {\1c&HFF00&\u1}avec{\r} moi.
-Dialogue: 0,0:00:48.88,0:00:48.90,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec moi.
-Dialogue: 0,0:00:48.90,0:00:49.00,Default,,0,0,0,,Malek est à l'interne brillant, qui apprend le métier avec {\1c&HFF00&\u1}moi.{\r}
-Dialogue: 0,0:00:49.02,0:00:49.06,Default,,0,0,0,,{\1c&HFF00&\u1}Ah!{\r}
-Dialogue: 0,0:00:49.06,0:00:49.20,Default,,0,0,0,,{\1c&HFF00&\u1}C'est{\r} vrai.
-Dialogue: 0,0:00:49.20,0:00:49.22,Default,,0,0,0,,C'est vrai.
-Dialogue: 0,0:00:49.22,0:00:49.30,Default,,0,0,0,,C'est {\1c&HFF00&\u1}vrai.{\r}
-Dialogue: 0,0:00:49.30,0:00:49.44,Default,,0,0,0,,{\1c&HFF00&\u1}Bien.{\r}
-Dialogue: 0,0:00:52.93,0:00:53.21,Default,,0,0,0,,{\1c&HFF00&\u1}Justement,{\r} y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.21,0:00:53.33,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.33,0:00:53.35,Default,,0,0,0,,Justement, {\1c&HFF00&\u1}y{\r} a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.35,0:00:53.37,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.37,0:00:53.39,Default,,0,0,0,,Justement, y {\1c&HFF00&\u1}a{\r} la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.39,0:00:53.43,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.43,0:00:53.49,Default,,0,0,0,,Justement, y a {\1c&HFF00&\u1}la{\r} famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.49,0:00:53.51,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.51,0:00:53.85,Default,,0,0,0,,Justement, y a la {\1c&HFF00&\u1}famille{\r} Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.85,0:00:53.89,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:53.89,0:00:54.16,Default,,0,0,0,,Justement, y a la famille {\1c&HFF00&\u1}Boboun{\r} qui m'attend pour une consultation.
-Dialogue: 0,0:00:54.16,0:00:54.18,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:54.18,0:00:54.26,Default,,0,0,0,,Justement, y a la famille Boboun {\1c&HFF00&\u1}qui{\r} m'attend pour une consultation.
-Dialogue: 0,0:00:54.26,0:00:54.30,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:54.30,0:00:54.52,Default,,0,0,0,,Justement, y a la famille Boboun qui {\1c&HFF00&\u1}m'attend{\r} pour une consultation.
-Dialogue: 0,0:00:54.52,0:00:54.54,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:54.54,0:00:54.64,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend {\1c&HFF00&\u1}pour{\r} une consultation.
-Dialogue: 0,0:00:54.64,0:00:54.68,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:54.68,0:00:54.90,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour {\1c&HFF00&\u1}une{\r} consultation.
-Dialogue: 0,0:00:54.90,0:00:54.94,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une consultation.
-Dialogue: 0,0:00:54.94,0:00:55.34,Default,,0,0,0,,Justement, y a la famille Boboun qui m'attend pour une {\1c&HFF00&\u1}consultation.{\r}
-Dialogue: 0,0:00:55.58,0:00:55.64,Default,,0,0,0,,{\1c&HFF00&\u1}Qui?{\r}
-Dialogue: 0,0:00:56.53,0:00:56.79,Default,,0,0,0,,{\1c&HFF00&\u1}Faisons{\r} pas attendre les bobounes, allez.
-Dialogue: 0,0:00:56.79,0:00:56.81,Default,,0,0,0,,Faisons pas attendre les bobounes, allez.
-Dialogue: 0,0:00:56.81,0:00:56.91,Default,,0,0,0,,Faisons {\1c&HFF00&\u1}pas{\r} attendre les bobounes, allez.
-Dialogue: 0,0:00:56.91,0:00:56.93,Default,,0,0,0,,Faisons pas attendre les bobounes, allez.
-Dialogue: 0,0:00:56.93,0:00:57.15,Default,,0,0,0,,Faisons pas {\1c&HFF00&\u1}attendre{\r} les bobounes, allez.
-Dialogue: 0,0:00:57.15,0:00:57.19,Default,,0,0,0,,Faisons pas attendre les bobounes, allez.
-Dialogue: 0,0:00:57.19,0:00:57.25,Default,,0,0,0,,Faisons pas attendre {\1c&HFF00&\u1}les{\r} bobounes, allez.
-Dialogue: 0,0:00:57.25,0:00:57.27,Default,,0,0,0,,Faisons pas attendre les bobounes, allez.
-Dialogue: 0,0:00:57.27,0:00:57.59,Default,,0,0,0,,Faisons pas attendre les {\1c&HFF00&\u1}bobounes,{\r} allez.
-Dialogue: 0,0:00:57.59,0:00:57.61,Default,,0,0,0,,Faisons pas attendre les bobounes, allez.
-Dialogue: 0,0:00:57.61,0:00:57.75,Default,,0,0,0,,Faisons pas attendre les bobounes, {\1c&HFF00&\u1}allez.{\r}
\ No newline at end of file
diff --git a/examples/whisperx/sample_fr_01.wav.srt b/examples/whisperx/sample_fr_01.wav.srt
deleted file mode 100644
index 6a345c1..0000000
--- a/examples/whisperx/sample_fr_01.wav.srt
+++ /dev/null
@@ -1,120 +0,0 @@
-1
-00:00:00,765 --> 00:00:01,309
-Lâchez, c'est bon.
-
-2
-00:00:01,309 --> 00:00:01,891
-Ça va?
-
-3
-00:00:01,911 --> 00:00:01,971
-Oui.
-
-4
-00:00:01,971 --> 00:00:03,495
-Merci beaucoup.
-
-5
-00:00:04,356 --> 00:00:05,037
-Chèque ou espèce?
-
-6
-00:00:06,544 --> 00:00:08,393
-J'ai laissé un chèque sur la commode, il est signé.
-
-7
-00:00:08,393 --> 00:00:09,255
-Vous le remplirez.
-
-8
-00:00:09,275 --> 00:00:09,335
-OK.
-
-9
-00:00:09,355 --> 00:00:09,395
-Oh!
-
-10
-00:00:12,410 --> 00:00:12,590
-Ouh là!
-
-11
-00:00:12,590 --> 00:00:12,731
-Venez.
-
-12
-00:00:14,454 --> 00:00:14,635
-Merci.
-
-13
-00:00:14,655 --> 00:00:14,755
-Ah!
-
-14
-00:00:15,640 --> 00:00:16,626
-C'est qui?
-
-15
-00:00:20,000 --> 00:00:24,234
-C'est pas vrai, qu'est-ce qu'il fout ici, ce con?
-
-16
-00:00:24,515 --> 00:00:26,177
-Excusez-moi, mais je crois que j'ai oublié mon sac chez vous.
-
-17
-00:00:26,177 --> 00:00:26,359
-Ça va?
-
-18
-00:00:31,040 --> 00:00:31,241
-Attendez.
-
-19
-00:00:36,813 --> 00:00:38,278
-Tout à l'heure, là, c'était...
-
-20
-00:00:38,278 --> 00:00:38,359
-Vous?
-
-21
-00:00:39,123 --> 00:00:39,425
-Vous? Pas...
-
-22
-00:00:39,425 --> 00:00:39,706
-Pas lui? Vous?
-
-23
-00:00:44,194 --> 00:00:45,980
-Vous avez tout à fait raison, M. Xenakis.
-
-24
-00:00:46,745 --> 00:00:49,000
-Malek est à l'interne brillant, qui apprend le métier avec moi.
-
-25
-00:00:49,020 --> 00:00:49,061
-Ah!
-
-26
-00:00:49,061 --> 00:00:49,303
-C'est vrai.
-
-27
-00:00:49,303 --> 00:00:49,443
-Bien.
-
-28
-00:00:52,932 --> 00:00:55,338
-Justement, y a la famille Boboun qui m'attend pour une consultation.
-
-29
-00:00:55,581 --> 00:00:55,642
-Qui?
-
-30
-00:00:56,527 --> 00:00:57,753
-Faisons pas attendre les bobounes, allez.
-
diff --git a/examples/whisperx/sample_it_01.wav.ass b/examples/whisperx/sample_it_01.wav.ass
deleted file mode 100644
index 85ae574..0000000
--- a/examples/whisperx/sample_it_01.wav.ass
+++ /dev/null
@@ -1,177 +0,0 @@
-[Script Info]
-ScriptType: v4.00+
-PlayResX: 384
-PlayResY: 288
-ScaledBorderAndShadow: yes
-
-[V4+ Styles]
-Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
-Style: Default,Arial,24,&Hffffff,&Hffffff,&H0,&H0,0,0,0,0,100,100,0,0,1,1,0,2,10,10,10,0
-
-[Events]
-Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
-
-Dialogue: 0,0:00:1.20,0:00:1.62,Default,,0,0,0,,{\1c&HFF00&\u1}Signore,{\r} è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:1.62,0:00:1.64,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:1.64,0:00:1.66,Default,,0,0,0,,Signore, {\1c&HFF00&\u1}è{\r} un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:1.66,0:00:1.72,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:1.72,0:00:2.12,Default,,0,0,0,,Signore, è {\1c&HFF00&\u1}un{\r} piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:2.12,0:00:2.18,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:2.18,0:00:2.72,Default,,0,0,0,,Signore, è un {\1c&HFF00&\u1}piacere,{\r} gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:2.72,0:00:3.33,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:3.33,0:00:3.45,Default,,0,0,0,,Signore, è un piacere, {\1c&HFF00&\u1}gli{\r} amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:3.45,0:00:3.49,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:3.49,0:00:3.79,Default,,0,0,0,,Signore, è un piacere, gli {\1c&HFF00&\u1}amici{\r} della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:3.79,0:00:3.83,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:3.83,0:00:4.01,Default,,0,0,0,,Signore, è un piacere, gli amici {\1c&HFF00&\u1}della{\r} vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.01,0:00:4.05,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.05,0:00:4.35,Default,,0,0,0,,Signore, è un piacere, gli amici della {\1c&HFF00&\u1}vedetta{\r} ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.35,0:00:4.39,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.39,0:00:4.79,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta {\1c&HFF00&\u1}ammirata{\r} da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.79,0:00:4.85,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.85,0:00:4.95,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata {\1c&HFF00&\u1}da{\r} tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.95,0:00:4.97,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:4.97,0:00:5.15,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da {\1c&HFF00&\u1}tutti{\r} noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:5.15,0:00:5.21,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:5.21,0:00:5.33,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti {\1c&HFF00&\u1}noi,{\r} questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:5.33,0:00:5.41,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
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-Dialogue: 0,0:00:5.79,0:00:6.07,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa {\1c&HFF00&\u1}gemma{\r} propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
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-Dialogue: 0,0:00:6.51,0:00:6.57,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:6.57,0:00:6.77,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria {\1c&HFF00&\u1}della{\r} nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:6.77,0:00:6.81,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:6.81,0:00:6.99,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della {\1c&HFF00&\u1}nostra{\r} cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:6.99,0:00:7.07,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:7.07,0:00:7.35,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra {\1c&HFF00&\u1}cultura,{\r} saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:7.35,0:00:7.41,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:7.41,0:00:7.73,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, {\1c&HFF00&\u1}saranno{\r} naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:7.73,0:00:7.87,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:7.87,0:00:8.47,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno {\1c&HFF00&\u1}naturalmente{\r} accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:8.47,0:00:8.55,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:8.55,0:00:8.85,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente {\1c&HFF00&\u1}accolti{\r} sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:8.85,0:00:8.91,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:8.91,0:00:9.07,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti {\1c&HFF00&\u1}sotto{\r} la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.07,0:00:9.13,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.13,0:00:9.19,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto {\1c&HFF00&\u1}la{\r} mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.19,0:00:9.23,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.23,0:00:9.33,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la {\1c&HFF00&\u1}mia{\r} protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.33,0:00:9.37,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.37,0:00:9.82,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia {\1c&HFF00&\u1}protezione{\r} per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.82,0:00:9.88,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:9.88,0:00:9.96,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione {\1c&HFF00&\u1}per{\r} la durata del loro soggiorno.
-Dialogue: 0,0:00:9.96,0:00:10.02,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:10.02,0:00:10.08,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per {\1c&HFF00&\u1}la{\r} durata del loro soggiorno.
-Dialogue: 0,0:00:10.08,0:00:10.12,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:10.12,0:00:10.44,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la {\1c&HFF00&\u1}durata{\r} del loro soggiorno.
-Dialogue: 0,0:00:10.44,0:00:10.50,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:10.50,0:00:10.60,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata {\1c&HFF00&\u1}del{\r} loro soggiorno.
-Dialogue: 0,0:00:10.60,0:00:10.62,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:10.62,0:00:10.78,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del {\1c&HFF00&\u1}loro{\r} soggiorno.
-Dialogue: 0,0:00:10.78,0:00:10.86,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-Dialogue: 0,0:00:10.86,0:00:11.30,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro {\1c&HFF00&\u1}soggiorno.{\r}
-Dialogue: 0,0:00:13.00,0:00:13.12,Default,,0,0,0,,{\1c&HFF00&\u1}Grazie.{\r}
-Dialogue: 0,0:00:15.60,0:00:17.89,Default,,0,0,0,,{\1c&HFF00&\u1}Gorlami?{\r} Lo pronuncio correttamente?
-Dialogue: 0,0:00:17.89,0:00:18.75,Default,,0,0,0,,Gorlami? Lo pronuncio correttamente?
-Dialogue: 0,0:00:18.75,0:00:18.83,Default,,0,0,0,,Gorlami? {\1c&HFF00&\u1}Lo{\r} pronuncio correttamente?
-Dialogue: 0,0:00:18.83,0:00:18.87,Default,,0,0,0,,Gorlami? Lo pronuncio correttamente?
-Dialogue: 0,0:00:18.87,0:00:19.31,Default,,0,0,0,,Gorlami? Lo {\1c&HFF00&\u1}pronuncio{\r} correttamente?
-Dialogue: 0,0:00:19.31,0:00:19.37,Default,,0,0,0,,Gorlami? Lo pronuncio correttamente?
-Dialogue: 0,0:00:19.37,0:00:19.87,Default,,0,0,0,,Gorlami? Lo pronuncio {\1c&HFF00&\u1}correttamente?{\r}
-Dialogue: 0,0:00:21.58,0:00:21.74,Default,,0,0,0,,{\1c&HFF00&\u1}Sì,{\r} corretto.
-Dialogue: 0,0:00:21.74,0:00:22.34,Default,,0,0,0,,Sì, corretto.
-Dialogue: 0,0:00:22.34,0:00:22.72,Default,,0,0,0,,Sì, {\1c&HFF00&\u1}corretto.{\r}
-Dialogue: 0,0:00:23.54,0:00:24.82,Default,,0,0,0,,{\1c&HFF00&\u1}Gorlami?{\r} Per cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:24.82,0:00:25.45,Default,,0,0,0,,Gorlami? Per cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:25.45,0:00:25.53,Default,,0,0,0,,Gorlami? {\1c&HFF00&\u1}Per{\r} cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:25.53,0:00:25.61,Default,,0,0,0,,Gorlami? Per cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:25.61,0:00:26.17,Default,,0,0,0,,Gorlami? Per {\1c&HFF00&\u1}cortesia,{\r} me lo ripeto ancora.
-Dialogue: 0,0:00:26.17,0:00:26.39,Default,,0,0,0,,Gorlami? Per cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:26.39,0:00:26.45,Default,,0,0,0,,Gorlami? Per cortesia, {\1c&HFF00&\u1}me{\r} lo ripeto ancora.
-Dialogue: 0,0:00:26.45,0:00:26.49,Default,,0,0,0,,Gorlami? Per cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:26.49,0:00:26.55,Default,,0,0,0,,Gorlami? Per cortesia, me {\1c&HFF00&\u1}lo{\r} ripeto ancora.
-Dialogue: 0,0:00:26.55,0:00:26.61,Default,,0,0,0,,Gorlami? Per cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:26.61,0:00:27.05,Default,,0,0,0,,Gorlami? Per cortesia, me lo {\1c&HFF00&\u1}ripeto{\r} ancora.
-Dialogue: 0,0:00:27.05,0:00:27.11,Default,,0,0,0,,Gorlami? Per cortesia, me lo ripeto ancora.
-Dialogue: 0,0:00:27.11,0:00:27.49,Default,,0,0,0,,Gorlami? Per cortesia, me lo ripeto {\1c&HFF00&\u1}ancora.{\r}
-Dialogue: 0,0:00:27.55,0:00:28.79,Default,,0,0,0,,{\1c&HFF00&\u1}ancora{\r} gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:28.79,0:00:28.83,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:28.83,0:00:30.96,Default,,0,0,0,,ancora {\1c&HFF00&\u1}gourlami{\r} scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:30.96,0:00:31.02,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:31.02,0:00:31.36,Default,,0,0,0,,ancora gourlami {\1c&HFF00&\u1}scusi{\r} con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:31.36,0:00:31.46,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:31.46,0:00:31.66,Default,,0,0,0,,ancora gourlami scusi {\1c&HFF00&\u1}con{\r} me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:31.66,0:00:31.72,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:31.72,0:00:31.88,Default,,0,0,0,,ancora gourlami scusi con {\1c&HFF00&\u1}me{\r} gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:31.88,0:00:33.46,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:33.46,0:00:34.12,Default,,0,0,0,,ancora gourlami scusi con me {\1c&HFF00&\u1}gourlami{\r} ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:34.12,0:00:34.84,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:34.84,0:00:35.20,Default,,0,0,0,,ancora gourlami scusi con me gourlami {\1c&HFF00&\u1}ancora{\r} una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:35.20,0:00:35.32,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:35.32,0:00:35.44,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora {\1c&HFF00&\u1}una{\r} volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:35.44,0:00:35.48,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:35.48,0:00:35.82,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una {\1c&HFF00&\u1}volta{\r} gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:35.82,0:00:39.16,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:39.16,0:00:39.72,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta {\1c&HFF00&\u1}gourlami{\r} e come si chiama lei antonio
-Dialogue: 0,0:00:39.72,0:00:40.96,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:40.96,0:00:41.14,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami {\1c&HFF00&\u1}e{\r} come si chiama lei antonio
-Dialogue: 0,0:00:41.14,0:00:41.20,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:41.20,0:00:41.34,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e {\1c&HFF00&\u1}come{\r} si chiama lei antonio
-Dialogue: 0,0:00:41.34,0:00:41.38,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:41.38,0:00:41.46,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come {\1c&HFF00&\u1}si{\r} chiama lei antonio
-Dialogue: 0,0:00:41.46,0:00:41.50,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:41.50,0:00:41.70,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si {\1c&HFF00&\u1}chiama{\r} lei antonio
-Dialogue: 0,0:00:41.70,0:00:41.80,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:41.80,0:00:42.06,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama {\1c&HFF00&\u1}lei{\r} antonio
-Dialogue: 0,0:00:42.06,0:00:43.44,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-Dialogue: 0,0:00:43.44,0:00:43.98,Default,,0,0,0,,ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei {\1c&HFF00&\u1}antonio{\r}
-Dialogue: 0,0:00:44.36,0:00:45.14,Default,,0,0,0,,{\1c&HFF00&\u1}margarete{\r} ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:45.14,0:00:46.56,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:46.56,0:00:46.85,Default,,0,0,0,,margarete {\1c&HFF00&\u1}ancora{\r} margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:46.85,0:00:47.85,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:47.85,0:00:49.15,Default,,0,0,0,,margarete ancora {\1c&HFF00&\u1}margarete{\r} un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:49.15,0:00:49.43,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:49.43,0:00:49.85,Default,,0,0,0,,margarete ancora margarete {\1c&HFF00&\u1}un'altra{\r} volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:49.85,0:00:49.91,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:49.91,0:00:50.13,Default,,0,0,0,,margarete ancora margarete un'altra {\1c&HFF00&\u1}volta{\r} ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:50.13,0:00:50.19,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:50.19,0:00:50.37,Default,,0,0,0,,margarete ancora margarete un'altra volta {\1c&HFF00&\u1}ma{\r} adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:50.37,0:00:50.43,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:50.43,0:00:50.73,Default,,0,0,0,,margarete ancora margarete un'altra volta ma {\1c&HFF00&\u1}adesso{\r} vorrei proprio sentire la musica
-Dialogue: 0,0:00:50.73,0:00:50.77,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:50.77,0:00:51.01,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso {\1c&HFF00&\u1}vorrei{\r} proprio sentire la musica
-Dialogue: 0,0:00:51.01,0:00:51.05,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:51.05,0:00:51.29,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei {\1c&HFF00&\u1}proprio{\r} sentire la musica
-Dialogue: 0,0:00:51.29,0:00:51.43,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:51.43,0:00:51.71,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio {\1c&HFF00&\u1}sentire{\r} la musica
-Dialogue: 0,0:00:51.71,0:00:51.79,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:51.79,0:00:51.88,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire {\1c&HFF00&\u1}la{\r} musica
-Dialogue: 0,0:00:51.88,0:00:51.92,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-Dialogue: 0,0:00:51.92,0:00:52.36,Default,,0,0,0,,margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la {\1c&HFF00&\u1}musica{\r}
-Dialogue: 0,0:00:52.36,0:00:52.40,Default,,0,0,0,,{\1c&HFF00&\u1}le{\r} parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:52.40,0:00:52.52,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:52.52,0:00:54.32,Default,,0,0,0,,le {\1c&HFF00&\u1}parole{\r} margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:54.32,0:00:54.40,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:54.40,0:00:56.40,Default,,0,0,0,,le parole {\1c&HFF00&\u1}margheriti{\r} margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:56.40,0:00:56.96,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:56.96,0:00:57.92,Default,,0,0,0,,le parole margheriti {\1c&HFF00&\u1}margheriti{\r} e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:57.92,0:00:58.53,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:58.53,0:00:58.59,Default,,0,0,0,,le parole margheriti margheriti {\1c&HFF00&\u1}e{\r} lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:58.59,0:00:58.65,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:58.65,0:00:58.97,Default,,0,0,0,,le parole margheriti margheriti e {\1c&HFF00&\u1}lei{\r} dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:58.97,0:00:59.59,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:59.59,0:00:59.91,Default,,0,0,0,,le parole margheriti margheriti e lei {\1c&HFF00&\u1}dominic{\r} decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:59.91,0:00:59.95,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:00:59.95,0:01:0.35,Default,,0,0,0,,le parole margheriti margheriti e lei dominic {\1c&HFF00&\u1}decoco{\r} come dominic decoco bravo bravo
-Dialogue: 0,0:01:0.35,0:01:0.55,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:01:0.55,0:01:0.73,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco {\1c&HFF00&\u1}come{\r} dominic decoco bravo bravo
-Dialogue: 0,0:01:0.73,0:01:1.25,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:01:1.25,0:01:1.57,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come {\1c&HFF00&\u1}dominic{\r} decoco bravo bravo
-Dialogue: 0,0:01:1.57,0:01:1.61,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:01:1.61,0:01:2.01,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic {\1c&HFF00&\u1}decoco{\r} bravo bravo
-Dialogue: 0,0:01:2.01,0:01:2.17,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:01:2.17,0:01:2.45,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco {\1c&HFF00&\u1}bravo{\r} bravo
-Dialogue: 0,0:01:2.45,0:01:2.91,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-Dialogue: 0,0:01:2.91,0:01:3.29,Default,,0,0,0,,le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo {\1c&HFF00&\u1}bravo{\r}
\ No newline at end of file
diff --git a/examples/whisperx/sample_it_01.wav.srt b/examples/whisperx/sample_it_01.wav.srt
deleted file mode 100644
index d64ce1c..0000000
--- a/examples/whisperx/sample_it_01.wav.srt
+++ /dev/null
@@ -1,32 +0,0 @@
-1
-00:00:01,202 --> 00:00:11,297
-Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
-
-2
-00:00:13,000 --> 00:00:13,120
-Grazie.
-
-3
-00:00:15,602 --> 00:00:19,874
-Gorlami? Lo pronuncio correttamente?
-
-4
-00:00:21,576 --> 00:00:22,717
-Sì, corretto.
-
-5
-00:00:23,540 --> 00:00:27,495
-Gorlami? Per cortesia, me lo ripeto ancora.
-
-6
-00:00:27,555 --> 00:00:43,979
-ancora gourlami scusi con me gourlami ancora una volta gourlami e come si chiama lei antonio
-
-7
-00:00:44,360 --> 00:00:52,356
-margarete ancora margarete un'altra volta ma adesso vorrei proprio sentire la musica
-
-8
-00:00:52,356 --> 00:01:03,292
-le parole margheriti margheriti e lei dominic decoco come dominic decoco bravo bravo
-
diff --git a/requirements.txt b/requirements.txt
index 0142084..3b7ae35 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,10 @@
 numpy
-torch
-torchaudio
+pandas
+torch >=1.9
+torchaudio >=0.10,<1.0
 tqdm
 more-itertools
 transformers>=4.19.0
 ffmpeg-python==0.2.0
 pyannote.audio
-soundfile
+whisper
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 497d0b8..4aa1f3f 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ from setuptools import setup, find_packages
 setup(
     name="whisperx",
     py_modules=["whisperx"],
-    version="1.0",
+    version="2.0",
     description="Time-Accurate Automatic Speech Recognition using Whisper.",
     readme="README.md",
     python_requires=">=3.7",
diff --git a/tests/jfk.flac b/tests/jfk.flac
deleted file mode 100644
index e44b7c13897eae7f78beb220c61fe77429a3961d..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/tests/test_audio.py b/tests/test_audio.py
deleted file mode 100644
index ad6a6da..0000000
--- a/tests/test_audio.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import os.path
-
-import numpy as np
-
-from whisper.audio import load_audio, log_mel_spectrogram, SAMPLE_RATE
-
-
-def test_audio():
-    audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
-    audio = load_audio(audio_path)
-    assert audio.ndim == 1
-    assert SAMPLE_RATE * 10 < audio.shape[0] < SAMPLE_RATE * 12
-    assert 0 < audio.std() < 1
-
-    mel_from_audio = log_mel_spectrogram(audio)
-    mel_from_file = log_mel_spectrogram(audio_path)
-
-    assert np.allclose(mel_from_audio, mel_from_file)
-    assert mel_from_audio.max() - mel_from_audio.min() <= 2.0
diff --git a/tests/test_normalizer.py b/tests/test_normalizer.py
deleted file mode 100644
index a6c16cb..0000000
--- a/tests/test_normalizer.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import pytest
-
-from whisper.normalizers import EnglishTextNormalizer
-from whisper.normalizers.english import EnglishNumberNormalizer, EnglishSpellingNormalizer
-
-
-@pytest.mark.parametrize("std", [EnglishNumberNormalizer(), EnglishTextNormalizer()])
-def test_number_normalizer(std):
-    assert std("two") == "2"
-    assert std("thirty one") == "31"
-    assert std("five twenty four") == "524"
-    assert std("nineteen ninety nine") == "1999"
-    assert std("twenty nineteen") == "2019"
-
-    assert std("two point five million") == "2500000"
-    assert std("four point two billions") == "4200000000s"
-    assert std("200 thousand") == "200000"
-    assert std("200 thousand dollars") == "$200000"
-    assert std("$20 million") == "$20000000"
-    assert std("€52.4 million") == "€52400000"
-    assert std("£77 thousands") == "£77000s"
-
-    assert std("two double o eight") == "2008"
-
-    assert std("three thousand twenty nine") == "3029"
-    assert std("forty three thousand two hundred sixty") == "43260"
-    assert std("forty three thousand two hundred and sixty") == "43260"
-
-    assert std("nineteen fifties") == "1950s"
-    assert std("thirty first") == "31st"
-    assert std("thirty three thousand and three hundred and thirty third") == "33333rd"
-
-    assert std("three billion") == "3000000000"
-    assert std("millions") == "1000000s"
-
-    assert std("july third twenty twenty") == "july 3rd 2020"
-    assert std("august twenty sixth twenty twenty one") == "august 26th 2021"
-    assert std("3 14") == "3 14"
-    assert std("3.14") == "3.14"
-    assert std("3 point 2") == "3.2"
-    assert std("3 point 14") == "3.14"
-    assert std("fourteen point 4") == "14.4"
-    assert std("two point two five dollars") == "$2.25"
-    assert std("two hundred million dollars") == "$200000000"
-    assert std("$20.1 million") == "$20100000"
-
-    assert std("ninety percent") == "90%"
-    assert std("seventy six per cent") == "76%"
-
-    assert std("double oh seven") == "007"
-    assert std("double zero seven") == "007"
-    assert std("nine one one") == "911"
-    assert std("nine double one") == "911"
-    assert std("one triple oh one") == "10001"
-
-    assert std("two thousandth") == "2000th"
-    assert std("thirty two thousandth") == "32000th"
-
-    assert std("minus 500") == "-500"
-    assert std("positive twenty thousand") == "+20000"
-
-    assert std("two dollars and seventy cents") == "$2.70"
-    assert std("3 cents") == "¢3"
-    assert std("$0.36") == "¢36"
-    assert std("three euros and sixty five cents") == "€3.65"
-
-    assert std("three and a half million") == "3500000"
-    assert std("forty eight and a half dollars") == "$48.5"
-    assert std("b747") == "b 747"
-    assert std("10 th") == "10th"
-    assert std("10th") == "10th"
-
-
-def test_spelling_normalizer():
-    std = EnglishSpellingNormalizer()
-
-    assert std("mobilisation") == "mobilization"
-    assert std("cancelation") == "cancellation"
-
-
-def test_text_normalizer():
-    std = EnglishTextNormalizer()
-    assert std("Let's") == "let us"
-    assert std("he's like") == "he is like"
-    assert std("she's been like") == "she has been like"
-    assert std("10km") == "10 km"
-    assert std("RC232") == "rc 232"
-
-    assert (
-        std("Mr. Park visited Assoc. Prof. Kim Jr.")
-        == "mister park visited associate professor kim junior"
-    )
diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py
deleted file mode 100644
index f4cd80b..0000000
--- a/tests/test_tokenizer.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from whisper.tokenizer import get_tokenizer
-
-
-def test_tokenizer():
-    gpt2_tokenizer = get_tokenizer(multilingual=False)
-    multilingual_tokenizer = get_tokenizer(multilingual=True)
-
-    text = "다람쥐 헌 쳇바퀴에 타고파"
-    gpt2_tokens = gpt2_tokenizer.encode(text)
-    multilingual_tokens = multilingual_tokenizer.encode(text)
-
-    assert gpt2_tokenizer.decode(gpt2_tokens) == text
-    assert multilingual_tokenizer.decode(multilingual_tokens) == text
-    assert len(gpt2_tokens) > len(multilingual_tokens)
diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py
deleted file mode 100644
index 836cf40..0000000
--- a/tests/test_transcribe.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-
-import pytest
-
-import whisper
-
-
-@pytest.mark.parametrize('model_name', whisper.available_models())
-def test_transcribe(model_name: str):
-    model = whisper.load_model(model_name).cuda()
-    audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
-
-    language = "en" if model_name.endswith(".en") else None
-    result = model.transcribe(audio_path, language=language, temperature=0.0)
-    assert result["language"] == "en"
-
-    transcription = result["text"].lower()
-    assert "my fellow americans" in transcription
-    assert "your country" in transcription
-    assert "do for you" in transcription
diff --git a/whisperx/__init__.py b/whisperx/__init__.py
index 1d361e4..985ed32 100644
--- a/whisperx/__init__.py
+++ b/whisperx/__init__.py
@@ -1,115 +1,3 @@
-import hashlib
-import io
-import os
-import urllib
-import warnings
-from typing import List, Optional, Union
-
-import torch
-from tqdm import tqdm
-
-from .audio import load_audio, log_mel_spectrogram, pad_or_trim
-from .decoding import DecodingOptions, DecodingResult, decode, detect_language
-from .model import Whisper, ModelDimensions
-from .transcribe import transcribe, transcribe_with_vad, transcribe_with_vad_parallel
+from .transcribe import transcribe, transcribe_with_vad
 from .alignment import load_align_model, align
-_MODELS = {
-    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
-    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
-    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
-    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
-    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
-    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
-    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
-    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
-    "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
-    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
-    "large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
-}
-
-
-def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
-    os.makedirs(root, exist_ok=True)
-
-    expected_sha256 = url.split("/")[-2]
-    download_target = os.path.join(root, os.path.basename(url))
-
-    if os.path.exists(download_target) and not os.path.isfile(download_target):
-        raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-    if os.path.isfile(download_target):
-        with open(download_target, "rb") as f:
-            model_bytes = f.read()
-        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
-            return model_bytes if in_memory else download_target
-        else:
-            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
-
-    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
-        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
-            while True:
-                buffer = source.read(8192)
-                if not buffer:
-                    break
-
-                output.write(buffer)
-                loop.update(len(buffer))
-
-    model_bytes = open(download_target, "rb").read()
-    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
-        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.")
-
-    return model_bytes if in_memory else download_target
-
-
-def available_models() -> List[str]:
-    """Returns the names of available models"""
-    return list(_MODELS.keys())
-
-
-def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper:
-    """
-    Load a Whisper ASR model
-
-    Parameters
-    ----------
-    name : str
-        one of the official model names listed by `whisper.available_models()`, or
-        path to a model checkpoint containing the model dimensions and the model state_dict.
-    device : Union[str, torch.device]
-        the PyTorch device to put the model into
-    download_root: str
-        path to download the model files; by default, it uses "~/.cache/whisper"
-    in_memory: bool
-        whether to preload the model weights into host memory
-
-    Returns
-    -------
-    model : Whisper
-        The Whisper ASR model instance
-    """
-
-    if device is None:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if download_root is None:
-        download_root = os.getenv(
-            "XDG_CACHE_HOME", 
-            os.path.join(os.path.expanduser("~"), ".cache", "whisper")
-        )
-
-    if name in _MODELS:
-        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
-    elif os.path.isfile(name):
-        checkpoint_file = open(name, "rb").read() if in_memory else name
-    else:
-        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
-
-    with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp:
-        checkpoint = torch.load(fp, map_location=device)
-    del checkpoint_file
-
-    dims = ModelDimensions(**checkpoint["dims"])
-    model = Whisper(dims)
-    model.load_state_dict(checkpoint["model_state_dict"])
-
-    return model.to(device)
+from .vad import load_vad_model
\ No newline at end of file
diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index 4188da5..a906cf7 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -9,7 +9,7 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import torchaudio
 import torch
 from dataclasses import dataclass
-from .audio import SAMPLE_RATE, load_audio
+from whisper.audio import SAMPLE_RATE, load_audio
 from .utils import interpolate_nans
 
 
diff --git a/whisperx/asr.py b/whisperx/asr.py
new file mode 100644
index 0000000..661a28e
--- /dev/null
+++ b/whisperx/asr.py
@@ -0,0 +1,428 @@
+import warnings
+from typing import TYPE_CHECKING, Optional, Tuple, Union
+import tempfile
+import numpy as np
+import torch
+import tqdm
+
+from whisper.audio import (
+    FRAMES_PER_SECOND,
+    HOP_LENGTH,
+    N_FRAMES,
+    N_SAMPLES,
+    SAMPLE_RATE,
+    CHUNK_LENGTH,
+    log_mel_spectrogram,
+    pad_or_trim,
+    load_audio
+)
+from whisper.decoding import DecodingOptions, DecodingResult
+from whisper.timing import add_word_timestamps
+from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
+from whisper.utils import (
+    exact_div,
+    format_timestamp,
+    make_safe,
+)
+
+if TYPE_CHECKING:
+    from whisper.model import Whisper
+
+from .vad import merge_chunks
+
+def transcribe(
+    model: "Whisper",
+    audio: Union[str, np.ndarray, torch.Tensor] = None,
+    mel: np.ndarray = None,
+    verbose: Optional[bool] = None,
+    temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+    compression_ratio_threshold: Optional[float] = 2.4,
+    logprob_threshold: Optional[float] = -1.0,
+    no_speech_threshold: Optional[float] = 0.6,
+    condition_on_previous_text: bool = True,
+    initial_prompt: Optional[str] = None,
+    word_timestamps: bool = False,
+    prepend_punctuations: str = "\"'“¿([{-",
+    append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
+    **decode_options,
+):
+    """
+    Transcribe an audio file using Whisper.
+    We redefine the Whisper transcribe function to allow mel input (for sequential slicing of audio)
+
+    Parameters
+    ----------
+    model: Whisper
+        The Whisper model instance
+
+    audio: Union[str, np.ndarray, torch.Tensor]
+        The path to the audio file to open, or the audio waveform
+
+    mel: np.ndarray
+        Mel spectrogram of audio segment.
+        
+    verbose: bool
+        Whether to display the text being decoded to the console. If True, displays all the details,
+        If False, displays minimal details. If None, does not display anything
+
+    temperature: Union[float, Tuple[float, ...]]
+        Temperature for sampling. It can be a tuple of temperatures, which will be successively used
+        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
+
+    compression_ratio_threshold: float
+        If the gzip compression ratio is above this value, treat as failed
+
+    logprob_threshold: float
+        If the average log probability over sampled tokens is below this value, treat as failed
+
+    no_speech_threshold: float
+        If the no_speech probability is higher than this value AND the average log probability
+        over sampled tokens is below `logprob_threshold`, consider the segment as silent
+
+    condition_on_previous_text: bool
+        if True, the previous output of the model is provided as a prompt for the next window;
+        disabling may make the text inconsistent across windows, but the model becomes less prone to
+        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
+
+    word_timestamps: bool
+        Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
+        and include the timestamps for each word in each segment.
+
+    prepend_punctuations: str
+        If word_timestamps is True, merge these punctuation symbols with the next word
+
+    append_punctuations: str
+        If word_timestamps is True, merge these punctuation symbols with the previous word
+
+    initial_prompt: Optional[str]
+        Optional text to provide as a prompt for the first window. This can be used to provide, or
+        "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
+        to make it more likely to predict those word correctly.
+
+    decode_options: dict
+        Keyword arguments to construct `DecodingOptions` instances
+
+    Returns
+    -------
+    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
+    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
+    """
+    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
+    if model.device == torch.device("cpu"):
+        if torch.cuda.is_available():
+            warnings.warn("Performing inference on CPU when CUDA is available")
+        if dtype == torch.float16:
+            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
+            dtype = torch.float32
+
+    if dtype == torch.float32:
+        decode_options["fp16"] = False
+
+    # Pad 30-seconds of silence to the input audio, for slicing
+    if mel is None:
+        if audio is None:
+            raise ValueError("Transcribe needs either audio or mel as input, currently both are none.")
+        mel = log_mel_spectrogram(audio, padding=N_SAMPLES)
+    content_frames = mel.shape[-1] - N_FRAMES
+
+    if decode_options.get("language", None) is None:
+        if not model.is_multilingual:
+            decode_options["language"] = "en"
+        else:
+            if verbose:
+                print(
+                    "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
+                )
+            mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
+            _, probs = model.detect_language(mel_segment)
+            decode_options["language"] = max(probs, key=probs.get)
+            if verbose is not None:
+                print(
+                    f"Detected language: {LANGUAGES[decode_options['language']].title()}"
+                )
+
+    language: str = decode_options["language"]
+    task: str = decode_options.get("task", "transcribe")
+    tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
+
+    if word_timestamps and task == "translate":
+        warnings.warn("Word-level timestamps on translations may not be reliable.")
+
+    def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
+        temperatures = (
+            [temperature] if isinstance(temperature, (int, float)) else temperature
+        )
+        decode_result = None
+
+        for t in temperatures:
+            kwargs = {**decode_options}
+            if t > 0:
+                # disable beam_size and patience when t > 0
+                kwargs.pop("beam_size", None)
+                kwargs.pop("patience", None)
+            else:
+                # disable best_of when t == 0
+                kwargs.pop("best_of", None)
+
+            options = DecodingOptions(**kwargs, temperature=t)
+            decode_result = model.decode(segment, options)
+
+            needs_fallback = False
+            if (
+                compression_ratio_threshold is not None
+                and decode_result.compression_ratio > compression_ratio_threshold
+            ):
+                needs_fallback = True  # too repetitive
+            if (
+                logprob_threshold is not None
+                and decode_result.avg_logprob < logprob_threshold
+            ):
+                needs_fallback = True  # average log probability is too low
+
+            if not needs_fallback:
+                break
+
+        return decode_result
+
+    seek = 0
+    input_stride = exact_div(
+        N_FRAMES, model.dims.n_audio_ctx
+    )  # mel frames per output token: 2
+    time_precision = (
+        input_stride * HOP_LENGTH / SAMPLE_RATE
+    )  # time per output token: 0.02 (seconds)
+    all_tokens = []
+    all_segments = []
+    prompt_reset_since = 0
+
+    if initial_prompt is not None:
+        initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
+        all_tokens.extend(initial_prompt_tokens)
+    else:
+        initial_prompt_tokens = []
+
+    def new_segment(
+        *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult
+    ):
+        tokens = tokens.tolist()
+        text_tokens = [token for token in tokens if token < tokenizer.eot]
+        return {
+            "seek": seek,
+            "start": start,
+            "end": end,
+            "text": tokenizer.decode(text_tokens),
+            "tokens": tokens,
+            "temperature": result.temperature,
+            "avg_logprob": result.avg_logprob,
+            "compression_ratio": result.compression_ratio,
+            "no_speech_prob": result.no_speech_prob,
+        }
+
+
+    # show the progress bar when verbose is False (if True, transcribed text will be printed)
+    with tqdm.tqdm(
+        total=content_frames, unit="frames", disable=verbose is not False
+    ) as pbar:
+        while seek < content_frames:
+            time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
+            mel_segment = mel[:, seek : seek + N_FRAMES]
+            segment_size = min(N_FRAMES, content_frames - seek)
+            segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
+            mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
+
+            decode_options["prompt"] = all_tokens[prompt_reset_since:]
+            result: DecodingResult = decode_with_fallback(mel_segment)
+            tokens = torch.tensor(result.tokens)
+            if no_speech_threshold is not None:
+                # no voice activity check
+                should_skip = result.no_speech_prob > no_speech_threshold
+                if (
+                    logprob_threshold is not None
+                    and result.avg_logprob > logprob_threshold
+                ):
+                    # don't skip if the logprob is high enough, despite the no_speech_prob
+                    should_skip = False
+
+                if should_skip:
+                    seek += segment_size  # fast-forward to the next segment boundary
+                    continue
+
+            previous_seek = seek
+            current_segments = []
+
+            timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
+            single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]
+
+            consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
+            consecutive.add_(1)
+            if len(consecutive) > 0:
+                # if the output contains two consecutive timestamp tokens
+                slices = consecutive.tolist()
+                if single_timestamp_ending:
+                    slices.append(len(tokens))
+
+                last_slice = 0
+                for current_slice in slices:
+                    sliced_tokens = tokens[last_slice:current_slice]
+                    start_timestamp_pos = (
+                        sliced_tokens[0].item() - tokenizer.timestamp_begin
+                    )
+                    end_timestamp_pos = (
+                        sliced_tokens[-1].item() - tokenizer.timestamp_begin
+                    )
+                    current_segments.append(
+                        new_segment(
+                            start=time_offset + start_timestamp_pos * time_precision,
+                            end=time_offset + end_timestamp_pos * time_precision,
+                            tokens=sliced_tokens,
+                            result=result,
+                        )
+                    )
+                    last_slice = current_slice
+
+                if single_timestamp_ending:
+                    # single timestamp at the end means no speech after the last timestamp.
+                    seek += segment_size
+                else:
+                    # otherwise, ignore the unfinished segment and seek to the last timestamp
+                    last_timestamp_pos = (
+                        tokens[last_slice - 1].item() - tokenizer.timestamp_begin
+                    )
+                    seek += last_timestamp_pos * input_stride
+            else:
+                duration = segment_duration
+                timestamps = tokens[timestamp_tokens.nonzero().flatten()]
+                if (
+                    len(timestamps) > 0
+                    and timestamps[-1].item() != tokenizer.timestamp_begin
+                ):
+                    # no consecutive timestamps but it has a timestamp; use the last one.
+                    last_timestamp_pos = (
+                        timestamps[-1].item() - tokenizer.timestamp_begin
+                    )
+                    duration = last_timestamp_pos * time_precision
+
+                current_segments.append(
+                    new_segment(
+                        start=time_offset,
+                        end=time_offset + duration,
+                        tokens=tokens,
+                        result=result,
+                    )
+                )
+                seek += segment_size
+
+            if not condition_on_previous_text or result.temperature > 0.5:
+                # do not feed the prompt tokens if a high temperature was used
+                prompt_reset_since = len(all_tokens)
+
+            if word_timestamps:
+                add_word_timestamps(
+                    segments=current_segments,
+                    model=model,
+                    tokenizer=tokenizer,
+                    mel=mel_segment,
+                    num_frames=segment_size,
+                    prepend_punctuations=prepend_punctuations,
+                    append_punctuations=append_punctuations,
+                )
+                word_end_timestamps = [
+                    w["end"] for s in current_segments for w in s["words"]
+                ]
+                if not single_timestamp_ending and len(word_end_timestamps) > 0:
+                    seek_shift = round(
+                        (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND
+                    )
+                    if seek_shift > 0:
+                        seek = previous_seek + seek_shift
+
+            if verbose:
+                for segment in current_segments:
+                    start, end, text = segment["start"], segment["end"], segment["text"]
+                    line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
+                    print(make_safe(line))
+
+            # if a segment is instantaneous or does not contain text, clear it
+            for i, segment in enumerate(current_segments):
+                if segment["start"] == segment["end"] or segment["text"].strip() == "":
+                    segment["text"] = ""
+                    segment["tokens"] = []
+                    segment["words"] = []
+
+            all_segments.extend(
+                [
+                    {"id": i, **segment}
+                    for i, segment in enumerate(
+                        current_segments, start=len(all_segments)
+                    )
+                ]
+            )
+            all_tokens.extend(
+                [token for segment in current_segments for token in segment["tokens"]]
+            )
+
+            # update progress bar
+            pbar.update(min(content_frames, seek) - previous_seek)
+
+
+    return dict(
+        text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
+        segments=all_segments,
+        language=language,
+    )
+
+
+def transcribe_with_vad(
+    model: "Whisper",
+    audio: str,
+    vad_pipeline,
+    mel = None,
+    verbose: Optional[bool] = None,
+    **kwargs
+):
+    """
+    Transcribe per VAD segment
+    """
+
+    vad_segments = vad_pipeline(audio)
+
+    # if not torch.is_tensor(audio):
+        # if isinstance(audio, str):
+    audio = load_audio(audio)
+    audio = torch.from_numpy(audio)
+
+    prev = 0
+    output = {"segments": []}
+
+    # merge segments to approx 30s inputs to make whisper most appropraite
+    vad_segments = merge_chunks(vad_segments, chunk_size=CHUNK_LENGTH)
+
+    print("Performing transcription...")
+    for sdx, seg_t in enumerate(vad_segments):
+        if verbose:
+            print(f"~~ Transcribing VAD chunk: ({format_timestamp(seg_t['start'])} --> {format_timestamp(seg_t['end'])}) ~~")
+        seg_f_start, seg_f_end = int(seg_t["start"] * SAMPLE_RATE), int(seg_t["end"] * SAMPLE_RATE)
+        local_f_start, local_f_end = seg_f_start - prev, seg_f_end - prev
+        audio = audio[local_f_start:] # seek forward
+        seg_audio = audio[:local_f_end-local_f_start] # seek forward
+        prev = seg_f_start
+        local_mel = log_mel_spectrogram(seg_audio, padding=N_SAMPLES)
+        # need to pad
+
+        result = transcribe(model, audio, mel=local_mel, verbose=verbose, **kwargs)
+        seg_t["text"] = result["text"]
+        output["segments"].append(
+            {
+                "start": seg_t["start"],
+                "end": seg_t["end"],
+                "language": result["language"],
+                "text": result["text"],
+                "seg-text": [x["text"] for x in result["segments"]],
+                "seg-start": [x["start"] for x in result["segments"]],
+                "seg-end": [x["end"] for x in result["segments"]],
+                }
+            )
+
+    output["language"] = output["segments"][0]["language"]
+
+    return output
\ No newline at end of file
diff --git a/whisperx/assets/gpt2/merges.txt b/whisperx/assets/gpt2/merges.txt
deleted file mode 100644
index 6636bda..0000000
--- a/whisperx/assets/gpt2/merges.txt
+++ /dev/null
@@ -1,50001 +0,0 @@
-#version: 0.2 - Trained by `huggingface/tokenizers`
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diff --git a/whisperx/assets/gpt2/special_tokens_map.json b/whisperx/assets/gpt2/special_tokens_map.json
deleted file mode 100644
index 817762d..0000000
--- a/whisperx/assets/gpt2/special_tokens_map.json
+++ /dev/null
@@ -1 +0,0 @@
-{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/whisperx/assets/gpt2/tokenizer_config.json b/whisperx/assets/gpt2/tokenizer_config.json
deleted file mode 100644
index c92208c..0000000
--- a/whisperx/assets/gpt2/tokenizer_config.json
+++ /dev/null
@@ -1 +0,0 @@
-{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/whisperx/assets/gpt2/vocab.json b/whisperx/assets/gpt2/vocab.json
deleted file mode 100644
index 84ef7fb..0000000
--- a/whisperx/assets/gpt2/vocab.json
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diff --git a/whisperx/assets/multilingual/added_tokens.json b/whisperx/assets/multilingual/added_tokens.json
deleted file mode 100644
index 5c50999..0000000
--- a/whisperx/assets/multilingual/added_tokens.json
+++ /dev/null
@@ -1 +0,0 @@
-{"<|endoftext|>": 50257}
diff --git a/whisperx/assets/multilingual/merges.txt b/whisperx/assets/multilingual/merges.txt
deleted file mode 100644
index afde9f4..0000000
--- a/whisperx/assets/multilingual/merges.txt
+++ /dev/null
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diff --git a/whisperx/assets/multilingual/special_tokens_map.json b/whisperx/assets/multilingual/special_tokens_map.json
deleted file mode 100644
index 817762d..0000000
--- a/whisperx/assets/multilingual/special_tokens_map.json
+++ /dev/null
@@ -1 +0,0 @@
-{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/whisperx/assets/multilingual/tokenizer_config.json b/whisperx/assets/multilingual/tokenizer_config.json
deleted file mode 100644
index 0235ccf..0000000
--- a/whisperx/assets/multilingual/tokenizer_config.json
+++ /dev/null
@@ -1 +0,0 @@
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7569, "Ñį": 7570, "ĠÐĿÑĥ": 7571, "music": 7572, "ìĽIJ": 7573, "Ġgan": 7574, "ìĭł": 7575, "Ġdefault": 7576, "Ġbem": 7577, "Ùī": 7578, "Ġforced": 7579, "ĠObviously": 7580, "Ġstone": 7581, "Ġtie": 7582, "Ġdrinking": 7583, "Ġserved": 7584, "Cause": 7585, "Ġconference": 7586, "ĠExactly": 7587, "ãĥĪ": 7588, "łľ": 7589, "ìĻĢ": 7590, "ĠRa": 7591, "Ġfake": 7592, "Ġdiff": 7593, "ãģ©": 7594, "Ġchallenging": 7595, "Ġì¤ij": 7596, "Ïĩ": 7597, "ä»Ģ麼": 7598, "Ġintelligence": 7599, "rete": 7600, "Ġstudying": 7601, "Ġappoint": 7602, "Ġtan": 7603, "Ġим": 7604, "Ġcurve": 7605, "ĠTeam": 7606, "ĠAz": 7607, "Ġзд": 7608, "ĠMusic": 7609, "field": 7610, "iration": 7611, "Ġfailed": 7612, "Ġnovel": 7613, "Ġdifferently": 7614, "Ġescape": 7615, "ĠYo": 7616, "ĠOctober": 7617, "ıyor": 7618, "Ġdescribed": 7619, "Ġconvert": 7620, "acement": 7621, "Ġhotel": 7622, "isation": 7623, "Ġsuis": 7624, "ãģij": 7625, "åŃIJ": 7626, "æĢİ": 7627, "Ġwalked": 7628, "200": 7629, "Ġneighborhood": 7630, "isp": 7631, "ĠLos": 7632, 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"hern": 7894, "idos": 7895, "Ġemphas": 7896, "Ġmessages": 7897, "Ġgender": 7898, "ĠWenn": 7899, "Ġво": 7900, "Ġprices": 7901, "olo": 7902, "Ġпон": 7903, "wing": 7904, "ĠFil": 7905, "аем": 7906, "ĠCur": 7907, "Ġfalse": 7908, "Ġfields": 7909, "Ġsé": 7910, "24": 7911, "Ġmac": 7912, "uÅŁ": 7913, "Ġlayers": 7914, "Ġadvoc": 7915, "wan": 7916, "Ġkar": 7917, "ĠÅŀ": 7918, "Ġdecor": 7919, "Ġwalls": 7920, "oe": 7921, "issions": 7922, "Ġresol": 7923, "×¢": 7924, "ĠCarol": 7925, "ĠVide": 7926, "leep": 7927, "ĠYOU": 7928, "Ġflip": 7929, "Ġsurgery": 7930, "Ġchop": 7931, "UR": 7932, ".,": 7933, "Ġagency": 7934, "Ġwanting": 7935, "Ġsolar": 7936, "Ġhoriz": 7937, "ĠAdam": 7938, "Ġstaying": 7939, "olic": 7940, "Ġgrateful": 7941, "Ġremark": 7942, "Ġtechnologies": 7943, "Ġprotein": 7944, "å¿ĥ": 7945, "дел": 7946, "ĠMont": 7947, "Ġshoulder": 7948, "Ġza": 7949, "rey": 7950, "ĠOoh": 7951, "Ġsty": 7952, "icar": 7953, "оÑĤÑĢ": 7954, "Ġroute": 7955, "ĠTurn": 7956, "Ġbom": 7957, "Ġdebate": 7958, "Ġpossibility": 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"igs": 17156, "ologically": 17157, "opp": 17158, "Ġdizer": 17159, "ĠAllen": 17160, "Ġgli": 17161, "Ġacceleration": 17162, "Ġvitamin": 17163, "ãĥŃ": 17164, "vä": 17165, "ĠAccess": 17166, "à®Ļ": 17167, "rás": 17168, "Ġappreciated": 17169, "Ġnah": 17170, "Ġposter": 17171, "Ġtale": 17172, "Ġhighlighted": 17173, "æĸĩ": 17174, "żeli": 17175, "Ġblockchain": 17176, "Ġmicrow": 17177, "Ġcinema": 17178, "ĠChang": 17179, "ĠSearch": 17180, "usters": 17181, "ĠZero": 17182, "ĠDivision": 17183, "ÑĢаÑģ": 17184, "Ġscare": 17185, "Ġjelly": 17186, "ĠAdministration": 17187, "SO": 17188, "Ġlined": 17189, "Ġê°Ħ": 17190, "Ġgeben": 17191, "Ġsoda": 17192, "Ġwinners": 17193, "³¼": 17194, "ÙĴ": 17195, "ĠAmb": 17196, "åķıé¡Į": 17197, "åĶ": 17198, "Ġpeg": 17199, "å·±": 17200, "43": 17201, "Ġraus": 17202, "Ġrewards": 17203, "Ġinclus": 17204, "Ġhighway": 17205, "Ġhah": 17206, "Ġmultiplied": 17207, "Ġsẽ": 17208, "Ġdisciples": 17209, "Ġning": 17210, "Ġdressing": 17211, "Ġattributes": 17212, "ĠMosc": 17213, "ĠGreece": 17214, "Ġsek": 17215, "ĠLearn": 17216, "Ġjus": 17217, "rendre": 17218, "Ġpersonne": 17219, "plete": 17220, "Ġplacing": 17221, "Ġluego": 17222, "illance": 17223, "ĠобÑī": 17224, "Ġprovision": 17225, "Ġlion": 17226, "tra": 17227, "boards": 17228, "Ġbehaviour": 17229, "hey": 17230, "Ġsubscription": 17231, "Ġprotagon": 17232, "ãĥ£": 17233, "Ġvara": 17234, "ĠÅŁu": 17235, "Ġhaha": 17236, "Ġteaspoon": 17237, "æŁ": 17238, "avoir": 17239, "Ġcrypto": 17240, "ĠÑģÑĤаÑĢ": 17241, "ĠStore": 17242, "abs": 17243, "ĠStudents": 17244, "Ġlaund": 17245, "into": 17246, "Ġapproached": 17247, "°ľ": 17248, "ÑĥÑİÑī": 17249, "ĠLabor": 17250, "otes": 17251, "iatric": 17252, "ĠgroÃŁ": 17253, "utive": 17254, "Ġид": 17255, "ĠGib": 17256, "Ġplacement": 17257, "ĠdifÃŃcil": 17258, "Ġfrog": 17259, "ĠвÑģеÑħ": 17260, "ĠJr": 17261, "azed": 17262, "ÑĥÑī": 17263, "Ġê¼": 17264, "frame": 17265, "аеÑĪÑĮ": 17266, "Ġlockdown": 17267, "åij³": 17268, "Ġmedi": 17269, "Ġ×Ķ×ŀ×": 17270, "ений": 17271, "emale": 17272, "ì¢ħ": 17273, "ateral": 17274, "Ġdistant": 17275, "Ġbears": 17276, "Ġjournalist": 17277, "解": 17278, "ĠMarshall": 17279, "ĠIhnen": 17280, "uetooth": 17281, "bag": 17282, "ĠÄijã": 17283, "ĠHighness": 17284, "Ġì°į": 17285, "ика": 17286, "ĠWu": 17287, "ĠFran": 17288, "Ġpeng": 17289, "Ġfon": 17290, "Ġhypothesis": 17291, "ĠÑĢÑĥ": 17292, "Ġly": 17293, "×ļ": 17294, "ìĽĶ": 17295, "ĠRadio": 17296, "à¸ŀ": 17297, "Dav": 17298, "Ġembarrassing": 17299, "ĠìŀĪìĸ´": 17300, "Ġcasting": 17301, "Ġcage": 17302, "ĠPsych": 17303, "ĠìĿ¼ëĭ¨": 17304, "Ġž": 17305, "imb": 17306, "Ġdirectors": 17307, "SH": 17308, "ĠÏĦην": 17309, "á»ģu": 17310, "ĠkonuÅŁ": 17311, "Ġoptional": 17312, "quarters": 17313, "iker": 17314, "ĠSant": 17315, "Ġverses": 17316, "ë¶Ģ": 17317, "Ġolar": 17318, "ĠÏĩ": 17319, "ãĥķ": 17320, "Ġγια": 17321, "ĠImm": 17322, "Ġcontroversial": 17323, "Ġersten": 17324, "Ġrecip": 17325, "ĠChristianity": 17326, "Ġê´ľ": 17327, "ordon": 17328, "×ķש": 17329, "Ġslash": 17330, "ĠPf": 17331, "ÑĥдÑĮ": 17332, "×ķ×Ŀ": 17333, "ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, 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"Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, 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"code": 22332, "Ġhierarchy": 22333, "netes": 22334, "Ġcounterpart": 22335, "Ġtôi": 22336, "Ġted": 22337, "ĠBart": 22338, "ĠëĿ¼": 22339, "ĠGenau": 22340, "Ġincoming": 22341, "ĠABC": 22342, "rique": 22343, "ĠоÑĤп": 22344, "qual": 22345, "Ġincentive": 22346, "Ġihren": 22347, "׳×Ļ": 22348, "loe": 22349, "Ġ1930": 22350, "Ġbarg": 22351, "Ġdiction": 22352, "Ġönce": 22353, "INS": 22354, "Ġreh": 22355, "isiaj": 22356, "mouth": 22357, "Ġscoring": 22358, "lık": 22359, "ĠìķĦ주": 22360, "ORIA": 22361, "ĠEstados": 22362, "Ġcompanion": 22363, "Ġassemble": 22364, "Ġpunished": 22365, "Ġital": 22366, "Ġprevents": 22367, "istes": 22368, "ĠKentucky": 22369, "Ġlocate": 22370, "Ġfasting": 22371, "ãģ¨æĢĿ": 22372, "ĥĢ": 22373, "ĠSeb": 22374, "ĠCrown": 22375, "opia": 22376, "Ġwhip": 22377, "usz": 22378, "ками": 22379, "Ġdatabases": 22380, "åŃĹ": 22381, "Ġprosec": 22382, "Ġ1997": 22383, "ĠìĤ´ì§Ŀ": 22384, "ĠSolar": 22385, "ĠPues": 22386, "ĠZen": 22387, "ollo": 22388, "ĠGuru": 22389, "Ġsqueez": 22390, "ĠÐĹа": 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"ĠëIJ©ëĭĪëĭ¤": 23630, "Ġzeg": 23631, "åĪĩ": 23632, "ĠManhattan": 23633, "Ġsujet": 23634, "uelle": 23635, "stoff": 23636, "Ġdür": 23637, "Ġsubmar": 23638, "eses": 23639, "Ġaquele": 23640, "Ġnou": 23641, "ĠFaith": 23642, "tz": 23643, "ĠÑĤомÑĥ": 23644, "aceut": 23645, "liers": 23646, "Ġbandwidth": 23647, "Æ°á»Ŀ": 23648, "Ġrespective": 23649, "ĠAve": 23650, "Ġspreadshe": 23651, "ĠSent": 23652, "icamente": 23653, "Ġinfra": 23654, "Ġlearners": 23655, "Ġà®ī": 23656, "aiah": 23657, "renal": 23658, "Ġmustard": 23659, "Ġhabt": 23660, "çĥ": 23661, "ĠQué": 23662, "Ġanalyzing": 23663, "æ¯ı": 23664, "Ġsolic": 23665, "Ġ×Ķ×ķ×IJ": 23666, "Ġcausa": 23667, "Ġwelcomed": 23668, "ĠSuccess": 23669, "Ġfacile": 23670, "ĠÐŁÐ¾ÑĤомÑĥ": 23671, "schein": 23672, "Ġfetch": 23673, "Ġstrat": 23674, "ĠÑģÑĤоиÑĤ": 23675, "ìĹIJìĦľëĬĶ": 23676, "ĠÑģпоÑģоб": 23677, "mam": 23678, "ĠserÃŃa": 23679, "naments": 23680, "writer": 23681, "Ġconsulting": 23682, "íĺĢ": 23683, "ĠBerkeley": 23684, "eu": 23685, "asive": 23686, "UU": 23687, "ĠAnalyt": 23688, "Ġsubmission": 23689, "Ġmagnificent": 23690, "enza": 23691, "Ġecon": 23692, "Ġprofiles": 23693, "Ġincar": 23694, "Ab": 23695, "ĠNun": 23696, "Ġhic": 23697, "screaming": 23698, "Ġresilient": 23699, "åĪ©": 23700, "grund": 23701, "Ġconcur": 23702, "Ġbereits": 23703, "LD": 23704, "Ġnurt": 23705, "ìī": 23706, "Ġfeast": 23707, "Ġencuent": 23708, "ĠMichel": 23709, "Ġsuprem": 23710, "\"]": 23711, "Ġfeeds": 23712, "ĠKollegen": 23713, "isser": 23714, "ĠFeng": 23715, "ĠWen": 23716, "mun": 23717, "ĠtenÃŃa": 23718, "ĠWrest": 23719, "Ġìĺ¤ëĬĺìĿĢ": 23720, "Ġstead": 23721, "Ġrestoration": 23722, "Ġdonated": 23723, "Ġdels": 23724, "Ġcensus": 23725, "Ġdesperately": 23726, "worthy": 23727, "HE": 23728, "ĠSpa": 23729, "ĠBryan": 23730, "Ġhj": 23731, "ĠRaw": 23732, "ìķĦë": 23733, "ĠCamera": 23734, "Ġzien": 23735, "Ġstyl": 23736, "ĠTW": 23737, "ĠCheese": 23738, "borne": 23739, "Ġobl": 23740, "ĠAlready": 23741, "Ġunstable": 23742, "Ġflames": 23743, "post": 23744, "Ha": 23745, "romagn": 23746, "ĠìĹĦë§Ī": 23747, "dest": 23748, "Ġkolej": 23749, "Ġtemporarily": 23750, "Ġdetermining": 23751, "ĠGlass": 23752, "ÑĢон": 23753, "olan": 23754, "Ġdominated": 23755, "åĮĸ": 23756, "____": 23757, "ĠÙĩذا": 23758, "ĠDana": 23759, "Ġdinheiro": 23760, "aqu": 23761, "민": 23762, "ĠÃłs": 23763, "ĠJoey": 23764, "ĠGriff": 23765, "Ġattain": 23766, "Ġtransitions": 23767, "ĠLiterally": 23768, "енд": 23769, "ĠHaven": 23770, "Ġgrabbing": 23771, "Ġcrystals": 23772, "ĠFourth": 23773, "Ġcandles": 23774, "ĠÑģлÑĥÑĩа": 23775, "rico": 23776, "Ġ5000": 23777, "etto": 23778, "Ġundo": 23779, "Ġkto": 23780, "Ġdivert": 23781, "Ġchir": 23782, "Ġpersec": 23783, "Ġhiking": 23784, "Ġannouncements": 23785, "çĶ±": 23786, "зÑĭ": 23787, "Ġauc": 23788, "Ġsystemic": 23789, "ĠRM": 23790, "Ïĥα": 23791, "ĠÐĶж": 23792, "Ġyar": 23793, "ĠWard": 23794, "Ġpissed": 23795, "Ġcarn": 23796, "Ġautonomous": 23797, "ãħİãħİ": 23798, "sover": 23799, "æ²ĴéĮ¯": 23800, "å¾Ī好": 23801, "Ġreflex": 23802, "Ġgardens": 23803, "Ġdated": 23804, "ì±": 23805, "amiÄĻ": 23806, "Ġcontinuity": 23807, "Ġcitizenship": 23808, "Ġschwer": 23809, "Ġzak": 23810, "table": 23811, "ĠÑģÑĩ": 23812, "è§ģ": 23813, "ĠÏĥε": 23814, "Ġgenerates": 23815, "구ëĤĺ": 23816, "öh": 23817, "óm": 23818, "alam": 23819, "ĠJUDY": 23820, "ĠBug": 23821, "Ġãģ¦": 23822, "Ġdrones": 23823, "Ġágua": 23824, "acaks": 23825, "æļ": 23826, "ĠÐļон": 23827, "×ĸ×Ķ": 23828, "Ġstrive": 23829, "ĠAltern": 23830, "Ġnearest": 23831, "Ġproyect": 23832, "tera": 23833, "ĠASHLEY": 23834, "Ġworm": 23835, "Ġreplay": 23836, "Ġtara": 23837, "ĠIndians": 23838, "ãĤ°": 23839, "icaid": 23840, "ĠìĪľ": 23841, "Ġappealing": 23842, "ĠWes": 23843, "Ġmentions": 23844, "Ġделе": 23845, "Ġkw": 23846, "Ġfragile": 23847, "isz": 23848, "ków": 23849, "hang": 23850, "color": 23851, "Ġpresidente": 23852, "87": 23853, "еÑĦ": 23854, "çĪ¸": 23855, "Ġдобав": 23856, "ĠNelson": 23857, "áfic": 23858, "ĠMICHAEL": 23859, "Ġmechanic": 23860, "Ġmetres": 23861, "ĠoczywiÅĽcie": 23862, "ĠCind": 23863, "ĠogsÃ¥": 23864, "Ġlandsca": 23865, "ACE": 23866, "Ġheadlines": 23867, "Ġcatalyst": 23868, "ĠCatch": 23869, "inkles": 23870, "Ġpills": 23871, "ordo": 23872, "Ġimmigrant": 23873, "Ġexamination": 23874, "Ġaccidents": 23875, "zÄħd": 23876, "Ġquiere": 23877, "Ġnella": 23878, "Ġ67": 23879, "Ġpassa": 23880, "Ġsuperfic": 23881, "istor": 23882, "Ġnov": 23883, "ëĭµ": 23884, "Ġmandate": 23885, "isons": 23886, "ĠVirtual": 23887, "Ġselber": 23888, "Ġcounseling": 23889, "ĠNBA": 23890, "Ġsept": 23891, "Ġbeliever": 23892, "Ġmarvel": 23893, "ĠIntegr": 23894, "ĠмÑĸ": 23895, "Ġorph": 23896, "Ġbackward": 23897, "ĠGeneration": 23898, "ĠPict": 23899, "ĠÑĤоÑĤ": 23900, "Ġtapi": 23901, "prochen": 23902, "Ġhallway": 23903, "hte": 23904, "ĠÛģÛĴ": 23905, "ĠZum": 23906, "èĢģ師": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ĠEddie": 23911, "ĠKil": 23912, "Ġwellness": 23913, "stock": 23914, "è¼ĥ": 23915, "Ġkaç": 23916, "Ġterrorism": 23917, "Ġpointer": 23918, "Of": 23919, "heric": 23920, "ĠUltimately": 23921, "Ġmeses": 23922, "ĠTrade": 23923, "Ġpint": 23924, "Ġtuition": 23925, "Ġdisagre": 23926, "Ġê²ĮìŀĦ": 23927, "Ġmanuscript": 23928, "Ġroomm": 23929, "Ġoutputs": 23930, "еÑĨи": 23931, "Ġries": 23932, "Ġsalud": 23933, "otzdem": 23934, "Ġmasses": 23935, "ĠbyÅĤa": 23936, "Ġclearing": 23937, "Ġdiscourse": 23938, "atson": 23939, "Ġfolded": 23940, "ĠJar": 23941, "ÙĦÙī": 23942, "900": 23943, "ĠÑĥÑģп": 23944, "Ġprophecy": 23945, "Ġinterfere": 23946, "иÑħод": 23947, "à¹Į": 23948, "Ġthri": 23949, "Ġ×ŀש": 23950, "Ġlazım": 23951, "Ġ1992": 23952, "Ġfuturo": 23953, "Ġlocking": 23954, "Ġembargo": 23955, "ĠNeither": 23956, "ivamente": 23957, "ĠmÃ¥ste": 23958, "Ġmik": 23959, "Ġcollector": 23960, "екоÑĤоÑĢ": 23961, "ĠGand": 23962, "Ġsentir": 23963, "ĠMight": 23964, "å¡Ķ": 23965, "Ġganzen": 23966, "UC": 23967, "Ġrelating": 23968, "SD": 23969, "Ġmosquito": 23970, "GR": 23971, "Ġhollow": 23972, "âĺħ": 23973, "ĠWalker": 23974, "Ġaffiliate": 23975, "Ġduplicate": 23976, "нем": 23977, "Ġgrape": 23978, "ĠOrganization": 23979, "Ġsynt": 23980, "Joe": 23981, "Ġgeg": 23982, "Ġrevealing": 23983, "ĠEthan": 23984, "outer": 23985, "Ġyay": 23986, "é«Ķ": 23987, "лаÑĢ": 23988, "Ġreportedly": 23989, "Ġihrer": 23990, "Ġrecognise": 23991, "Ġbumper": 23992, "ĠRandy": 23993, "ĠVenus": 23994, "tles": 23995, "Ġappetite": 23996, "Ġglucose": 23997, "Ġchodzi": 23998, "ĠFurthermore": 23999, "tir": 24000, "Ġconta": 24001, "Ġintuition": 24002, "Ġaltitude": 24003, "Ġchunks": 24004, "ĠJoshua": 24005, "ıģım": 24006, "rylic": 24007, "leans": 24008, "ĠíĶ¼ë": 24009, "LL": 24010, "Que": 24011, "Ġgor": 24012, "ĠзнаÑĩиÑĤ": 24013, "Ġpoems": 24014, "Ġexcel": 24015, "Ġexplored": 24016, "Ġpopul": 24017, "Ġincluso": 24018, "stä": 24019, "ĠGavin": 24020, "alling": 24021, "ĠÏĦον": 24022, "é©": 24023, "arbeit": 24024, "ĠGas": 24025, "Ġglorious": 24026, "rieben": 24027, "Ġspam": 24028, "Ġindoor": 24029, "Ġthrust": 24030, "ĠAld": 24031, "ĠPrior": 24032, "Ġonboard": 24033, "ãģłãģķãģĦ": 24034, "oca": 24035, "ASH": 24036, "£ł": 24037, "ĠChristine": 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27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, 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27930, "Ġfreel": 27931, "ĠAlzheimer": 27932, "ĠÅļ": 27933, "Her": 27934, "Ġtornado": 27935, "agens": 27936, "ĠìŀĪìĸ´ìĦľ": 27937, "ĠTransform": 27938, "Ġprocesso": 27939, "Ġmillise": 27940, "Ġprofessionally": 27941, "Ġmemb": 27942, "ocation": 27943, "Ġstyling": 27944, "ĠобÑıз": 27945, "ĠOperation": 27946, "Ġwygl": 27947, "ĠRan": 27948, "ĠçļĦ": 27949, "ĠKin": 27950, "á»±c": 27951, "ĠBAR": 27952, "Ġpaperwork": 27953, "Ġtule": 27954, "Ġqueria": 27955, "Ġcomply": 27956, "ĠHair": 27957, "×Ļ׼": 27958, "ĠпÑĢоÑģÑĤ": 27959, "Ġmutation": 27960, "Ġreprés": 27961, "Ġoctopus": 27962, "Ġimportantes": 27963, "Ġdeserved": 27964, "etr": 27965, "Ġdisasters": 27966, "lında": 27967, "iqué": 27968, "ĠDeshalb": 27969, "soo": 27970, "ossip": 27971, "Ġrelieved": 27972, "ĠCollins": 27973, "Ġwaterproof": 27974, "ĠYuk": 27975, "Ġcopying": 27976, "Ġbütün": 27977, "ĠHeute": 27978, "ĠEntre": 27979, "Ġresidual": 27980, "Ġcolonies": 27981, "Ġénorm": 27982, "ĠErin": 27983, "Ġstan": 27984, "Ġtremendously": 27985, "Ġcaptures": 27986, "ĠSai": 27987, "âce": 27988, "ĠmiaÅĤ": 27989, "Ġ87": 27990, "Ġlogging": 27991, "Ġinserted": 27992, "Ġinherently": 27993, "ìĿij": 27994, "lave": 27995, "ниÑĩ": 27996, "Ġfemmes": 27997, "Ġdép": 27998, "uks": 27999, "acia": 28000, "ĠWade": 28001, "Ġjij": 28002, "ĠVincent": 28003, "ĠIceland": 28004, "hem": 28005, "Ġapology": 28006, "ĠPeg": 28007, "Ġglued": 28008, "Ġcompanions": 28009, "ĠLiver": 28010, "Ġcriticized": 28011, "leading": 28012, "Ġsäga": 28013, "æ¼Ĥ": 28014, "Ġsquid": 28015, "Ġnarratives": 28016, "Ġtaka": 28017, "nez": 28018, "weit": 28019, "Ġtripod": 28020, "Ġexplic": 28021, "Ġspinal": 28022, "Ġapproximation": 28023, "Ġpagar": 28024, "ĠCalvin": 28025, "ĠведÑĮ": 28026, "Ġlac": 28027, "Ġproactive": 28028, "ĠTrain": 28029, "orf": 28030, "Ġsten": 28031, "Ġgrapes": 28032, "Ġmeus": 28033, "Ġautomat": 28034, "Ġbiased": 28035, "Ġchaîne": 28036, "coal": 28037, "Ġrencont": 28038, "ĠKum": 28039, "Ġfestivals": 28040, "Ġstartups": 28041, "Ġaka": 28042, "ãģ¹": 28043, "Ġcylind": 28044, "sna": 28045, "CRI": 28046, "Ġresultado": 28047, "Ġmilestone": 28048, "ĠÏħ": 28049, "Ġteleport": 28050, "zych": 28051, "62": 28052, "åħ³": 28053, "ĠFear": 28054, "Ġnucleus": 28055, "Ġshines": 28056, "hov": 28057, "ĠPartners": 28058, "ĠKas": 28059, "Ġnadie": 28060, "Ġalerts": 28061, "ĠBILL": 28062, "strong": 28063, "ĠNate": 28064, "ĠDenmark": 28065, "ĠCav": 28066, "OST": 28067, "hält": 28068, "ĠìķĦëĭĮ": 28069, "anyon": 28070, "Ġencourages": 28071, "ĠпоÑģÑĤав": 28072, "ĠHuang": 28073, "ãģĬé¡ĺãģĦ": 28074, "STA": 28075, "Ġpaints": 28076, "ãģĻãģĶ": 28077, "Ġschedules": 28078, "Ġcheated": 28079, "Ġapprox": 28080, "Ġï·": 28081, "Ġ».": 28082, "Ġsmiles": 28083, "isure": 28084, "Ġnered": 28085, "arden": 28086, "Ġcurt": 28087, "ĠëĮ": 28088, "ĠRoth": 28089, "Ġpuisque": 28090, "ĠGET": 28091, "ĠVeget": 28092, "Ġproduz": 28093, "ĠBelgium": 28094, "ĠCampus": 28095, "ר×Ļ×Ŀ": 28096, "icut": 28097, "ĠÑģним": 28098, "Ġréuss": 28099, "Ġslippery": 28100, "ĠEw": 28101, "ų": 28102, "ĠLegends": 28103, "ĠTiffany": 28104, "ализ": 28105, "ĠпеÑĢев": 28106, "ĠогÑĢом": 28107, "Ġcros": 28108, "ĠCE": 28109, "Bu": 28110, "Ġensures": 28111, "Ġgrandchildren": 28112, "Ġacuerdo": 28113, "Ġprisoner": 28114, "Ġthirsty": 28115, "bane": 28116, "Ġë¹ł": 28117, "Ġúltima": 28118, "ĠLaunch": 28119, "nity": 28120, "Ġcombustion": 28121, "Ġunicorn": 28122, "Ġfamille": 28123, "Ġlowering": 28124, "ĠYing": 28125, "building": 28126, "Ġduo": 28127, "ĠMéxico": 28128, "astian": 28129, "Ġ먹ìĿĦ": 28130, "ĠRalph": 28131, "Ġrewrite": 28132, "Ġglam": 28133, "ifique": 28134, "Er": 28135, "ĠRunning": 28136, "онов": 28137, "Ġmeanings": 28138, "Ġchewy": 28139, "ĠLeslie": 28140, "Ġfinest": 28141, "Ġhahaha": 28142, "ĠSTEP": 28143, "Ġloneliness": 28144, "rians": 28145, "Ġquestioned": 28146, "Ġesque": 28147, "Ġsinking": 28148, "Ġpeso": 28149, "ĠWrong": 28150, "asmine": 28151, "Ġdefinitive": 28152, "Ġbuys": 28153, "Ġcruc": 28154, "cool": 28155, "ĠëłĪ": 28156, "Ġpó": 28157, "Ġutilized": 28158, "Ġworthwhile": 28159, "ĠDylan": 28160, "ESE": 28161, "Ġvertex": 28162, "tı": 28163, "ĠFir": 28164, "Ġzaw": 28165, "ĠGed": 28166, "ĠÐĿап": 28167, "dz": 28168, "Ġcursor": 28169, "Ġswipe": 28170, "Ġinevitably": 28171, "Ġposters": 28172, "Ġinclined": 28173, "Ġgreeting": 28174, "Ġdisappointment": 28175, "ãģ¾ãģ§": 28176, "Ġrelação": 28177, "TT": 28178, "Ġrabb": 28179, "ĠMaine": 28180, "Ġanalyzed": 28181, "FE": 28182, "ĠÐŁÐ¾Ð»": 28183, "ĠSandra": 28184, "Ġplague": 28185, "ARE": 28186, "Ġvär": 28187, "ĠViv": 28188, "umed": 28189, "hando": 28190, "houette": 28191, "ĠBailey": 28192, "ä¸įéģİ": 28193, "yson": 28194, "Ġsemua": 28195, "Ġhardcore": 28196, "âĤ¬": 28197, "Ñĸм": 28198, "éra": 28199, "OTH": 28200, "Ġforeigners": 28201, "ĠPalestinian": 28202, "Ġproprio": 28203, "аний": 28204, "Ġmyths": 28205, "WH": 28206, "Ġninth": 28207, "ĠCreator": 28208, "лом": 28209, "ĠFlip": 28210, "Ġeman": 28211, "ĠkiÅŁ": 28212, "zieh": 28213, "ĠEarnest": 28214, "system": 28215, "ĸìĹIJ": 28216, "Ġarmies": 28217, "ĠOutside": 28218, "Ġharus": 28219, "æºĸ": 28220, "одаÑĢ": 28221, "Ġvisitor": 28222, "çŃĶ": 28223, "Ġstrengthening": 28224, "Ġ92": 28225, "vio": 28226, "Ġ리": 28227, "Ġgreedy": 28228, "Ġpoquito": 28229, "uder": 28230, "ĠKopf": 28231, "Ġëĭ¤ìĿĮìĹIJ": 28232, "Ġseis": 28233, "ático": 28234, "Ġtrusting": 28235, "ÃŃp": 28236, "ĠEmm": 28237, "leen": 28238, "ĠاÙĦÙĨ": 28239, "Ġrecruitment": 28240, "ĠFilip": 28241, "ĠÙĥÙĦ": 28242, "Clint": 28243, "ĠвеÑģ": 28244, "auft": 28245, "Ġdominate": 28246, "Ġresto": 28247, "Ġkra": 28248, "ái": 28249, "ĠCait": 28250, "rows": 28251, "Ġcountryside": 28252, "Ġ1945": 28253, "аÑĨиÑİ": 28254, "Ġди": 28255, "Ġkernel": 28256, "lov": 28257, "Ġcalculating": 28258, "دا": 28259, "ĠWalt": 28260, "Ġempowering": 28261, "Ġchassis": 28262, "linear": 28263, "гÑĥ": 28264, "Ġnova": 28265, "Ġuy": 28266, "Ġ69": 28267, "Ġencompass": 28268, "trl": 28269, "Ġcomputational": 28270, "Ġworms": 28271, "Ġnhiá»ģu": 28272, "Ġastronauts": 28273, "Ġves": 28274, "Ġsytu": 28275, "Ġdemanded": 28276, "Ġcs": 28277, "ĠMol": 28278, "Ġ`": 28279, "Ġchant": 28280, "Ġthereby": 28281, "Ġpenis": 28282, "Ġemoc": 28283, "wyn": 28284, "Ñĥже": 28285, "Ġtread": 28286, "óle": 28287, "Ġdeepest": 28288, "Ġmache": 28289, "ĠVent": 28290, "ĠAmsterdam": 28291, "ãĥĽ": 28292, "Ġrebel": 28293, "Ġ61": 28294, "ĠвкÑĥÑģ": 28295, "uffs": 28296, "ĠdoÄŁru": 28297, "ĠNapole": 28298, "ήÏĥ": 28299, "Ġworkouts": 28300, "ĠGlad": 28301, "неÑģ": 28302, "Ġtensions": 28303, "ĠShift": 28304, "ĠGuer": 28305, "íĮIJ": 28306, "Ġì¹ľêµ¬": 28307, "Ðĸ": 28308, "Ġimplant": 28309, "êu": 28310, "ê¸Ģ": 28311, "Ġauthorized": 28312, "CER": 28313, "ĠRV": 28314, "Ġhil": 28315, "lev": 28316, "cimento": 28317, "ĠUFO": 28318, "ìĥĪ": 28319, "è¨Ĥ": 28320, "wor": 28321, "Ġdances": 28322, "ĠPixel": 28323, "çľĭä¸Ģä¸ĭ": 28324, "Ġtrotzdem": 28325, "Ġobten": 28326, "ĠAlfred": 28327, "Ġcostly": 28328, "ĠStanley": 28329, "Ġterrorists": 28330, "ĠWid": 28331, "ħëĭĪëĭ¤": 28332, "Ġleicht": 28333, "ìĿ´ìĬ¤": 28334, "Ġdobrze": 28335, "Ġhesit": 28336, "Ġerzäh": 28337, "Ġeinige": 28338, "Ġhebt": 28339, "Ñģе": 28340, "Ġunpredict": 28341, "Cómo": 28342, "remos": 28343, "ĠThankfully": 28344, "Ġpurse": 28345, "chs": 28346, "ancer": 28347, "ulos": 28348, "stud": 28349, "æľīæ²Ĵæľī": 28350, "Ġneurolog": 28351, "ĠAncient": 28352, "Out": 28353, "awsze": 28354, "Ġoppose": 28355, "Ġantibodies": 28356, "ĠSomehow": 28357, "ropolitan": 28358, "ktor": 28359, "ĠÑģÑĤоÑĢонÑĭ": 28360, "Ġrockets": 28361, "Ġdisable": 28362, "Ġcatastroph": 28363, "´ìŀ": 28364, "Ġcyn": 28365, "ĠдÑĢÑĥзÑĮÑı": 28366, "Ġinstructors": 28367, "emaal": 28368, "Ġetwa": 28369, "Ġyuan": 28370, "ĠGround": 28371, "Ġpremiere": 28372, "Ñĩив": 28373, "Ġsaint": 28374, "yba": 28375, "Ġkok": 28376, "Ġcontractors": 28377, "Ġê°ģ": 28378, "Ġ×IJ׾": 28379, "Ġheadline": 28380, "Ġcompletamente": 28381, "Ġinexpensive": 28382, "Ġviu": 28383, "ĠGrande": 28384, "Ġbleed": 28385, "물": 28386, "Ġ73": 28387, "ĠtodavÃŃa": 28388, "ĠRush": 28389, "ĠElder": 28390, "ê°ĢëĬĶ": 28391, "ĠRou": 28392, "ĠженÑī": 28393, "ĠMira": 28394, "Ġdeine": 28395, "Ġkarma": 28396, "Ġumm": 28397, "Ġentsche": 28398, "ĠHolocaust": 28399, "Ġdiscoveries": 28400, "aments": 28401, "Ġraison": 28402, "Ġburgers": 28403, "Back": 28404, "Ġgdy": 28405, "ĠAG": 28406, "ĠDaw": 28407, "ìķł": 28408, "headed": 28409, "ĠClar": 28410, "Inst": 28411, "ĠLieutenant": 28412, "ĠAfD": 28413, "ĠCes": 28414, "Ġpersonalized": 28415, "Ġinterfaces": 28416, "à¸Īะ": 28417, "ĠÑĢеж": 28418, "Ġsuic": 28419, "Ġstarving": 28420, "Ġoxide": 28421, "Ġdecorated": 28422, "ĠDU": 28423, "ĠìĺĪìģĺ": 28424, "Ġquo": 28425, "Ġdistortion": 28426, "段": 28427, "Ġ먹ìĸ´ë": 28428, "Ġstakes": 28429, "æĺİçĻ½": 28430, "Ġsyntax": 28431, "Ġbiết": 28432, "thy": 28433, "icie": 28434, "Ġbrasile": 28435, "isis": 28436, "RC": 28437, "Ġshook": 28438, "Ġdepths": 28439, "ĠCosta": 28440, "Ġvocals": 28441, "Ġcoaster": 28442, "Ġfalou": 28443, "ettle": 28444, "Ġkennen": 28445, "Ġderive": 28446, "Ġaids": 28447, "ĠÐĿик": 28448, "Ġentwic": 28449, "Ġvertically": 28450, "ĠÍ": 28451, "ĠSUV": 28452, "Ġfireworks": 28453, "Ġspecifics": 28454, "交": 28455, "Ġinsisted": 28456, "Ġdeshalb": 28457, "ĠGonz": 28458, "love": 28459, "ĠMilitary": 28460, "ĠPierre": 28461, "ĠâĪ": 28462, "ĠWhose": 28463, "Ġperfume": 28464, "ĠÏĢε": 28465, "Ġlowered": 28466, "Ġcrosses": 28467, "Ġtranslates": 28468, "Ġarriba": 28469, "ÃŃdo": 28470, "ĠLev": 28471, "åħ§": 28472, "ĠCiao": 28473, "Ġscholarships": 28474, "Ġgestures": 28475, "ĠÑĢезÑĥлÑĮÑĤаÑĤ": 28476, "Ġquestão": 28477, "ĠColonel": 28478, "ĠBott": 28479, "رÙģ": 28480, "NING": 28481, "ĠWatching": 28482, "ĠPurple": 28483, "ÑģÑĤÑĢан": 28484, "Ġexecutives": 28485, "ĠKris": 28486, "orneys": 28487, "еннÑĭй": 28488, "Ġcoated": 28489, "Ä©": 28490, "Ġparked": 28491, "ĠÑģвеÑĤ": 28492, "!!!!!": 28493, "ĠFloyd": 28494, "ısı": 28495, "ziÄĩ": 28496, "Ġmotivate": 28497, "ĠElon": 28498, "lean": 28499, "Ĩĵ": 28500, "Ġip": 28501, "Ġniż": 28502, "ĠExperience": 28503, "ĠTina": 28504, "ĠKollege": 28505, "ĠAmbassador": 28506, "inya": 28507, "Ġtheft": 28508, "Ġheures": 28509, "ĠMyst": 28510, "Ġmaison": 28511, "leb": 28512, "Ġbowls": 28513, "ĠBürger": 28514, "ĠRoosevelt": 28515, "RP": 28516, "ê°ĢìļĶ": 28517, "ĠDelicious": 28518, "erdings": 28519, "ĠAssociate": 28520, "ousse": 28521, "ĠCort": 28522, "ĠRepeat": 28523, "ĠGlory": 28524, "Ġcontag": 28525, "à¹Ģล": 28526, "ĠParad": 28527, "ĠKerry": 28528, "Ġê¿": 28529, "ĠWave": 28530, "å¿ħ": 28531, "Ġgateway": 28532, "çIJĥ": 28533, "!ãĢį": 28534, "Ġtranscend": 28535, "Ġdamages": 28536, "Ġtails": 28537, "Ġgravitational": 28538, "ĠShield": 28539, "Ġprimitive": 28540, "Ġcarriers": 28541, "ĠHuawei": 28542, "ÙĤد": 28543, "Ġfeliz": 28544, "ĠMia": 28545, "åĥķ": 28546, "ĠпÑĢÑıмо": 28547, "ĠпÑĢоиÑģÑħодиÑĤ": 28548, "ĠMurphy": 28549, "ĠActiv": 28550, "ãĥĥãĤ¯": 28551, "Ġdiscomfort": 28552, "×ij×Ķ": 28553, "ĠKell": 28554, "ĠCentury": 28555, "Ġspaghetti": 28556, "ĠDurch": 28557, "Ġcierto": 28558, "ĠEmpress": 28559, "Ġguts": 28560, "neg": 28561, "ĠдоÑģÑĤаÑĤоÑĩно": 28562, "Ġvoluntary": 28563, "失": 28564, "Ġsquirrel": 28565, "欢": 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29381, "Ġrainfall": 29382, "ĠMouse": 29383, "Ġrefund": 29384, "ĠZw": 29385, "assemb": 29386, "Ġ220": 29387, "ĠOrd": 29388, "è§Ĵ": 29389, "Ġveins": 29390, "ĠGiant": 29391, "Ġmãe": 29392, "Ġvap": 29393, "Ġmisses": 29394, "οÏħÏĤ": 29395, "Mo": 29396, "ĠEntwick": 29397, "INT": 29398, "ÙĨت": 29399, "Ġtheoretically": 29400, "Ġtearing": 29401, "Ġtroubled": 29402, "prem": 29403, "Ġrepetitive": 29404, "Ġâĸ": 29405, "Ġheavenly": 29406, "ĠAmber": 29407, "Ġполож": 29408, "Ġíķ´ì¤": 29409, "Ġvowel": 29410, "anking": 29411, "ĠWirtschaft": 29412, "Ġirr": 29413, "Ġcozy": 29414, "Ġunfamiliar": 29415, "ĠPors": 29416, "Ġë§ŀìķĦ": 29417, "ĠTimothy": 29418, "ÑģолÑİÑĤ": 29419, "pex": 29420, "ĠVIS": 29421, ")(": 29422, "Ġsuperst": 29423, "Ġimprov": 29424, "ĠBeng": 29425, "Ġdisconnected": 29426, "Ġapt": 29427, "ÑĢен": 29428, "ĠExtra": 29429, "Ġбел": 29430, "shop": 29431, "dings": 29432, "ĠConnecticut": 29433, "ì°¬": 29434, "ĠGC": 29435, "åıĸ": 29436, "beh": 29437, "Jeremy": 29438, "ĠBatt": 29439, "ãģ¸": 29440, 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29554, "Ġkara": 29555, "าà¸Ļ": 29556, "Ġfavorable": 29557, "ĠìķĬìķĺ": 29558, "ĠHispanic": 29559, "à¸ľ": 29560, "Ġ×Ķ׼": 29561, "Ġvalidate": 29562, "ĠResident": 29563, "Ġcomenz": 29564, "beiter": 29565, "erer": 29566, "ä¸Ģèµ·": 29567, "Ġdado": 29568, "atching": 29569, "metros": 29570, "ĠHin": 29571, "ĠDum": 29572, "Ġhazır": 29573, "ĠNatalie": 29574, "Ġencryption": 29575, "оÑĩка": 29576, "mma": 29577, "houses": 29578, "Ġanalytical": 29579, "ĠDang": 29580, "first": 29581, "æŃĮ": 29582, "çºĮ": 29583, "ĠEnc": 29584, "cando": 29585, "Ġludzi": 29586, "wart": 29587, "Ġstatistic": 29588, "ĠìĤ°": 29589, "Ġcommenting": 29590, "Ġcoordinated": 29591, "ĠHyper": 29592, "åļ": 29593, "ĠBert": 29594, "çľ¾": 29595, "ĠHip": 29596, "kem": 29597, "ünü": 29598, "Ġzal": 29599, "ĠíķĺëĬĶëį°": 29600, "ĠRobot": 29601, "éĸ±": 29602, "rawn": 29603, "Ġrhetoric": 29604, "ullah": 29605, "ĠDiet": 29606, "Ġtakich": 29607, "Ġpossessed": 29608, "ĵľëĬĶ": 29609, "Ġwakes": 29610, "ĠRaf": 29611, "Mart": 29612, "Ġecc": 29613, 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"ĠSlowly": 29674, "ĠBrig": 29675, "ĠTorah": 29676, "Ġache": 29677, "Ġ???": 29678, "ĠDob": 29679, "kannt": 29680, "Mary": 29681, "Ġstam": 29682, "ĠDemon": 29683, "pla": 29684, "ĠFreund": 29685, "ĠBenn": 29686, "Ġhighs": 29687, "Ġکر": 29688, "ĠPrepare": 29689, "Ġproxy": 29690, "Ġcampo": 29691, "ĠAugen": 29692, "£¨ë": 29693, "ĠChloe": 29694, "icularly": 29695, "young": 29696, "ĠãģĮ": 29697, "©Ķë": 29698, "Ġscratching": 29699, "Ġglac": 29700, "Ġgemeinsam": 29701, "anal": 29702, "acaksın": 29703, "ĠForum": 29704, "ennial": 29705, "ĠResources": 29706, "ãģ¨æĢĿãģĦãģ¾ãģĻ": 29707, "Ġmeisten": 29708, "ĠFell": 29709, "Ġunanim": 29710, "ĠTB": 29711, "ĠSelbst": 29712, "æĨ": 29713, "Ġintimidating": 29714, "ĠGefühl": 29715, "Ġì½Ķë¡ľ": 29716, "æĭī": 29717, "idor": 29718, "iciones": 29719, "arsa": 29720, "]..": 29721, "azo": 29722, "Ġkendi": 29723, "ĠTage": 29724, "termin": 29725, "ĠProzent": 29726, "Maybe": 29727, "lé": 29728, "Ġquesti": 29729, "Ġmemes": 29730, "Ġcorre": 29731, "ĠVIP": 29732, "ĠGallery": 29733, "Ġurgency": 29734, "Ġnoche": 29735, "Ġkindly": 29736, "ĠMeredith": 29737, "ĠváºŃy": 29738, "ĠاÙĦب": 29739, "ĠEstado": 29740, "åĩºä¾Ĩ": 29741, "zug": 29742, "oque": 29743, "Ġobesity": 29744, "Off": 29745, "ĠEuropeans": 29746, "öd": 29747, "ì¹´ë": 29748, "Ġhoop": 29749, "Ġenjoys": 29750, "ĠChip": 29751, "patient": 29752, "Ġmicroscope": 29753, "Ġlegitim": 29754, "ĠÑıвлÑıеÑĤÑģÑı": 29755, "Ïĥι": 29756, "argent": 29757, "Ġsham": 29758, "Ġlicensing": 29759, "olia": 29760, "Sorry": 29761, "rama": 29762, "Ġaccelerated": 29763, "Ġwym": 29764, "Ġfairness": 29765, "ĠReading": 29766, "Ġslack": 29767, "ĠDok": 29768, "ziÄĻkujÄĻ": 29769, "Ġrubbing": 29770, "аÑĤÑĥ": 29771, "Ġallocated": 29772, "jung": 29773, "Ġpains": 29774, "Ġwinding": 29775, "Ġgeliyor": 29776, "ĠCU": 29777, "mot": 29778, "cock": 29779, "ĠPosition": 29780, "bros": 29781, "Ġlivestream": 29782, "ĠBrain": 29783, "ì°©": 29784, "Ġprzek": 29785, "ĠEi": 29786, "ĠCoco": 29787, "ба": 29788, "Ġshovel": 29789, "ãĥıãĥı": 29790, "ea": 29791, "Ġchocol": 29792, "Ġrebellion": 29793, "Ġshowc": 29794, "ĠHalo": 29795, "Ġdividend": 29796, "mission": 29797, "Ġusando": 29798, "Ġ[\"": 29799, "Ġfalei": 29800, "æĽ¸": 29801, "Black": 29802, "ĠSurely": 29803, "ĠÅ»": 29804, "Ġphilosopher": 29805, "ä½łä»¬": 29806, "Ġoverhe": 29807, "ĠBorn": 29808, "Ġobjetivo": 29809, "Ġ128": 29810, "scheid": 29811, "ĠNazis": 29812, "Ġsolche": 29813, "lift": 29814, "cede": 29815, "adors": 29816, "Ġmarshm": 29817, "ĠLORD": 29818, "ĶìĿ´íģ¬": 29819, "Ġowning": 29820, "Cont": 29821, "Ġlandscapes": 29822, "Ġlending": 29823, "ĠAuthority": 29824, "овой": 29825, "oqu": 29826, "ĠSes": 29827, "ĠFerrari": 29828, "Ġresponsabil": 29829, "Ġvários": 29830, "Ġdelic": 29831, "Ġembark": 29832, "Ġembroider": 29833, "Ġframeworks": 29834, "Ġsimmer": 29835, "Ġnacional": 29836, "Ġremainder": 29837, "ĠVielleicht": 29838, "Ġquieres": 29839, "ìĹĶ": 29840, "Ġtestoster": 29841, "ihen": 29842, "ĠOz": 29843, "èle": 29844, "Ġportrayed": 29845, "κε": 29846, "ĠPolitik": 29847, "Ġaperture": 29848, "Ġbland": 29849, "indust": 29850, "ĠобÑĢаÑĤ": 29851, "ĠThous": 29852, "Bay": 29853, "Ġdando": 29854, "Ġsher": 29855, "Ġadmissions": 29856, "ĠCrew": 29857, "ĠÑĸн": 29858, "SINGING": 29859, "Ġounce": 29860, "Ġiy": 29861, "Ġbasil": 29862, "Ġovertime": 29863, "Ġthreaten": 29864, "Ġpartnered": 29865, "ĠCann": 29866, "avana": 29867, "ĠзнаеÑĤе": 29868, "éĢĻäºĽ": 29869, "ĠоÑĤÑģ": 29870, "ĠTudo": 29871, "ì½Ķ": 29872, "ĠëĨĢë": 29873, "fel": 29874, "Ġrearr": 29875, "Ġinward": 29876, "ĠRogers": 29877, "à¹ĥห": 29878, "Ġtweak": 29879, "Ġdryer": 29880, "cession": 29881, "Ġrigorous": 29882, "ĠDaar": 29883, "omics": 29884, "Ġfats": 29885, "vad": 29886, "Ġzipper": 29887, "acceptable": 29888, "Ġdemonstrating": 29889, "ĠYum": 29890, "Ġbeau": 29891, "Ġroster": 29892, "Ġpredominantly": 29893, "еÑĢÑĥ": 29894, "ningar": 29895, "Ġtriangles": 29896, "Ġtexting": 29897, "Ġberries": 29898, "ĠìĤ¬ì§Ħ": 29899, "éĶĻ": 29900, "adder": 29901, "Ġfaites": 29902, "ĠImage": 29903, "lere": 29904, "Ġbounds": 29905, "ĠLaur": 29906, "ĠìķĦ무ë": 29907, "Ġmio": 29908, "Ġusa": 29909, "ĠØ°": 29910, "Ġtoen": 29911, "ĠJang": 29912, "že": 29913, "chod": 29914, "anan": 29915, "ĠобÑĢазом": 29916, "Ġpersever": 29917, "ĠSwe": 29918, "Ġaugment": 29919, "ä¸ĥ": 29920, "uggling": 29921, "ièrement": 29922, "istles": 29923, "acjÄĻ": 29924, "91": 29925, "Ġmah": 29926, "ĠKIR": 29927, "Die": 29928, "Ġdownhill": 29929, "Ġ1968": 29930, "оÑĢоÑĪо": 29931, "å¹¹": 29932, "ographics": 29933, "Ġtässä": 29934, "ê²łì£ł": 29935, "ĠлиÑĩ": 29936, "AUDIO": 29937, "ĠплоÑħ": 29938, "Ġproposing": 29939, "éł»": 29940, "Ġtempted": 29941, "Ġconverting": 29942, "ĠLehr": 29943, "Ġpersone": 29944, "ĠFeeling": 29945, "ìĸ´ì£¼": 29946, "ombres": 29947, "Ġ׾×Ļ": 29948, "Ġguru": 29949, "Ġdement": 29950, "низ": 29951, "иÑĤелей": 29952, "Ġcompañ": 29953, "æľª": 29954, "å¸ĮæľĽ": 29955, "Ġredo": 29956, "Ġconductor": 29957, "mia": 29958, "Ġidols": 29959, "ĠMul": 29960, "Ġinex": 29961, "Ġtämä": 29962, "Ġimpacting": 29963, "Ġdaylight": 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"ãħĭãħĭãħĭ": 30021, "ĠOriginal": 30022, "×Ļ×Ĺ": 30023, "Ġbroadcasting": 30024, "ĠпеÑĢвÑĭй": 30025, "uchi": 30026, "Ġheure": 30027, "Ġgrabs": 30028, "ĠWOR": 30029, "ĠPlaid": 30030, "Min": 30031, "Ġpaz": 30032, "ĠPuis": 30033, "umu": 30034, "itates": 30035, "Ġcoats": 30036, "Ġbuen": 30037, "Ġheir": 30038, "Ġpneum": 30039, "שר": 30040, "enser": 30041, "ĠJUDGE": 30042, "Ġblonde": 30043, "á¹Ľ": 30044, "Ġgak": 30045, "Ġsık": 30046, "Ġquoted": 30047, "Ġequipo": 30048, "Ġwishing": 30049, "ÃŃcia": 30050, "Ġverbs": 30051, "çµĦ": 30052, "ĠCanadians": 30053, "Ġgoverning": 30054, "ĠEvans": 30055, "Euro": 30056, "Ġgenres": 30057, "Ġunterschied": 30058, "ĠBecky": 30059, "³¼ê²ĮìļĶ": 30060, "Ġeinge": 30061, "ĠRaise": 30062, "oland": 30063, "ĠStrateg": 30064, "Ġeres": 30065, "ĠVeterans": 30066, "Ġbreakout": 30067, "Ġsanté": 30068, "Ġadel": 30069, "Ġinvestigated": 30070, "Ġpeur": 30071, "Ġagile": 30072, "Ġrailroad": 30073, "anska": 30074, "Ġей": 30075, "Ġexpos": 30076, "atories": 30077, "ĠContent": 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30196, "Ġextraction": 30197, "ĠAV": 30198, "ĠGibbs": 30199, "Ġíľ": 30200, "Ġsı": 30201, "IAM": 30202, "View": 30203, "ĠGRANT": 30204, "Ġ몸": 30205, "Ġverification": 30206, "Ġdepicted": 30207, "ĠMoz": 30208, "oux": 30209, "Ġtul": 30210, "Ġscanner": 30211, "Ġcomedian": 30212, "ĠVolks": 30213, "ĠJEFF": 30214, "è¨Ĥéĸ±": 30215, "§Ħ": 30216, "Ġdistraction": 30217, "rá": 30218, "ĠINTER": 30219, "Ġsincer": 30220, "Ġ×ŀת": 30221, "Ġש׳": 30222, "Ġconstructive": 30223, "arf": 30224, "ĠëĪĦë": 30225, "Ġeco": 30226, "ramos": 30227, "Ġrenewed": 30228, "inement": 30229, "ĠUb": 30230, "ĠPepper": 30231, "ì§Ģê°Ģ": 30232, "ĠDarwin": 30233, "Ġmerchand": 30234, "Ġvárias": 30235, "èce": 30236, "NG": 30237, "ĠìľĦíķ´ìĦľ": 30238, "ĠакÑĤив": 30239, "ĠUnters": 30240, "عÙĦ": 30241, "Ġintric": 30242, "omma": 30243, "ieving": 30244, "ĠCaroline": 30245, "åĵģ": 30246, "ĠPRES": 30247, "Ġperformer": 30248, "Ġautour": 30249, "ãģ¾ãģĽãĤĵ": 30250, "Ġutterly": 30251, "Ġsynthesis": 30252, "Ġlesbian": 30253, "Ġretrieve": 30254, 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30313, "mesi": 30314, "Ġrelaxation": 30315, "ĠÑģвоиÑħ": 30316, "Ġmargins": 30317, "ĠsaÄŁ": 30318, "ĠPride": 30319, "ĠÏĦοÏħÏĤ": 30320, "иÑĨи": 30321, "enci": 30322, "Does": 30323, "Ġcorpse": 30324, "Ġendurance": 30325, "Ġíŀĺ": 30326, "ì¹´": 30327, "Ġhaircut": 30328, "Ġinterrupted": 30329, "Ġwindy": 30330, "ĠCaleb": 30331, "ÏģÏĩ": 30332, "ĠPourquoi": 30333, "Ġholistic": 30334, "uclear": 30335, "ĠWhole": 30336, "士": 30337, "Act": 30338, "Ġgallon": 30339, "cade": 30340, "ĠRegional": 30341, "roads": 30342, "ĠSchne": 30343, "áng": 30344, "Ġизмен": 30345, "ãĤĪãģŃ": 30346, "Ġmenus": 30347, "Ġsplitting": 30348, "Ġpriced": 30349, "ĠÎĵ": 30350, "Ġusername": 30351, "ĠÐŀÑĩ": 30352, "Ġcompressed": 30353, "yin": 30354, "Ġguardian": 30355, "Ġgoof": 30356, "Ġchecklist": 30357, "Ġinterchange": 30358, "Ġexpedition": 30359, "Ġextern": 30360, "Ġinfrared": 30361, "engo": 30362, "Ġdenying": 30363, "Ġpackets": 30364, "onent": 30365, "BB": 30366, "ĠIncre": 30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, 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"selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 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33211, "ĠпоÑģÑĤоÑıн": 33212, "ĠHood": 33213, "Huh": 33214, "Ġë¶ģ": 33215, "Ġhyung": 33216, "ĠMeeting": 33217, "Ġimporta": 33218, "Ġì°¾ìķĦ": 33219, "ĠVern": 33220, "Ġstripped": 33221, "Ġrefuses": 33222, "Ġqualifications": 33223, "opl": 33224, "ĢëıĦ": 33225, "ixÃŃ": 33226, "Ġdiab": 33227, "itime": 33228, "flows": 33229, "Ġinac": 33230, "ĠGong": 33231, "Ġmeaningless": 33232, "Ġcourageous": 33233, "Ġmicrobi": 33234, "azy": 33235, "hist": 33236, "Ġvolunteering": 33237, "VIE": 33238, "Ġviolated": 33239, "Ġsympathy": 33240, "ĠEdit": 33241, "好åĥı": 33242, "electric": 33243, "product": 33244, "Ġpandemia": 33245, "Ġgeometric": 33246, "ĠConvers": 33247, "gre": 33248, "Ġglut": 33249, "isted": 33250, "ĠاÙĦÙĥ": 33251, "ĠChain": 33252, "ĠPresent": 33253, "ĠYin": 33254, "ĠÑģог": 33255, "ĠVlog": 33256, "Ġìĸ´ë¨¸": 33257, "Ġdonn": 33258, "Ġhitch": 33259, "ucking": 33260, "ãģĬãģĦ": 33261, "wald": 33262, "risk": 33263, "Ġhari": 33264, "ĠKens": 33265, "ĠIdol": 33266, "Ġвнимание": 33267, "Ġtodd": 33268, 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33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 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36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, 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36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, 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"Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 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"Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, 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"Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, 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"ĠìŀĪëĬĶëį°ìļĶ": 43550, "zew": 43551, "baren": 43552, "Ġstol": 43553, "Ġbelongings": 43554, "ĠResearchers": 43555, "Ġefendim": 43556, "ÏħÏĩ": 43557, "ÅĤÄħcz": 43558, "ĠUng": 43559, "ĠJub": 43560, "Ġcerebral": 43561, "á»ĩu": 43562, "Ġצר": 43563, "ĠподаÑĢ": 43564, "Ġmarched": 43565, "Ġawaken": 43566, "Ġako": 43567, "Ġacept": 43568, "Ġinitiation": 43569, "è¯ī": 43570, "lot": 43571, "ĠwÅĤas": 43572, "ĠMongol": 43573, "utral": 43574, "Ġtentang": 43575, "Ġinversion": 43576, "ĠìĿ´íĽĦ": 43577, "Ġlok": 43578, "ÅĤbym": 43579, "RS": 43580, "Ġstos": 43581, "Ġinteracts": 43582, "ĠCalendar": 43583, "Ġvanish": 43584, "Ġphysiology": 43585, "Ġlinearly": 43586, "ĠJY": 43587, "ÄŁan": 43588, "funded": 43589, "iziert": 43590, "Ġzmian": 43591, "ĠGrill": 43592, "Ġunbelievably": 43593, "otechnology": 43594, "ĠCars": 43595, "ĠÙĨÛģ": 43596, "ĠFolge": 43597, "ĠBeverly": 43598, "äischen": 43599, "Ġaumento": 43600, "ìĽĮìĦľ": 43601, "Ġmailbox": 43602, "Ġsteeds": 43603, "ĠPeak": 43604, "å·§": 43605, "Ġwykor": 43606, "Ġprawda": 43607, "иÑĤÑĭ": 43608, "Ġdiscours": 43609, "Ġaccuse": 43610, "cesso": 43611, "uire": 43612, "Ġпопад": 43613, "Ġtha": 43614, "Ġmeasurable": 43615, "beeping": 43616, "ĠInnen": 43617, "ĠпÑıÑĤÑĮ": 43618, "Ġcompeted": 43619, "ĠItalians": 43620, "Ġencontra": 43621, "Ġniew": 43622, "Ġfiltration": 43623, "ĠпÑĢоÑĦеÑģÑģ": 43624, "Ġpajamas": 43625, "Ġcilantro": 43626, "ĠSoc": 43627, "Luc": 43628, "Ġê¹Ģë": 43629, "ĠOdd": 43630, "Ġhydration": 43631, "мов": 43632, "Ġplywood": 43633, "ĠCompetition": 43634, "изнеÑģ": 43635, "flight": 43636, "ĠBeit": 43637, "bourg": 43638, "Ġcoils": 43639, "Ġcâmera": 43640, "Ġamended": 43641, "Äģm": 43642, "Angel": 43643, "ĠStacy": 43644, "flo": 43645, "Ġnormale": 43646, "Ġconsonant": 43647, "Ġaccompanying": 43648, "кÑĸ": 43649, "Ġirritated": 43650, "ĠfÃ¥tt": 43651, "Ġcrocodile": 43652, "IJĺëĬĶ": 43653, "Ġalbeit": 43654, "ĠPhilosophy": 43655, "ç´¯": 43656, "ÅĨ": 43657, "ytic": 43658, "Ġrèg": 43659, "Ġfrança": 43660, "Ġattentive": 43661, "Ham": 43662, "Ġalrededor": 43663, "æĿ¿": 43664, "sei": 43665, "ĠÑģвид": 43666, "Ġgimbal": 43667, "Ġchina": 43668, "ĠðŁİ¶": 43669, "ĠÐĴам": 43670, "Ġstimulating": 43671, "ĠOra": 43672, "ytes": 43673, "Ġheft": 43674, "Ġhaters": 43675, "Ġcomplexes": 43676, "Ġ03": 43677, "ród": 43678, "clear": 43679, "Ġbesteht": 43680, "çķĻè¨Ģ": 43681, "wny": 43682, "moil": 43683, "Ġsloppy": 43684, "Ġinsignificant": 43685, "Ġdubbed": 43686, "Ġëĸł": 43687, "Ġconsigo": 43688, "лÑĥÑĪай": 43689, "Sn": 43690, "Ġ×Ķצ": 43691, "ĠÎĮ": 43692, "Ġnadzie": 43693, "Ġfreshmen": 43694, "taa": 43695, "ĠuwagÄĻ": 43696, "ĠFavorite": 43697, "ĠCriminal": 43698, "Ġeviden": 43699, "Ġsymb": 43700, "Les": 43701, "ĠBeau": 43702, "uned": 43703, "plement": 43704, "Ac": 43705, "Ġdermat": 43706, "ĠNolan": 43707, "Ñĭп": 43708, "Ġsitt": 43709, "Ġeverlasting": 43710, "Ġestavam": 43711, "Ġмик": 43712, "Ġkhác": 43713, "Ġinvit": 43714, "Ġtreble": 43715, "Ġjig": 43716, "mani": 43717, "Ġtuvo": 43718, "ĠRUS": 43719, "ĠErde": 43720, "ĠDziÄĻkujÄĻ": 43721, "Ġblueberries": 43722, "kell": 43723, "acions": 43724, "çĪ·": 43725, "ви": 43726, "LET": 43727, "Ġsprout": 43728, "Ġspor": 43729, "Ġbên": 43730, "ĠMona": 43731, "ĠContain": 43732, "ĠKeys": 43733, "озÑı": 43734, "Ġfunción": 43735, "Ġrappelle": 43736, "Ġevolves": 43737, "Ġscraping": 43738, "Ġcomentários": 43739, "Ġpratique": 43740, "Ġauxiliary": 43741, "ĠSponge": 43742, "Ñģким": 43743, "uvo": 43744, "ĠÑģамо": 43745, "Ġsank": 43746, "Ġhighways": 43747, "Ġinventions": 43748, "Ġиногда": 43749, "Ġcreatively": 43750, "Ġbenchmarks": 43751, "oncé": 43752, "alal": 43753, "Ġsotto": 43754, "Ġcalves": 43755, "ĠMov": 43756, "Ġlavender": 43757, "Ġeyeballs": 43758, "Ġawaiting": 43759, "ĠPaty": 43760, "ÙĦÙĩ": 43761, "Ġembroidery": 43762, "Ġduh": 43763, "Ġcamar": 43764, "ĠBOB": 43765, "Ġspaced": 43766, "ĠgÅĤos": 43767, "аемÑģÑı": 43768, "Ġescapes": 43769, "ĠRogue": 43770, "zcz": 43771, "èŀ": 43772, "¬ë¥¼": 43773, "ĠMoże": 43774, "ĠеÑģÑĤе": 43775, "ĠBurada": 43776, "éĮ²": 43777, "wd": 43778, "uuuu": 43779, "Ġsash": 43780, "ĠLub": 43781, "Ġnotebooks": 43782, "Ġmae": 43783, "Ġconflicting": 43784, "Ġsummertime": 43785, "acas": 43786, "Ġbauen": 43787, "blowing": 43788, "ạo": 43789, "Ġìĸ¸ìłľ": 43790, "ä»ĬæĹ¥ãģ¯": 43791, "ĠSenhor": 43792, "ĠiPhones": 43793, "ĠQuarter": 43794, "ĠìłľëĮĢë¡ľ": 43795, "uÃŁ": 43796, "Ġë§Ī무ë": 43797, "Ġsettlers": 43798, "Ġcrest": 43799, "Ġtransc": 43800, "æĽ¾": 43801, "Ġriots": 43802, "Ġclones": 43803, "ĠOprah": 43804, "ίζ": 43805, "Ġpals": 43806, ".......": 43807, "ãģĶãģĸãģĦãģ¾ãģĻ": 43808, "ĠÑĢоÑģÑģ": 43809, "ĠLaser": 43810, "Ġzaczy": 43811, "Ġsevi": 43812, "Ġregeneration": 43813, "ìĹ¼": 43814, "would": 43815, "Ġüzerine": 43816, "ĠStraÃŁe": 43817, "Ġvengeance": 43818, "Ġrer": 43819, "ĠSafari": 43820, "ĠHEY": 43821, "çķ«": 43822, "Ġsacar": 43823, "Ġimagem": 43824, "ĠBundest": 43825, "mesan": 43826, "ĠPaste": 43827, "Ġsizz": 43828, "ĠпоÑģÑĤÑĥп": 43829, "×Ķ×ķ": 43830, "trad": 43831, "Ġfrançaise": 43832, "ĠBou": 43833, "Ġbarre": 43834, "ĠZhi": 43835, "ĠGeez": 43836, "ihad": 43837, "Ġreconoc": 43838, "Ġpelig": 43839, "Ġindices": 43840, "Ġë°ĶëĢ": 43841, "Ġconduction": 43842, "Ġìķħ": 43843, "Ġzeker": 43844, "Ġfum": 43845, "ĠWür": 43846, "breaker": 43847, "Ġsprite": 43848, "Crowd": 43849, "Ġopener": 43850, "Ġolv": 43851, "Ġbuenas": 43852, "ĠSilk": 43853, "ĠHIM": 43854, "kop": 43855, "compl": 43856, "Ġpossono": 43857, "³Ģ": 43858, "Ġoscillator": 43859, "ĠSith": 43860, "èĥ¡": 43861, "ажи": 43862, "Ġraft": 43863, "hall": 43864, "Ġschneller": 43865, "Ġimporting": 43866, "Ġassembling": 43867, "Ġubiqu": 43868, "Ġactivates": 43869, "acci": 43870, "ĵľë¥¼": 43871, "Ġcomposers": 43872, "ĠACL": 43873, "Conf": 43874, "Ġì½ĺ": 43875, "ĠнекоÑĤоÑĢÑĭе": 43876, "Ġcandies": 43877, "åĬłåħ¥": 43878, "ĠMuss": 43879, "à¹ĥà¸Ĭ": 43880, "Ġduda": 43881, "ником": 43882, "meden": 43883, "Ġìĸ´ëķĮ": 43884, "ĠYeshua": 43885, "zag": 43886, "hodou": 43887, "Ġaloud": 43888, "ĠPalmer": 43889, "imize": 43890, "ãĤ·ãĥ§": 43891, "Ġmaritime": 43892, "Ġcommunal": 43893, "Ġbadges": 43894, "Ġrugby": 43895, "Ġmarshmallow": 43896, "Ġfiery": 43897, "Ġaccountant": 43898, "Ġabla": 43899, "ĠMonroe": 43900, "ĠFont": 43901, "ĠBoost": 43902, "ĠBarnes": 43903, "answer": 43904, "ĠBurning": 43905, "Ġä¸įæĺ¯": 43906, "Ġangef": 43907, "ĠWesley": 43908, "lls": 43909, "ìµ": 43910, "ש׾": 43911, "iliÅĽmy": 43912, "×IJף": 43913, "amura": 43914, "ĠFuj": 43915, "Ġpani": 43916, "ĠTrop": 43917, "arbeiten": 43918, "Ġrue": 43919, "ĠRare": 43920, "ängen": 43921, "ĠÑģмоÑĤÑĢеÑĤÑĮ": 43922, "ĠÐļаÑĢ": 43923, "ĠMTV": 43924, "boarding": 43925, "][": 43926, "ĠëłĪë": 43927, "stanbul": 43928, "pielt": 43929, "ĠHardy": 43930, "ĠEngagement": 43931, "ĠDienst": 43932, "Ġwären": 43933, "Ġfuego": 43934, "Ġestruct": 43935, "Ġcalam": 43936, "ĠResponse": 43937, "ĠãĤĦ": 43938, "ĠMohammad": 43939, "Ġresisting": 43940, "Ġdurant": 43941, "èģ¯": 43942, "åĨµ": 43943, "ĠOLED": 43944, "Ġverz": 43945, "män": 43946, "ĠÙĨÛĴ": 43947, "Ġparanoid": 43948, "ĠAware": 43949, "ĠEngineers": 43950, "Ġprocedural": 43951, "Ġpersonnage": 43952, "Ġfarklı": 43953, "é¡Ĩ": 43954, "flowing": 43955, "ĠмеÑģÑĤа": 43956, "ĠBare": 43957, "istem": 43958, "ĠpoczÄħtku": 43959, "Ġpersonajes": 43960, "Ġìĸ´ëłµ": 43961, "Ńī": 43962, "ĠХоÑĤÑı": 43963, "Ġunsett": 43964, "ĠAbsol": 43965, "Ġấy": 43966, "ĠMAYOR": 43967, "полне": 43968, "Ġinforming": 43969, "Ġamps": 43970, "ÐŁÑĢ": 43971, "ĠëŃĶ": 43972, "aeda": 43973, "Ġ×Ķ×ij×": 43974, "ấn": 43975, "kelijk": 43976, "Ġatheist": 43977, "Ġtrout": 43978, "Ġneues": 43979, "ĠNokia": 43980, "machen": 43981, "Ġwholesale": 43982, "ırd": 43983, "Ins": 43984, "ĠÑįп": 43985, "Ġprick": 43986, "ĠKindern": 43987, "à¸Ĺำ": 43988, "Ġclassy": 43989, "Ġînt": 43990, "ĠShopify": 43991, "ĠÑģоÑĢ": 43992, "ĠзакÑĢÑĭ": 43993, "zuk": 43994, "Ġuniversally": 43995, "Ġteaspoons": 43996, "Ġrecount": 43997, "ĠnÃ¥gonting": 43998, "ĠXue": 43999, "isième": 44000, "Ġweakest": 44001, "ĠteÅŁekkür": 44002, "Ġmathematically": 44003, "ĠHos": 44004, "Ġíķľëĭ¤": 44005, "Ġpartager": 44006, "ĠDarr": 44007, "êº": 44008, "Ġεκ": 44009, "Ġgerms": 44010, "Ġgelir": 44011, "Ġdul": 44012, ",-": 44013, "Ġìĸ¸ë": 44014, "Ġ×ŀצ": 44015, "ĠÑıÑĢ": 44016, "Ġquotid": 44017, "Ġprzysz": 44018, "Ġhardness": 44019, "Ġaquatic": 44020, "ĠJungle": 44021, "ĠPCR": 44022, "ĠEliot": 44023, "Ġostr": 44024, "Ġmapa": 44025, "essä": 44026, "ĠGIR": 44027, "ĠDriving": 44028, "ĠSami": 44029, "ĠMedien": 44030, "ĠCompanies": 44031, "ĠPharm": 44032, "seits": 44033, "ĠRim": 44034, "ĠοÏĢο": 44035, "Ġweiteren": 44036, "Ġpizzas": 44037, "ĠLydia": 44038, "ĠHeights": 44039, "Ġsincerity": 44040, "Ġnossas": 44041, "ĠdÅĤ": 44042, "Ġalarming": 44043, "ĠCauc": 44044, "ĠÑģмÑĭÑģ": 44045, "facing": 44046, "bags": 44047, "WW": 44048, "ĠØ´ÙĬ": 44049, "Ġcourtroom": 44050, "ĠPhillip": 44051, "Ġê²ĥì²ĺëŁ¼": 44052, "ĠSpieler": 44053, "ãĤıãģĭ": 44054, "Ġkant": 44055, "Ġadmitting": 44056, "ãĥģãĥ£ãĥ³ãĥįãĥ«": 44057, "Ġcontainment": 44058, "å¼ł": 44059, "Ġremovable": 44060, "Ġjumper": 44061, "focused": 44062, "ĠиÑĤоге": 44063, "ĠТем": 44064, "Ġvase": 44065, "ĠUSC": 44066, "ĠMonate": 44067, "ĠJacobs": 44068, "ĠHOL": 44069, "iked": 44070, "erweise": 44071, "Ġgoodies": 44072, "Ġhomage": 44073, "׼ש×Ļ×ķ": 44074, "Ġquais": 44075, "Ġinicial": 44076, "Ġguarding": 44077, "Ġdazz": 44078, "Ġcombos": 44079, "ĠÑĥпÑĢав": 44080, "ĠTalent": 44081, "å¥ĩæĢª": 44082, "Ġór": 44083, "Ġintermittent": 44084, "ĠMcCarthy": 44085, "Ġspans": 44086, "Ġtyre": 44087, "Ġquy": 44088, "èĪĪ": 44089, "jut": 44090, "ĠZent": 44091, "Ġgat": 44092, "大åĵ¥": 44093, "Ġscaffold": 44094, "Ġnecesario": 44095, "ĠZahlen": 44096, "ĠSAND": 44097, "ĠPU": 44098, "Everything": 44099, "----------------": 44100, "ĠвзÑıÑĤÑĮ": 44101, "Ġsparks": 44102, "Ġpendulum": 44103, "×ŀף": 44104, "Ġìĥīê¹": 44105, "Ġmultiplier": 44106, "Ġладно": 44107, "urat": 44108, "Ġupsetting": 44109, "è¡Ģ": 44110, "bak": 44111, "ĠìµľëĮĢ": 44112, "Ġanál": 44113, "ĠJOE": 44114, "Ġkosten": 44115, "ĠPatty": 44116, "ĠGuin": 44117, "cked": 44118, "ĠEgyptians": 44119, "ĠCitizens": 44120, "ר׼": 44121, "ĠÐķÑīе": 44122, "Ġйого": 44123, "Ġsnowfl": 44124, "Ġlekker": 44125, "Ġacost": 44126, "ĠBabe": 44127, "Ġgamble": 44128, "Ġadjective": 44129, "кими": 44130, "oys": 44131, "Ġmontre": 44132, "ĠHyundai": 44133, "Ġmoisturizing": 44134, "Ġmozzarella": 44135, "OOO": 44136, "Ġfacult": 44137, "Ġdoet": 44138, "Ġfearless": 44139, "Ġespresso": 44140, "Ġallora": 44141, "ĠCinc": 44142, "ãĥ¼ãĤ¸": 44143, "Ġconteúdo": 44144, "ĠPelosi": 44145, "Ġminder": 44146, "root": 44147, "Ġíķłë": 44148, "Ġпад": 44149, "ĠCalling": 44150, "ĠConfig": 44151, "ĠConsole": 44152, "insky": 44153, "énergie": 44154, "Ġsolitary": 44155, "оде": 44156, "Ġguarded": 44157, "160": 44158, "ĠпÑģиÑħ": 44159, "ĠShap": 44160, "Ġtitre": 44161, "ologne": 44162, "ĠпаÑĢÑĥ": 44163, "ĠPRE": 44164, "ãĥ¼ãĥī": 44165, "Ġln": 44166, "ĠMitgl": 44167, "ĠCarry": 44168, "Ġspind": 44169, "ĠCanton": 44170, "Ġkingdoms": 44171, "remo": 44172, "Ġraging": 44173, "Ġincapable": 44174, "ĠWR": 44175, "åĨįè§ģ": 44176, "ĠÑģобÑģÑĤвен": 44177, "ĠкакиÑħ": 44178, "ĠSHE": 44179, "ëĭ¹íŀĪ": 44180, "Ġscarcity": 44181, "Ġperde": 44182, "Ġexits": 44183, "ĠSinger": 44184, "Ġsupper": 44185, "Ġmunicipality": 44186, "ĠDiversity": 44187, "Ġtiro": 44188, "iels": 44189, "ĠlÃŃder": 44190, "Ġbluff": 44191, "Ġatra": 44192, "lys": 44193, "Ġmahd": 44194, "Ġcódigo": 44195, "ĠHarlem": 44196, "rule": 44197, "icity": 44198, "Ġsimplistic": 44199, "ĠKonst": 44200, "åģ¥": 44201, "ELLI": 44202, "Ġförsta": 44203, "Ġconstitutes": 44204, "ĠÑģÑĤоÑĢонÑĥ": 44205, "Ġurged": 44206, "ĠPanda": 44207, "ì°¨ë": 44208, "rece": 44209, "Ġpatriot": 44210, "ĠCrush": 44211, "Ġwink": 44212, "ойÑĤи": 44213, "urança": 44214, "Ġseizures": 44215, "Ġelectrod": 44216, "ĠDonkey": 44217, "ĠIU": 44218, "ĠMOS": 44219, "Ġalkal": 44220, "ì´ī": 44221, "besondere": 44222, "Ġparallels": 44223, "Ġbitterness": 44224, "ättre": 44225, "essional": 44226, "Ġsoybean": 44227, "Ġcollab": 44228, "ĠReporting": 44229, "å§Ķ": 44230, "Ġкомпании": 44231, "Ġwszyscy": 44232, "ĠCrunch": 44233, "iseen": 44234, "Ġambassadors": 44235, "ĠChev": 44236, "åįĪ": 44237, "овÑĭе": 44238, "sca": 44239, "ĠÑĢеÑĪил": 44240, "оÑĤо": 44241, "Ġgleichzeitig": 44242, "mern": 44243, "üst": 44244, "ĠHae": 44245, "³´ê²łìĬµëĭĪëĭ¤": 44246, "Ġshores": 44247, "Ġdepress": 44248, "Ġahor": 44249, "ĠSteuer": 44250, "ahh": 44251, "Ġrevise": 44252, "ĠÑģамÑĭе": 44253, "jat": 44254, "Ġherbal": 44255, "Ġcuánt": 44256, "Ġbuna": 44257, "niejsze": 44258, "Finally": 44259, "×ķ×ĸ": 44260, "cje": 44261, "ĠìŀĪê±°ëĵłìļĶ": 44262, "ĠëĤĺëĪ": 44263, "Ġprzest": 44264, "ãĥ¼ãĥł": 44265, "lica": 44266, "ĠDuch": 44267, "å°įå°į": 44268, "ÑĸйÑģÑĮ": 44269, "passen": 44270, "Ġsatisfies": 44271, "ĠAdditional": 44272, "Ġcámara": 44273, "еÑĩение": 44274, "Ġpomp": 44275, "Ġë§IJìĿ´": 44276, "ĠMills": 44277, "евид": 44278, "Ġrespectable": 44279, "Ġfilament": 44280, "Ġvender": 44281, "Ġmattered": 44282, "oure": 44283, "층": 44284, "Korean": 44285, "Ġestudio": 44286, "Ġcactus": 44287, "ĠVive": 44288, "ĠRag": 44289, "Ġcompliqué": 44290, "ĠÙĪÛģ": 44291, "Ġtao": 44292, "¦¿": 44293, "Since": 44294, "Ġjeopard": 44295, "ĠSell": 44296, "åºĶ": 44297, "ĠìĺĽ": 44298, "Ġketo": 44299, "Ġintelig": 44300, "ĠAngeb": 44301, "Ġtiden": 44302, "Ġsocio": 44303, "Ġreminiscent": 44304, "Ġcaregiver": 44305, "Space": 44306, "ĠExercise": 44307, "ĠBecome": 44308, "êts": 44309, "akk": 44310, "!..": 44311, "ĠÑģпÑĢоÑģ": 44312, "ĠαÏĢο": 44313, "Ġshootings": 44314, "Ġape": 44315, "ĠSammy": 44316, "ĠKung": 44317, "Ġcuál": 44318, "ĠLup": 44319, "æĿŁ": 44320, "ä¾ĨåĪ°": 44321, "ĠÑģÑĤÑĥд": 44322, "Ġsweeter": 44323, "Ġcomum": 44324, "ĠAds": 44325, "hyung": 44326, "ĠбÑĥдÑĥÑī": 44327, "Ġwaffle": 44328, "ĠOrb": 44329, "Ġlaut": 44330, "Ġforecasting": 44331, "åª": 44332, "Ġrapping": 44333, "Ġprefers": 44334, "Ġbenz": 44335, "Ġnik": 44336, "ĠBahn": 44337, "Ġsanding": 44338, "Ġimminent": 44339, "ĠпÑĢоблемÑĭ": 44340, "Ġdoivent": 44341, "ола": 44342, "Ġżycia": 44343, "ihu": 44344, "Ġexistem": 44345, "ĠInterior": 44346, "ĠTakes": 44347, "Ġtoddler": 44348, "Ġdictatorship": 44349, "ĠSmithson": 44350, 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"Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "></": 48986, "ĠespÃŃ": 48987, "ĠCOP": 48988, "åŃ©åŃIJ": 48989, "visible": 48990, "ĠпÑĢеÑģÑĤÑĥп": 48991, "Ġstitched": 48992, "à¯Ī.": 48993, "Ġlatent": 48994, "ĠPrab": 48995, "ĠMcN": 48996, "ĠHealing": 48997, "ĠCuriosity": 48998, "cert": 48999, "Ġ민주": 49000, "Ġpatiently": 49001, "ĠYT": 49002, "foreign": 49003, "Ġvẫn": 49004, "Ġindustri": 49005, "Ġcocktails": 49006, "Ġbrighten": 49007, "Ġconsolidated": 49008, "аÑĢд": 49009, "ltry": 49010, "Ġgrille": 49011, "Ġbona": 49012, "Ġdiligently": 49013, "ĠWrestleMania": 49014, "erkt": 49015, "energy": 49016, "999": 49017, "à®ķவ": 49018, "Ġtote": 49019, "iono": 49020, "DIO": 49021, "Ġschizophrenia": 49022, "Ġpostponed": 49023, "ĠQiu": 49024, "ĠÏĥÏħν": 49025, "ĠzdjÄĻ": 49026, "Ġspannend": 49027, "ĠDIS": 49028, "Rel": 49029, "Ġrhin": 49030, "immune": 49031, "Old": 49032, "Ġplötzlich": 49033, "Ġmound": 49034, "Ġastronomical": 49035, "ĠGuid": 49036, "ĠCul": 49037, "HI": 49038, "ĠÅł": 49039, "Ġrepo": 49040, "ĠMaurice": 49041, "ä¸ĢçĤ¹": 49042, "Ġbandits": 49043, "ĠDesktop": 49044, "äss": 49045, "fta": 49046, "Ġlicence": 49047, "Ġimaginar": 49048, "ĠEntreprene": 49049, "xo": 49050, "Ġ맼ìŀĪëĬĶ": 49051, "Ġ×Ķ×ij": 49052, "Ġpumpkins": 49053, "Ġkanssa": 49054, "ĠjÄĻzy": 49055, "Ġcommunauté": 49056, "bür": 49057, "Ġerhö": 49058, "ĠWolver": 49059, "ĠSharing": 49060, "令": 49061, "Ġpakai": 49062, "Ġinsulted": 49063, "ÐľÑĭ": 49064, "оÑĹ": 49065, "Ġconsiste": 49066, "æĮij": 49067, "Ġyoungsters": 49068, "Ġgleichen": 49069, "weder": 49070, "Ġmote": 49071, "Ġclauses": 49072, "état": 49073, "prus": 49074, "Ġwast": 49075, "ç»ĻæĪij": 49076, "ĠCrisp": 49077, "ĠçĦ¶å¾Į": 49078, "Ġoffenders": 49079, "Ġconvection": 49080, "Ġconfian": 49081, "ollow": 49082, "amet": 49083, "ĠÑĹÑħ": 49084, "第äºĮåĢĭ": 49085, "fficiency": 49086, "Ġunglaub": 49087, "igans": 49088, "Ġmarketed": 49089, "ĠVAN": 49090, "Ġproclaimed": 49091, "Ġcélulas": 49092, "Ġcollide": 49093, "ĠOculus": 49094, "adore": 49095, "Ji": 49096, "Ġsustaining": 49097, "ĠFasc": 49098, "Ġsetzt": 49099, "Ġnosaltres": 49100, "Most": 49101, "ĠвÑĩ": 49102, "Ġnauc": 49103, "ĠBhar": 49104, "çĪ¸çĪ¸": 49105, "æĪijè·Łä½łè¬Ľ": 49106, "Ġyêu": 49107, "Ġtimest": 49108, "Ġpertama": 49109, "irmi": 49110, "Ġzwr": 49111, "Ġverbess": 49112, "Ġvortex": 49113, "ĠSTACK": 49114, "ثر": 49115, "¹Ħë": 49116, "ĶĶìĺ¤": 49117, "Ġlinkage": 49118, "ĠFraser": 49119, "enario": 49120, "ĠëĿ¼ëĬĶ": 49121, "ĠìĦłë°°": 49122, "hthal": 49123, "Ġê¹Į": 49124, "ĠKhông": 49125, "Ãĥ": 49126, "Ġscrambled": 49127, "ĠEink": 49128, "Ġmicroorgan": 49129, "Ġnarcissist": 49130, "ĠKombat": 49131, "Ġ맡": 49132, "ĠAGA": 49133, "Ġperfekt": 49134, "ĠSerie": 49135, "determ": 49136, "-'": 49137, "Ġponytail": 49138, "Ġkoska": 49139, "ìĵ": 49140, "Ġobec": 49141, "Ġchests": 49142, "veer": 49143, "Ġuprising": 49144, "Ġstoked": 49145, "associ": 49146, "Ġprodução": 49147, "ĠShape": 49148, "ìłľê°Ģ": 49149, "ĠëĶ°": 49150, "Ġjon": 49151, "Ġinadvert": 49152, "antas": 49153, "ĠнаконеÑĨ": 49154, "Ġå°įåķĬ": 49155, "ĠArsenal": 49156, "Ġproteg": 49157, "Ġliberté": 49158, "Ġglare": 49159, "åĪļ": 49160, "å·²ç»ı": 49161, "Ġverein": 49162, "Ġinserts": 49163, "ĠJana": 49164, "Ġwydaje": 49165, "ÅĤum": 49166, "Ġ%.": 49167, "origine": 49168, "Ġsynagogue": 49169, "Ġfallait": 49170, "Ġdisobed": 49171, "Ġantic": 49172, "ĠCycl": 49173, "Ġasynchronous": 49174, "Ġë²Įìį¨": 49175, "Ġgesund": 49176, "Ġgagn": 49177, "Ġpea": 49178, "Ġgrin": 49179, "ést": 49180, "Ġsauc": 49181, "ĠMäd": 49182, "íķ´ëıĦ": 49183, "pps": 49184, "ĠεÏĢι": 49185, "Ġpeuple": 49186, "Ġdeben": 49187, "ĠBree": 49188, "ĠÑĢолÑĮ": 49189, "Ġкаким": 49190, "Ġútil": 49191, "Ġdistributor": 49192, "алÑĭ": 49193, "ĠswojÄħ": 49194, "Ġfolklore": 49195, "Ġreceivers": 49196, "ĠMOO": 49197, "bins": 49198, "astre": 49199, "ìķĪë": 49200, "ĠëĦ£ê³ł": 49201, "Ġmultimedia": 49202, "Ġgebaut": 49203, "овÑĭÑħ": 49204, "ãy": 49205, "Ġdane": 49206, "okol": 49207, "emitism": 49208, "ONEY": 49209, "ĠyaÄŁ": 49210, "Ġchauff": 49211, "容æĺĵ": 49212, "Ġesfuer": 49213, "Äĥn": 49214, "ertas": 49215, "Ġfonctionne": 49216, "omina": 49217, "Ġivory": 49218, "ĠYoutuber": 49219, "ĠSkywalker": 49220, "иÑĩеÑģкаÑı": 49221, "toi": 49222, "Ġveya": 49223, "Ġgelernt": 49224, "Ġchancellor": 49225, "ĠStatistics": 49226, "Ġwelded": 49227, "Ġondan": 49228, "ĠSei": 49229, "Ġmedically": 49230, "Ġenergized": 49231, "ĠVia": 49232, "Ġвик": 49233, "Ġuninter": 49234, "Ġhighness": 49235, "ĠíĮĶë": 49236, "Ġamplified": 49237, "ĠSergey": 49238, "ĠMins": 49239, "warm": 49240, "pell": 49241, "ophile": 49242, "Ġhè": 49243, "ĠBelo": 49244, "ĠSketch": 49245, "Ġcharacterization": 49246, "ansen": 49247, "ĠÑĤÑĥÑĢ": 49248, "Ġãħĭãħĭãħĭ": 49249, "Note": 49250, "ĠkoÅŁ": 49251, "Ġciert": 49252, "flu": 49253, "Ġbaht": 49254, "ĠDowntown": 49255, "ĠCRIS": 49256, "odie": 49257, "140": 49258, "Ġlitres": 49259, "Ġgriev": 49260, "æ§ĺ": 49261, "ĠìĶ¨ê°Ģ": 49262, "Ġsucceeds": 49263, "Ġ__": 49264, "enting": 49265, "Ġvimos": 49266, "Ġsì": 49267, "defense": 49268, "ĠMcD": 49269, "ĠMarion": 49270, "ĠDont": 49271, "ĠDDR": 49272, "ĠLazar": 49273, "ĠDAR": 49274, "Ġkuv": 49275, "Kn": 49276, "Ġsembla": 49277, "Ġairborne": 49278, "ĠViolence": 49279, "ëIJIJ": 49280, "Ġrestraint": 49281, "Ġwhistles": 49282, "Ġscolded": 49283, "Ġacceso": 49284, "Ġabsolutamente": 49285, "ĠTyl": 49286, "ĠSap": 49287, "¶Ģë¶Ħ": 49288, "itäten": 49289, "adem": 49290, "Ġý": 49291, "Ġprescribe": 49292, "ĠMage": 49293, "ĠHelena": 49294, "å¾Īæľī": 49295, "亲": 49296, "vt": 49297, "Ġvienen": 49298, "Ġsneez": 49299, "Ġmolé": 49300, "Æ°á»Łng": 49301, "Ġtransporting": 49302, "ĠLean": 49303, "Ġkung": 49304, "ÑĥÑĢа": 49305, "ÏĦÎŃ": 49306, "utches": 49307, "onders": 49308, "liyor": 49309, "Nat": 49310, "Ġzij": 49311, "Ġmammal": 49312, "Ġkäyt": 49313, "ĠJoanna": 49314, "sent": 49315, "Ġस": 49316, "Ġvested": 49317, "ĠErfahrung": 49318, "okee": 49319, "Ġclipping": 49320, "ĠListening": 49321, "Ġ(#": 49322, "fö": 49323, "Ġvidare": 49324, "Ġbrittle": 49325, "ĠSTART": 49326, "ĠDamas": 49327, "ĠYog": 49328, "ãĤĵãģ¨": 49329, "gart": 49330, "Ġverlier": 49331, "Ġheartfelt": 49332, "ĠdoÅĽÄĩ": 49333, "ì¹ĺê°Ģ": 49334, ".»": 49335, "Ġmaximal": 49336, "Ġdistintos": 49337, "ĠìĻľëĥIJíķĺë©´": 49338, "Ġsailed": 49339, "Ġconveyed": 49340, "ĠTinder": 49341, "ĠSUPER": 49342, "ниÑĨÑĥ": 49343, "controlled": 49344, "Ġfunz": 49345, "Ġbastards": 49346, "ĠGinsburg": 49347, "Ġnuovo": 49348, "ĠPere": 49349, "ĠJES": 49350, "ĠDingen": 49351, "ĠBets": 49352, "umba": 49353, "acción": 49354, "ĠìŀĪì§Ģë§Į": 49355, "Ġretra": 49356, "ĠLaurent": 49357, "Ġpozy": 49358, "Ġgrooves": 49359, "Ġmáquina": 49360, "Ġminion": 49361, "Ġdeinen": 49362, "ĠShaun": 49363, "×Ļ×Ļ": 49364, "Ġhonorary": 49365, "osaurus": 49366, "Ġzeit": 49367, "Ġespecie": 49368, "ĠBCE": 49369, "аÑĤе": 49370, "Justin": 49371, "ĠWheels": 49372, "ĠìĿ´íķ´": 49373, "ĠبÙĬÙĨ": 49374, "Ġpropulsion": 49375, "Ġperceber": 49376, "ĠNewman": 49377, "å´": 49378, "culosis": 49379, "Mi": 49380, "ĠаккÑĥ": 49381, "Ġmastering": 49382, "Ġläh": 49383, "Ġfists": 49384, "ä»Ķ": 49385, "Ġmarinade": 49386, "Lilly": 49387, "Ġëħ¸ëł¥": 49388, "ĠYH": 49389, "Ġurgently": 49390, "Ġinformational": 49391, "Ġacordo": 49392, "izzy": 49393, "ãģĦãģı": 49394, "ìĿ´ìĸ´": 49395, "imar": 49396, "ĠëĤĺìĺ¤ë": 49397, "Ġtwenties": 49398, "Ġrasp": 49399, "Ġbumpy": 49400, "بة": 49401, "worker": 49402, "Ġquickest": 49403, "Ġattaches": 49404, "виг": 49405, "ĠëĤĺíĥĢë": 49406, "Ġpuree": 49407, "Ġoversized": 49408, "Ġstirred": 49409, "Ġjakim": 49410, "Ġhomicide": 49411, "ãĤĤãģĹ": 49412, "iscilla": 49413, "Ġì±Ļ": 49414, "Ġspeculative": 49415, "Ġassists": 49416, "main": 49417, "jähr": 49418, "indet": 49419, "ĠÅŁur": 49420, "Ġforecasts": 49421, "Ġdiversion": 49422, "Ġtare": 49423, "Ġogl": 49424, "ĠOrganisation": 49425, "ĠChevy": 49426, "Ġbaja": 49427, "andır": 49428, "ĠÙĪÙĦا": 49429, "Ġradiant": 49430, "Ġliaison": 49431, "Ġdemokrat": 49432, "ĠMARC": 49433, "ÏĢοÏħ": 49434, "Ġrunt": 49435, "Ġprécis": 49436, "Ġgeven": 49437, "Ġvéhic": 49438, "ĠJESS": 49439, "STR": 49440, "Ġìĸĺë": 49441, "Ġvisionary": 49442, "Ġburadan": 49443, "ĠãģĤãĤĬ": 49444, "Ġrebirth": 49445, "Ġexhibited": 49446, "ĠMetall": 49447, "olie": 49448, "elyn": 49449, "Ġflavours": 49450, "Ġescrito": 49451, "ĠDelete": 49452, "ĠìķĮìķĺìĸ´": 49453, "ĠÑĥкÑĢаÑĹн": 49454, "Ġinterrupting": 49455, "Ġidentific": 49456, "ĠSuzuki": 49457, "ĠLanding": 49458, "件äºĭæĥħ": 49459, "andi": 49460, "Ġestran": 49461, "Ġcouleur": 49462, "Ġagrad": 49463, "ĠSny": 49464, "Ġà®ĩல": 49465, "Ġander": 49466, "Ġrua": 49467, "Ġprise": 49468, "Ġlaure": 49469, "ĠíĬĢ": 49470, "Ġmoderation": 49471, "Ġerfahren": 49472, "Ġdeconst": 49473, "ĠReese": 49474, "ĠPK": 49475, "etos": 49476, "ãģĵãĤĮãģ§": 49477, "ĠGravity": 49478, "ĠEren": 49479, "Ġoverboard": 49480, "Ġmüsst": 49481, "ĠEmail": 49482, "еÑĢм": 49483, "ydi": 49484, "iÄĻdzy": 49485, "ĠLOU": 49486, "ĠFuÃŁball": 49487, "ĠRD": 49488, "alts": 49489, "ĠìĬ¤íĬ¸ë": 49490, "ĠÐļÑĢаÑģ": 49491, "Ġtelev": 49492, "ĠÑĢо": 49493, "Ġresignation": 49494, "Ġjingle": 49495, "ĠStudien": 49496, "ĠIX": 49497, "ĠSentinel": 49498, "ĠPang": 49499, "éĦ": 49500, "Jake": 49501, "Ġpersonagem": 49502, "Ġmédia": 49503, "ĠChern": 49504, "antically": 49505, "Ġthá»Ŀi": 49506, "Ġparalysis": 49507, "Ġjapanese": 49508, "Ġconex": 49509, "Ġefic": 49510, "Ġunderside": 49511, "Ġneol": 49512, "Ġfian": 49513, "имоÑģÑĤÑĮ": 49514, "Ġquirky": 49515, "Ġpista": 49516, "ĠClement": 49517, "nothing": 49518, "ĠпоеÑħ": 49519, "Ġhorrend": 49520, "Ġconsolidate": 49521, "ploys": 49522, "emaker": 49523, "Jennifer": 49524, "Ġnuméro": 49525, "Ġfamoso": 49526, "ĠNeptune": 49527, "ĠíĸĪìĸ´": 49528, "ĠпÑĢезид": 49529, "Ġsitcom": 49530, "Ġserio": 49531, "Ġmue": 49532, "Ġglands": 49533, "Ġbörjar": 49534, "ĠYJ": 49535, "ĠRiot": 49536, "paragus": 49537, "Ġsegurança": 49538, "Ġimmature": 49539, "ĠMadonna": 49540, "à¸į": 49541, "Ġlingering": 49542, "Ġacesso": 49543, "ĠOrient": 49544, "ĠRecomm": 49545, "Ġcomplac": 49546, "founded": 49547, "attend": 49548, "Ġcielo": 49549, "ĠZhan": 49550, "naires": 49551, "cco": 49552, "Ġ×IJ׳": 49553, "Ġstata": 49554, "Ġcontradictory": 49555, "ĠSé": 49556, "ĠSAN": 49557, "ĠConnie": 49558, "Ġëĭ¹ìĭľ": 49559, "ĠÑģамой": 49560, "Ġmajestic": 49561, "ĠPenguin": 49562, "ĠCOME": 49563, "ÃŃcios": 49564, "pero": 49565, "Ġmg": 49566, "Ġfauc": 49567, "Ġcorrer": 49568, "ĠGottes": 49569, "ĠAnglo": 49570, "Har": 49571, "á»Ĺi": 49572, "Ġvitesse": 49573, "Ġannouncer": 49574, "ĠOmaha": 49575, "kum": 49576, "Ġspared": 49577, "ĠÑĢаза": 49578, "ĠполÑĥÑĩиÑĤÑģÑı": 49579, "Ġtähän": 49580, "Ġпонад": 49581, "Ġpertaining": 49582, "ĠRate": 49583, "iern": 49584, "Gold": 49585, "Ġteste": 49586, "ĠdeÄŁild": 49587, "Ġdamping": 49588, "ĠPartnership": 49589, "zysta": 49590, "geld": 49591, "Ġsmokes": 49592, "ĠMarriage": 49593, "쪽ìĹIJ": 49594, "èħ³": 49595, "isce": 49596, "Ġtryna": 49597, "ĠDirectory": 49598, "ĠëĤĺìĺ¬": 49599, "Ġshameful": 49600, "Ġmentre": 49601, "Ġassigning": 49602, "æĺ¯éĢĻ樣": 49603, "Ġrepertoire": 49604, "Ġobjetos": 49605, "稱": 49606, "Ġunderworld": 49607, "Ġendeavors": 49608, "Ġignite": 49609, "ĠÙĪج": 49610, "Ġexperient": 49611, "ĠÐĹап": 49612, "ĠзаклÑİÑĩ": 49613, "Ġvoltages": 49614, "Ġniego": 49615, "Ġdeficits": 49616, "Ġbuenos": 49617, "ĠSleeping": 49618, "ĠSalem": 49619, "Ġunlocking": 49620, "Ġinteracted": 49621, "Ġentendeu": 49622, "ĠSuperintendent": 49623, "Ġszczegól": 49624, "Ġquas": 49625, "Ġpaling": 49626, "Ġkho": 49627, "بØŃ": 49628, "Ġcolabor": 49629, "ĠпÑĢигоÑĤов": 49630, "Ġmauv": 49631, "ĠJudas": 49632, "ĠAssist": 49633, "ĠÑĤеÑĢÑĢи": 49634, "ĠнаÑģколÑĮко": 49635, "Ġsubsidy": 49636, "ĠEmbassy": 49637, "Ġdagen": 49638, "ĠSanto": 49639, "èĪ¬": 49640, "ש×ķ×ij": 49641, "Ġabruptly": 49642, "ĠAdapt": 49643, "Ġvaak": 49644, "Ġpostal": 49645, "Ġinvestir": 49646, "Ġfiquei": 49647, "Ġdowntime": 49648, "ĠWebb": 49649, "ĠNCAA": 49650, "ĠEstoy": 49651, "олоÑĤ": 49652, "ĠìĤ¬ê±´": 49653, "Ġnationalist": 49654, "ĠKathryn": 49655, "ĠKop": 49656, "éª": 49657, "Sean": 49658, "ONA": 49659, "ĠBj": 49660, "×¢×Ŀ": 49661, "ÃŃb": 49662, "idamente": 49663, "Ġглаза": 49664, "Ġunnie": 49665, "Ġgemaakt": 49666, "ĠINTERVIEWER": 49667, "ĠHaut": 49668, "ίο": 49669, "geois": 49670, "wydd": 49671, "Ġколи": 49672, "Ġtightened": 49673, "Ġplanners": 49674, "Ġherum": 49675, "Ġgörün": 49676, "Ġelectronically": 49677, "Ġceram": 49678, "Ġëĭ¤ìĸijíķľ": 49679, "Ġepilepsy": 49680, "ĠeÄŁ": 49681, "lins": 49682, "ĠShiny": 49683, "æł¡": 49684, "ĠÑģолн": 49685, "Ġmacaron": 49686, "Ġimpacto": 49687, "ĠVegan": 49688, "zeÅĦ": 49689, "ĠRapha": 49690, "ĠPars": 49691, "ĠLEO": 49692, "ãģĬãģ£": 49693, "cü": 49694, "Ġ׾×Ķ×Ļ×ķת": 49695, "Ġähnlich": 49696, "Ġfloss": 49697, "ĠAZ": 49698, "Ġmöchten": 49699, "Ġgrooming": 49700, "Ġgrasses": 49701, "ranch": 49702, "Ġrecibir": 49703, "Ġbouncy": 49704, "ĠHobby": 49705, "Ġviktig": 49706, "Ġbegitu": 49707, "ĠPicasso": 49708, "ĠKush": 49709, "모": 49710, "Ġobstruction": 49711, "Ġë¶ĦìľĦ": 49712, "Ġmicrob": 49713, "ĠWestminster": 49714, "rops": 49715, "dul": 49716, "Ġdevo": 49717, "ĠLehrer": 49718, "ĠAdvisor": 49719, "ucken": 49720, "ĠбÑĥм": 49721, "Ġflattering": 49722, "ĠTruman": 49723, "ĠSempre": 49724, "ĠMcCain": 49725, "ĠHindus": 49726, "Julia": 49727, "Ġwatershed": 49728, "Ġlush": 49729, "ìłĦë": 49730, "Before": 49731, "ĠÐĴÑĤоÑĢ": 49732, "ĠSaaS": 49733, "Ġsitzt": 49734, "Ġbeetle": 49735, "ĠEssential": 49736, "enko": 49737, "ĠëķĮëıĦ": 49738, "Ġrevving": 49739, "Ġpoorer": 49740, "Ġcoerc": 49741, "Ġidee": 49742, "Ġcoû": 49743, "alet": 49744, "Ġzdrow": 49745, "Ġfender": 49746, "growth": 49747, "DING": 49748, "Ġzde": 49749, "ä¸ĬéĿ¢": 49750, "ENTS": 49751, "Ġfacets": 49752, "éļª": 49753, "ushima": 49754, "ĠÅŁeh": 49755, "Ġparasite": 49756, "Ġlapse": 49757, "ĠMeer": 49758, "ĠKund": 49759, "Ġslog": 49760, "Ġbrunch": 49761, "ĠChart": 49762, "arz": 49763, "ĠMUS": 49764, "Ġoffenses": 49765, "Ġinglés": 49766, "Ġfoliage": 49767, "oplan": 49768, "Aut": 49769, "ĠJacqu": 49770, "tak": 49771, "iembre": 49772, "Ġxen": 49773, "Ġnominees": 49774, "Ġbiomedical": 49775, "ésus": 49776, "Ġestuv": 49777, "ÏĦÏĮ": 49778, "ATHAN": 49779, "Ġíķľëį°": 49780, "Ġheed": 49781, "crosstalk": 49782, "Bill": 49783, "Ġspouses": 49784, "ĠÑģÑİж": 49785, "Ġverso": 49786, "ĠSven": 49787, "ĠCau": 49788, "cuz": 49789, "Ġë³´ìĦ¸ìļĶ": 49790, "ĠÑħозÑı": 49791, "Ġmocking": 49792, "ĠOna": 49793, "ĠDá": 49794, "Ġfruitful": 49795, "Ġbanquet": 49796, "udding": 49797, "inctions": 49798, "dert": 49799, "sud": 49800, "Ġdescon": 49801, "ĠJC": 49802, "Ġ§": 49803, "Ġpubli": 49804, "ëĪĪ": 49805, "éģķãģĨ": 49806, "Ġentschieden": 49807, "ĠROI": 49808, "ãģįãģŁ": 49809, "ĠìĥĿê²¼": 49810, "Ġkäytt": 49811, "yani": 49812, "shaw": 49813, "Ġunleash": 49814, "Ġmanne": 49815, "Ġhistogram": 49816, "æĬ¥": 49817, "à¸Ńะà¹Ħร": 49818, "Ġgn": 49819, "Ġfella": 49820, "Ġeinges": 49821, "ĠBuilt": 49822, "Ġrepresenta": 49823, "Ġpunishing": 49824, "Ġoutsiders": 49825, "нÑĥÑĤÑĮÑģÑı": 49826, "current": 49827, "Ġfamiliarity": 49828, "Ġдив": 49829, "Ġprojets": 49830, "Ġaqueles": 49831, "ĠGlue": 49832, "those": 49833, "Ġinception": 49834, "Ġaquellos": 49835, "Ġillusions": 49836, "Ġattends": 49837, "rese": 49838, "Ġswarm": 49839, "Ġswab": 49840, "Ġregardez": 49841, "Ġposição": 49842, "Ġakhir": 49843, "Ġextracting": 49844, "Ġanecdote": 49845, "ĠTale": 49846, "Ġвин": 49847, "Ġabges": 49848, "ĠoluÅŁ": 49849, "Ġcomplicado": 49850, "Ġcovari": 49851, "ÑĸÑĤÑĮ": 49852, "Der": 49853, "Ġ×Ļ×Ķ": 49854, "Form": 49855, "Ġìĸ´ì¨Įëĵł": 49856, "Ġreadable": 49857, "Ġinhibit": 49858, "Ġdecipher": 49859, "ĠAngry": 49860, "pg": 49861, "வத": 49862, "ĠÑģобÑģÑĤвенно": 49863, "Ġsamh": 49864, "Ġescr": 49865, "Ġencompasses": 49866, "Ġauster": 49867, "Ġconfisc": 49868, "ĠMandal": 49869, "Ġ}": 49870, "atcher": 49871, "=#": 49872, "çļĦæŶåĢĻ": 49873, "Ġкино": 49874, "Ġstal": 49875, "lungs": 49876, "Ġvole": 49877, "Ġrequis": 49878, "ĠãĤĪ": 49879, "Ġpén": 49880, "Ġlecturer": 49881, "Ġinscription": 49882, "Ġcervical": 49883, "ĠTreasure": 49884, "ĠJW": 49885, "comings": 49886, "Ġeyesight": 49887, "ĠTails": 49888, "ÃŃsimo": 49889, "Ġworksheet": 49890, "Ġswiftly": 49891, "Ġconos": 49892, "Ġeliminates": 49893, "ĠBlaze": 49894, "алог": 49895, "Ġpictured": 49896, "Ġgiraffe": 49897, "ĠLogic": 49898, "åĺī": 49899, "Ġenrichment": 49900, "Fit": 49901, "Ġunintended": 49902, "Ġpersecuted": 49903, "akap": 49904, "ë°ĺ": 49905, "Ġbarber": 49906, "Ġarbeitet": 49907, "ĠSurprisingly": 49908, "ĠAutob": 49909, "unku": 49910, "prov": 49911, "ĠLoch": 49912, "obyl": 49913, "ĠподгоÑĤов": 49914, "Ġéconomique": 49915, "Ġpatt": 49916, "Ġceased": 49917, "ĠÑģпиÑģ": 49918, "Ġnuclei": 49919, "Ġiste": 49920, "ĠWag": 49921, "ĠzupeÅĤnie": 49922, "Ġproverb": 49923, "ĠAhÃŃ": 49924, "åĽŀåİ»": 49925, "liamo": 49926, "Ġreliably": 49927, "Ġpik": 49928, "ĠTrading": 49929, "ĠColeman": 49930, "Ġανα": 49931, "Ġmagari": 49932, "ĠPHIL": 49933, "Ġshedding": 49934, "ohner": 49935, "Ġpornography": 49936, "Ġbeneficiaries": 49937, "âĢ¢": 49938, "enin": 49939, "Ġresolving": 49940, "ĠÑģпоÑĢÑĤ": 49941, "Ġбег": 49942, "Ġnectar": 49943, "ultura": 49944, "imsical": 49945, "ĮĢ를": 49946, "å¹´åīį": 49947, "ãģĹãĤĥ": 49948, "Ġvisão": 49949, "éģİä¾Ĩ": 49950, "ÿÿÿÿÿÿÿÿ": 49951, "attform": 49952, "Ġë§ŀëĬĶ": 49953, "Ġpilgrimage": 49954, "Ġmating": 49955, "ĠReaper": 49956, "ĠBref": 49957, "çĶŁæ´»": 49958, "Ġ×ij×ĵ": 49959, "Ġnovamente": 49960, "Ġgrilling": 49961, "ĠWireless": 49962, "ĠRomanian": 49963, "ÒĽ": 49964, "ìľłë": 49965, "hait": 49966, "ĠBora": 49967, "ARRY": 49968, "Ġhypotheses": 49969, "马": 49970, "ikut": 49971, "ĠìķĦë²Ħ": 49972, "ĠÑĸз": 49973, "Ġnationale": 49974, "تÙī": 49975, "üllt": 49976, "Ġéléments": 49977, "ĠWare": 49978, "Ġ(-": 49979, "алÑĮном": 49980, "Ġindict": 49981, "ĠStones": 49982, "ãģŁãĤģ": 49983, "explosion": 49984, "ĠëĥĦìĥĪ": 49985, "Ġfelic": 49986, "Ġjudiciary": 49987, "Ġincarnation": 49988, "Ġinning": 49989, "Ġformul": 49990, "Ġshipment": 49991, "Ġreindeer": 49992, "æĴŃ": 49993, "ĠознаÑĩ": 49994, "Ġenvol": 49995, "undy": 49996, "ĠзнаÑĤÑĮ": 49997, "Ġвидели": 49998, "Ġexcluding": 49999, "death": 50000, "Ġberm": 50001, "Ġsoprattutto": 50002, "Ġdebido": 50003, "ĠZig": 50004, "ĠOv": 50005, "ĠKEVIN": 50006, "ĠPale": 50007, "ĠMire": 50008, "Ġandar": 50009, "including": 50010, "Ġswapped": 50011, "Ġmisconceptions": 50012, "Ġspong": 50013, "réal": 50014, "Ġorbitals": 50015, "Ġhashtags": 50016, "orit": 50017, "Ġmauvais": 50018, "иÑģа": 50019, "Ġlivres": 50020, "ĠIPS": 50021, "Ġ04": 50022, "ög": 50023, "instr": 50024, "ĠвнеÑĪ": 50025, "Ġhice": 50026, "isée": 50027, "Ġowes": 50028, "Ġesimerk": 50029, "ĠUH": 50030, "Ġirritation": 50031, "Ġgiggles": 50032, "Ġcolonialism": 50033, "ĠBliss": 50034, "strings": 50035, "Ġreunited": 50036, "ĠPsaki": 50037, "wach": 50038, "Ġcliffs": 50039, "ĠFalse": 50040, "äg": 50041, "pipe": 50042, "Ġwhopping": 50043, "Ġmeringue": 50044, "Ġbung": 50045, "industrie": 50046, "Ġleche": 50047, "ĠLoy": 50048, "Ġdrie": 50049, "Ġpassat": 50050, "Ġoleh": 50051, "Ġcéu": 50052, "ĠGabrie": 50053, "Ġreefs": 50054, "Ġbombers": 50055, "Ġepisódio": 50056, "ĠRug": 50057, "ĠProse": 50058, "onos": 50059, "Ġobese": 50060, "Ġgoog": 50061, "Ġpiace": 50062, "flanzen": 50063, "éĴŁ": 50064, "Ġflaps": 50065, "ĠAlto": 50066, "é£Łãģ¹": 50067, "Fin": 50068, "Ġresize": 50069, "ê·¸ëŀ¨": 50070, "è²»": 50071, "Nathan": 50072, "ŀĪ볤": 50073, "ĠÑĤай": 50074, "ĠNFT": 50075, "Ġsneeze": 50076, "Ġshroud": 50077, "ié": 50078, "Ġveramente": 50079, "Ġcascade": 50080, "ĠOok": 50081, "ìĹĨìĿ´": 50082, "Ġinfused": 50083, "fps": 50084, "center": 50085, "Ġgrappling": 50086, "ĠWohnung": 50087, "ĠTumb": 50088, "ĠImma": 50089, "ĠDuygusal": 50090, "енÑĤи": 50091, "Ġstewardship": 50092, "Ġharp": 50093, "Ġendorsed": 50094, "ılan": 50095, "Ġодним": 50096, "Ġcompetency": 50097, "Ġbert": 50098, "ĠTales": 50099, "Ġrhe": 50100, "Ġohh": 50101, "Ġê°Ħëĭ¨": 50102, "ĠmRNA": 50103, "Ġgangster": 50104, "ĠRunner": 50105, "еннÑĭм": 50106, "phoria": 50107, "ĠwÅĤaÅĽciwie": 50108, "Ġquarto": 50109, "Ġorganise": 50110, "ĠVet": 50111, "Pad": 50112, "ĠÙħØ«": 50113, "Ġstinks": 50114, "ĠDul": 50115, "uem": 50116, "isiej": 50117, "Top": 50118, "Ġtussen": 50119, "ĠEfendimiz": 50120, "ĠBoule": 50121, "ĠSloven": 50122, "ĠLö": 50123, "Ñijз": 50124, "ÑĢип": 50125, "cave": 50126, "Ġboî": 50127, "Ġapologise": 50128, "ĠMarly": 50129, "ĠExport": 50130, "ĠCaitlin": 50131, "Ġtavalla": 50132, "Ġentails": 50133, "Ġbrom": 50134, "ĠCopenh": 50135, "Ġwalnut": 50136, "Ġinsists": 50137, "Ġcuá»Ļc": 50138, "ĠQuit": 50139, "ĠDevice": 50140, "×Ĵ×Ŀ": 50141, "ĠDOT": 50142, "Ġvelocidad": 50143, "LIE": 50144, "Cool": 50145, "Ġsanitation": 50146, "Ġolho": 50147, "ĠEB": 50148, "ĠíĻķìĭ¤íŀĪ": 50149, "ĠÐľÐ¸Ñħ": 50150, "Ġzuk": 50151, "Ġsurname": 50152, "ĠSchuld": 50153, "ruff": 50154, "cultural": 50155, "ĠÑģÑĤолÑĮко": 50156, "æĻļä¸Ĭ": 50157, "Įëį°": 50158, "Ġtorto": 50159, "Ġbackups": 50160, "ÑĢий": 50161, "relax": 50162, "Ġsynergy": 50163, "Ġbuffs": 50164, "Ġapo": 50165, "ĠWellness": 50166, "rounded": 50167, "Ġuniverses": 50168, "Ġfera": 50169, "Ġstandby": 50170, "ĠSilva": 50171, "ĠJI": 50172, "ensored": 50173, "ĠìĹĨëĭ¤": 50174, "ĠÐIJв": 50175, "ĠоÑĤдел": 50176, "Ġfø": 50177, "ĠRockef": 50178, "ĠCompass": 50179, "ĠBears": 50180, "Ġä¸įè¦ģ": 50181, "Turn": 50182, "Ġthá»±c": 50183, "Ġpossibile": 50184, "Ġestem": 50185, "ĠCroatia": 50186, "Ġtätä": 50187, "ĠCAL": 50188, "à¹Ģà¸ŀ": 50189, "ĠÑģÑĤÑĢаÑħ": 50190, "Ġsalts": 50191, "Ġminimalist": 50192, "Ġincorporates": 50193, "ĠÙĨÛģÛĮÚº": 50194, "acao": 50195, "Ġslammed": 50196, "Ġcama": 50197, "Text": 50198, "!!!!!!": 50199, "Ġalcanz": 50200, "éma": 50201, "Ġincense": 50202, "Ġharden": 50203, "Ġgranting": 50204, "ĠNai": 50205, "ĠFirma": 50206, "Ġhypoc": 50207, "job": 50208, "ĠRH": 50209, "zur": 50210, "илÑı": 50211, "Ġź": 50212, "Ġdares": 50213, "anh": 50214, "Ġë§Įíģ¼": 50215, "Ġcuestión": 50216, "ĠLima": 50217, "æĻ¯": 50218, "Ġassunto": 50219, "ĠIPO": 50220, "ĠBengal": 50221, "ĠBier": 50222, "Ġpsyche": 50223, "Ġacquainted": 50224, "ĠGün": 50225, "ози": 50226, "ÅĽciÄħ": 50227, "AG": 50228, "Ġmalfunction": 50229, "Ġasteroids": 50230, "irez": 50231, "amorph": 50232, "ĠÑģоÑĤÑĢÑĥд": 50233, "Ġfreshwater": 50234, "Ġarran": 50235, "ĠпÑĢÑĭ": 50236, "ног": 50237, "Ġdiabetic": 50238, "ĠÙĤاÙĦ": 50239, "Ġoppress": 50240, "Ġcapacitance": 50241, "performance": 50242, "crates": 50243, "Ġapostle": 50244, "ĠJEN": 50245, "OULD": 50246, "Intro": 50247, "Ġstalls": 50248, "ĠABOUT": 50249, "cticamente": 50250, "Ġdiligent": 50251, "Ġmanifests": 50252, "ĠPakistani": 50253, "Ġ('": 50254, "åľº": 50255, "": 50256}
\ No newline at end of file
diff --git a/whisperx/audio.py b/whisperx/audio.py
deleted file mode 100644
index b6c7e83..0000000
--- a/whisperx/audio.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Union
-
-import ffmpeg
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from .utils import exact_div
-
-# hard-coded audio hyperparameters
-SAMPLE_RATE = 16000
-N_FFT = 400
-N_MELS = 80
-HOP_LENGTH = 160
-CHUNK_LENGTH = 30
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000: number of samples in a chunk
-N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)  # 3000: number of frames in a mel spectrogram input
-
-
-def load_audio(file: str, sr: int = SAMPLE_RATE):
-    """
-    Open an audio file and read as mono waveform, resampling as necessary
-
-    Parameters
-    ----------
-    file: str
-        The audio file to open
-
-    sr: int
-        The sample rate to resample the audio if necessary
-
-    Returns
-    -------
-    A NumPy array containing the audio waveform, in float32 dtype.
-    """
-    try:
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        out, _ = (
-            ffmpeg.input(file, threads=0)
-            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
-            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
-        )
-    except ffmpeg.Error as e:
-        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-
-    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
-def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
-    """
-    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
-    """
-    if torch.is_tensor(array):
-        if array.shape[axis] > length:
-            array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
-
-        if array.shape[axis] < length:
-            pad_widths = [(0, 0)] * array.ndim
-            pad_widths[axis] = (0, length - array.shape[axis])
-            array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
-    else:
-        if array.shape[axis] > length:
-            array = array.take(indices=range(length), axis=axis)
-
-        if array.shape[axis] < length:
-            pad_widths = [(0, 0)] * array.ndim
-            pad_widths[axis] = (0, length - array.shape[axis])
-            array = np.pad(array, pad_widths)
-
-    return array
-
-
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
-    """
-    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
-    Allows decoupling librosa dependency; saved using:
-
-        np.savez_compressed(
-            "mel_filters.npz",
-            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
-        )
-    """
-    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
-    with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f:
-        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
-
-
-def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
-    """
-    Compute the log-Mel spectrogram of
-
-    Parameters
-    ----------
-    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
-        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
-
-    n_mels: int
-        The number of Mel-frequency filters, only 80 is supported
-
-    Returns
-    -------
-    torch.Tensor, shape = (80, n_frames)
-        A Tensor that contains the Mel spectrogram
-    """
-    if not torch.is_tensor(audio):
-        if isinstance(audio, str):
-            audio = load_audio(audio)
-        audio = torch.from_numpy(audio)
-
-    window = torch.hann_window(N_FFT).to(audio.device)
-    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
-    magnitudes = stft[..., :-1].abs() ** 2
-
-    filters = mel_filters(audio.device, n_mels)
-    mel_spec = filters @ magnitudes
-
-    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
-    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
-    log_spec = (log_spec + 4.0) / 4.0
-    return log_spec
\ No newline at end of file
diff --git a/whisperx/decoding.py b/whisperx/decoding.py
deleted file mode 100644
index ca608ca..0000000
--- a/whisperx/decoding.py
+++ /dev/null
@@ -1,716 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import Tensor
-from torch.distributions import Categorical
-
-from .audio import CHUNK_LENGTH
-from .tokenizer import Tokenizer, get_tokenizer
-from .utils import compression_ratio
-
-if TYPE_CHECKING:
-    from .model import Whisper
-
-
-@torch.no_grad()
-def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
-    """
-    Detect the spoken language in the audio, and return them as list of strings, along with the ids
-    of the most probable language tokens and the probability distribution over all language tokens.
-    This is performed outside the main decode loop in order to not interfere with kv-caching.
-
-    Returns
-    -------
-    language_tokens : Tensor, shape = (n_audio,)
-        ids of the most probable language tokens, which appears after the startoftranscript token.
-    language_probs : List[Dict[str, float]], length = n_audio
-        list of dictionaries containing the probability distribution over all languages.
-    """
-    if tokenizer is None:
-        tokenizer = get_tokenizer(model.is_multilingual)
-    if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
-        raise ValueError(f"This model doesn't have language tokens so it can't perform lang id")
-
-    single = mel.ndim == 2
-    if single:
-        mel = mel.unsqueeze(0)
-
-    # skip encoder forward pass if already-encoded audio features were given
-    if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
-        mel = model.encoder(mel)
-
-    # forward pass using a single token, startoftranscript
-    n_audio = mel.shape[0]
-    x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device)  # [n_audio, 1]
-    logits = model.logits(x, mel)[:, 0]
-
-    # collect detected languages; suppress all non-language tokens
-    mask = torch.ones(logits.shape[-1], dtype=torch.bool)
-    mask[list(tokenizer.all_language_tokens)] = False
-    logits[:, mask] = -np.inf
-    language_tokens = logits.argmax(dim=-1)
-    language_token_probs = logits.softmax(dim=-1).cpu()
-    language_probs = [
-        {
-            c: language_token_probs[i, j].item()
-            for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
-        }
-        for i in range(n_audio)
-    ]
-
-    if single:
-        language_tokens = language_tokens[0]
-        language_probs = language_probs[0]
-
-    return language_tokens, language_probs
-
-
-@dataclass(frozen=True)
-class DecodingOptions:
-    task: str = "transcribe"  # whether to perform X->X "transcribe" or X->English "translate"
-    language: Optional[str] = None  # language that the audio is in; uses detected language if None
-
-    # sampling-related options
-    temperature: float = 0.0
-    sample_len: Optional[int] = None  # maximum number of tokens to sample
-    best_of: Optional[int] = None     # number of independent samples to collect, when t > 0
-    beam_size: Optional[int] = None   # number of beams in beam search, when t == 0
-    patience: Optional[float] = None  # patience in beam search (https://arxiv.org/abs/2204.05424)
-
-    # options for ranking generations (either beams or best-of-N samples)
-    length_penalty: Optional[float] = None   # "alpha" in Google NMT, None defaults to length norm
-
-    # prompt, prefix, and token suppression
-    prompt: Optional[Union[str, List[int]]] = None   # text or tokens for the previous context
-    prefix: Optional[Union[str, List[int]]] = None   # text or tokens to prefix the current context
-    suppress_blank: bool = True                      # this will suppress blank outputs
-
-    # list of tokens ids (or comma-separated token ids) to suppress
-    # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
-    suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
-
-    # timestamp sampling options
-    without_timestamps: bool = False              # use <|notimestamps|> to sample text tokens only
-    max_initial_timestamp: Optional[float] = 1.0  # the initial timestamp cannot be later than this
-
-    # implementation details
-    fp16: bool = True  # use fp16 for most of the calculation
-
-
-@dataclass(frozen=True)
-class DecodingResult:
-    audio_features: Tensor
-    language: str
-    language_probs: Optional[Dict[str, float]] = None
-    tokens: List[int] = field(default_factory=list)
-    text: str = ""
-    avg_logprob: float = np.nan
-    no_speech_prob: float = np.nan
-    temperature: float = np.nan
-    compression_ratio: float = np.nan
-
-
-class Inference:
-    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
-        """Perform a forward pass on the decoder and return per-token logits"""
-        raise NotImplementedError
-
-    def rearrange_kv_cache(self, source_indices) -> None:
-        """Update the key-value cache according to the updated beams"""
-        raise NotImplementedError
-
-    def cleanup_caching(self) -> None:
-        """Clean up any resources or hooks after decoding is finished"""
-        pass
-
-
-class PyTorchInference(Inference):
-    def __init__(self, model: "Whisper", initial_token_length: int):
-        self.model: "Whisper" = model
-        self.initial_token_length = initial_token_length
-        self.kv_cache = {}
-        self.hooks = []
-
-    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
-        if not self.kv_cache:
-            self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
-
-        if tokens.shape[-1] > self.initial_token_length:
-            # only need to use the last token except in the first forward pass
-            tokens = tokens[:, -1:]
-
-        return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache)
-
-    def cleanup_caching(self):
-        for hook in self.hooks:
-            hook.remove()
-
-        self.kv_cache = {}
-        self.hooks = []
-
-    def rearrange_kv_cache(self, source_indices):
-        for module, tensor in self.kv_cache.items():
-            # update the key/value cache to contain the selected sequences
-            self.kv_cache[module] = tensor[source_indices].detach()
-
-
-class SequenceRanker:
-    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]:
-        """
-        Given a list of groups of samples and their cumulative log probabilities,
-        return the indices of the samples in each group to select as the final result
-        """
-        raise NotImplementedError
-
-
-class MaximumLikelihoodRanker(SequenceRanker):
-    """
-    Select the sample with the highest log probabilities, penalized using either
-    a simple length normalization or Google NMT paper's length penalty
-    """
-
-    def __init__(self, length_penalty: Optional[float]):
-        self.length_penalty = length_penalty
-
-    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
-        def scores(logprobs, lengths):
-            result = []
-            for logprob, length in zip(logprobs, lengths):
-                if self.length_penalty is None:
-                    penalty = length
-                else:
-                    # from the Google NMT paper
-                    penalty = ((5 + length) / 6) ** self.length_penalty
-                result.append(logprob / penalty)
-            return result
-
-        # get the sequence with the highest score
-        lengths = [[len(t) for t in s] for s in tokens]
-        return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
-
-
-class TokenDecoder:
-    def reset(self):
-        """Initialize any stateful variables for decoding a new sequence"""
-
-    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
-        """Specify how to select the next token, based on the current trace and logits
-
-        Parameters
-        ----------
-        tokens : Tensor, shape = (n_batch, current_sequence_length)
-            all tokens in the context so far, including the prefix and sot_sequence tokens
-
-        logits : Tensor, shape = (n_batch, vocab_size)
-            per-token logits of the probability distribution at the current step
-
-        sum_logprobs : Tensor, shape = (n_batch)
-            cumulative log probabilities for each sequence
-
-        Returns
-        -------
-        tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
-            the tokens, appended with the selected next token
-
-        completed : bool
-            True if all sequences has reached the end of text
-
-        """
-        raise NotImplementedError
-
-    def finalize(
-        self, tokens: Tensor, sum_logprobs: Tensor
-    ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
-        """Finalize search and return the final candidate sequences
-
-        Parameters
-        ----------
-        tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
-            all tokens in the context so far, including the prefix and sot_sequence
-
-        sum_logprobs : Tensor, shape = (n_audio, n_group)
-            cumulative log probabilities for each sequence
-
-        Returns
-        -------
-        tokens : Sequence[Sequence[Tensor]], length = n_audio
-            sequence of Tensors containing candidate token sequences, for each audio input
-
-        sum_logprobs : List[List[float]], length = n_audio
-            sequence of cumulative log probabilities corresponding to the above
-
-        """
-        raise NotImplementedError
-
-
-class GreedyDecoder(TokenDecoder):
-    def __init__(self, temperature: float, eot: int):
-        self.temperature = temperature
-        self.eot = eot
-
-    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
-        temperature = self.temperature
-        if temperature == 0:
-            next_tokens = logits.argmax(dim=-1)
-        else:
-            next_tokens = Categorical(logits=logits / temperature).sample()
-
-        logprobs = F.log_softmax(logits.float(), dim=-1)
-        current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
-        sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
-
-        next_tokens[tokens[:, -1] == self.eot] = self.eot
-        tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
-
-        completed = (tokens[:, -1] == self.eot).all()
-        return tokens, completed
-
-    def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
-        # make sure each sequence has at least one EOT token at the end
-        tokens = F.pad(tokens, (0, 1), value=self.eot)
-        return tokens, sum_logprobs.tolist()
-
-
-class BeamSearchDecoder(TokenDecoder):
-    def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None):
-        self.beam_size = beam_size
-        self.eot = eot
-        self.inference = inference
-        self.patience = patience or 1.0
-        self.max_candidates: int = round(beam_size * self.patience)
-        self.finished_sequences = None
-
-        assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
-
-    def reset(self):
-        self.finished_sequences = None
-
-    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
-        if tokens.shape[0] % self.beam_size != 0:
-            raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
-
-        n_audio = tokens.shape[0] // self.beam_size
-        if self.finished_sequences is None:  # for the first update
-            self.finished_sequences = [{} for _ in range(n_audio)]
-
-        logprobs = F.log_softmax(logits.float(), dim=-1)
-        next_tokens, source_indices, finished_sequences = [], [], []
-        for i in range(n_audio):
-            scores, sources, finished = {}, {}, {}
-
-            # STEP 1: calculate the cumulative log probabilities for possible candidates
-            for j in range(self.beam_size):
-                idx = i * self.beam_size + j
-                prefix = tokens[idx].tolist()
-                for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
-                    new_logprob = (sum_logprobs[idx] + logprob).item()
-                    sequence = tuple(prefix + [token.item()])
-                    scores[sequence] = new_logprob
-                    sources[sequence] = idx
-
-            # STEP 2: rank the candidates and keep the top beam_size sequences for each audio
-            saved = 0
-            for sequence in sorted(scores, key=scores.get, reverse=True):
-                if sequence[-1] == self.eot:
-                    finished[sequence] = scores[sequence]
-                else:
-                    sum_logprobs[len(next_tokens)] = scores[sequence]
-                    next_tokens.append(sequence)
-                    source_indices.append(sources[sequence])
-
-                    saved += 1
-                    if saved == self.beam_size:
-                        break
-
-            finished_sequences.append(finished)
-
-        tokens = torch.tensor(next_tokens, device=tokens.device)
-        self.inference.rearrange_kv_cache(source_indices)
-
-        # add newly finished sequences to self.finished_sequences
-        assert len(self.finished_sequences) == len(finished_sequences)
-        for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
-            for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
-                if len(previously_finished) >= self.max_candidates:
-                    break  # the candidate list is full
-                previously_finished[seq] = newly_finished[seq]
-
-        # mark as completed if all audio has enough number of samples
-        completed = all(
-            len(sequences) >= self.max_candidates for sequences in self.finished_sequences
-        )
-        return tokens, completed
-
-    def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
-        # collect all finished sequences, including patience, and add unfinished ones if not enough
-        sum_logprobs = sum_logprobs.cpu()
-        for i, sequences in enumerate(self.finished_sequences):
-            if len(sequences) < self.beam_size:  # when not enough sequences are finished
-                for j in list(np.argsort(sum_logprobs[i]))[::-1]:
-                    sequence = preceding_tokens[i, j].tolist() + [self.eot]
-                    sequences[tuple(sequence)] = sum_logprobs[i][j].item()
-                    if len(sequences) >= self.beam_size:
-                        break
-
-        tokens: List[List[Tensor]] = [
-            [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences
-        ]
-        sum_logprobs: List[List[float]] = [
-            list(sequences.values()) for sequences in self.finished_sequences
-        ]
-        return tokens, sum_logprobs
-
-
-class LogitFilter:
-    def apply(self, logits: Tensor, tokens: Tensor) -> None:
-        """Apply any filtering or masking to logits in-place
-
-        Parameters
-        ----------
-        logits : Tensor, shape = (n_batch, vocab_size)
-            per-token logits of the probability distribution at the current step
-
-        tokens : Tensor, shape = (n_batch, current_sequence_length)
-            all tokens in the context so far, including the prefix and sot_sequence tokens
-
-        """
-        raise NotImplementedError
-
-
-class SuppressBlank(LogitFilter):
-    def __init__(self, tokenizer: Tokenizer, sample_begin: int):
-        self.tokenizer = tokenizer
-        self.sample_begin = sample_begin
-
-    def apply(self, logits: Tensor, tokens: Tensor):
-        if tokens.shape[1] == self.sample_begin:
-            logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
-
-
-class SuppressTokens(LogitFilter):
-    def __init__(self, suppress_tokens: Sequence[int]):
-        self.suppress_tokens = list(suppress_tokens)
-
-    def apply(self, logits: Tensor, tokens: Tensor):
-        logits[:, self.suppress_tokens] = -np.inf
-
-
-class ApplyTimestampRules(LogitFilter):
-    def __init__(
-        self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int]
-    ):
-        self.tokenizer = tokenizer
-        self.sample_begin = sample_begin
-        self.max_initial_timestamp_index = max_initial_timestamp_index
-
-    def apply(self, logits: Tensor, tokens: Tensor):
-        # suppress <|notimestamps|> which is handled by without_timestamps
-        if self.tokenizer.no_timestamps is not None:
-            logits[:, self.tokenizer.no_timestamps] = -np.inf
-
-        # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
-        for k in range(tokens.shape[0]):
-            sampled_tokens = tokens[k, self.sample_begin :]
-            seq = [t for t in sampled_tokens.tolist()]
-            last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
-            penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
-
-            if last_was_timestamp:
-                if penultimate_was_timestamp:  # has to be non-timestamp
-                    logits[k, self.tokenizer.timestamp_begin :] = -np.inf
-                else:  # cannot be normal text tokens
-                    logits[k, : self.tokenizer.eot] = -np.inf
-                    
-            timestamps = sampled_tokens[sampled_tokens.ge(self.tokenizer.timestamp_begin)]
-            if timestamps.numel() > 0:
-                # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last
-                logits[k, self.tokenizer.timestamp_begin : timestamps[-1]] = -np.inf
-
-        if tokens.shape[1] == self.sample_begin:
-            # suppress generating non-timestamp tokens at the beginning
-            logits[:, : self.tokenizer.timestamp_begin] = -np.inf
-
-            # apply the `max_initial_timestamp` option
-            if self.max_initial_timestamp_index is not None:
-                last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
-                logits[:, last_allowed + 1 :] = -np.inf
-
-        # if sum of probability over timestamps is above any other token, sample timestamp
-        logprobs = F.log_softmax(logits.float(), dim=-1)
-        for k in range(tokens.shape[0]):
-            timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
-            max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
-            if timestamp_logprob > max_text_token_logprob:
-                logits[k, : self.tokenizer.timestamp_begin] = -np.inf
-
-
-class DecodingTask:
-    inference: Inference
-    sequence_ranker: SequenceRanker
-    decoder: TokenDecoder
-    logit_filters: List[LogitFilter]
-
-    def __init__(self, model: "Whisper", options: DecodingOptions):
-        self.model = model
-
-        language = options.language or "en"
-        tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task)
-        self.tokenizer: Tokenizer = tokenizer
-        self.options: DecodingOptions = self._verify_options(options)
-
-        self.n_group: int = options.beam_size or options.best_of or 1
-        self.n_ctx: int = model.dims.n_text_ctx
-        self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
-
-        self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
-        if self.options.without_timestamps:
-            self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
-
-        self.initial_tokens: Tuple[int] = self._get_initial_tokens()
-        self.sample_begin: int = len(self.initial_tokens)
-        self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
-
-        # inference: implements the forward pass through the decoder, including kv caching
-        self.inference = PyTorchInference(model, len(self.initial_tokens))
-
-        # sequence ranker: implements how to rank a group of sampled sequences
-        self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
-
-        # decoder: implements how to select the next tokens, given the autoregressive distribution
-        if options.beam_size is not None:
-            self.decoder = BeamSearchDecoder(
-                options.beam_size, tokenizer.eot, self.inference, options.patience
-            )
-        else:
-            self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
-
-        # logit filters: applies various rules to suppress or penalize certain tokens
-        self.logit_filters = []
-        if self.options.suppress_blank:
-            self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
-        if self.options.suppress_tokens:
-            self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
-        if not options.without_timestamps:
-            precision = CHUNK_LENGTH / model.dims.n_audio_ctx  # usually 0.02 seconds
-            max_initial_timestamp_index = None
-            if options.max_initial_timestamp:
-                max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision)
-            self.logit_filters.append(
-                ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index)
-            )
-
-    def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
-        if options.beam_size is not None and options.best_of is not None:
-            raise ValueError("beam_size and best_of can't be given together")
-        if options.temperature == 0:
-            if options.best_of is not None:
-                raise ValueError("best_of with greedy sampling (T=0) is not compatible")
-        if options.patience is not None and options.beam_size is None:
-            raise ValueError("patience requires beam_size to be given")
-        if options.length_penalty is not None and not (0 <= options.length_penalty <= 1):
-            raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
-
-        return options
-
-    def _get_initial_tokens(self) -> Tuple[int]:
-        tokens = list(self.sot_sequence)
-        prefix = self.options.prefix
-        prompt = self.options.prompt
-
-        if prefix:
-            prefix_tokens = (
-                self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
-            )
-            if self.sample_len is not None:
-                max_prefix_len = self.n_ctx // 2 - self.sample_len
-                prefix_tokens = prefix_tokens[-max_prefix_len:]
-            tokens = tokens + prefix_tokens
-
-        if prompt:
-            prompt_tokens = (
-                self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
-            )
-            tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens
-
-        return tuple(tokens)
-
-    def _get_suppress_tokens(self) -> Tuple[int]:
-        suppress_tokens = self.options.suppress_tokens
-
-        if isinstance(suppress_tokens, str):
-            suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
-
-        if -1 in suppress_tokens:
-            suppress_tokens = [t for t in suppress_tokens if t >= 0]
-            suppress_tokens.extend(self.tokenizer.non_speech_tokens)
-        elif suppress_tokens is None or len(suppress_tokens) == 0:
-            suppress_tokens = []  # interpret empty string as an empty list
-        else:
-            assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
-
-        suppress_tokens.extend(
-            [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
-        )
-        if self.tokenizer.no_speech is not None:
-            # no-speech probability is collected separately
-            suppress_tokens.append(self.tokenizer.no_speech)
-
-        return tuple(sorted(set(suppress_tokens)))
-
-    def _get_audio_features(self, mel: Tensor):
-        if self.options.fp16:
-            mel = mel.half()
-
-        if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
-            # encoded audio features are given; skip audio encoding
-            audio_features = mel
-        else:
-            audio_features = self.model.encoder(mel)
-
-        if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32):
-            return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
-
-        return audio_features
-
-    def _detect_language(self, audio_features: Tensor, tokens: Tensor):
-        languages = [self.options.language] * audio_features.shape[0]
-        lang_probs = None
-
-        if self.options.language is None or self.options.task == "lang_id":
-            lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
-            languages = [max(probs, key=probs.get) for probs in lang_probs]
-            if self.options.language is None:
-                tokens[:, self.sot_index + 1] = lang_tokens  # write language tokens
-
-        return languages, lang_probs
-
-    def _main_loop(self, audio_features: Tensor, tokens: Tensor):
-        assert audio_features.shape[0] == tokens.shape[0]
-        n_batch = tokens.shape[0]
-        sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
-        no_speech_probs = [np.nan] * n_batch
-
-        try:
-            for i in range(self.sample_len):
-                logits = self.inference.logits(tokens, audio_features)
-
-                if i == 0 and self.tokenizer.no_speech is not None:  # save no_speech_probs
-                    probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
-                    no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
-
-                # now we need to consider the logits at the last token only
-                logits = logits[:, -1]
-
-                # apply the logit filters, e.g. for suppressing or applying penalty to
-                for logit_filter in self.logit_filters:
-                    logit_filter.apply(logits, tokens)
-
-                # expand the tokens tensor with the selected next tokens
-                tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
-
-                if completed or tokens.shape[-1] > self.n_ctx:
-                    break
-        finally:
-            self.inference.cleanup_caching()
-
-        return tokens, sum_logprobs, no_speech_probs
-
-    @torch.no_grad()
-    def run(self, mel: Tensor) -> List[DecodingResult]:
-        self.decoder.reset()
-        tokenizer: Tokenizer = self.tokenizer
-        n_audio: int = mel.shape[0]
-
-        audio_features: Tensor = self._get_audio_features(mel)  # encoder forward pass
-        tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
-
-        # detect language if requested, overwriting the language token
-        languages, language_probs = self._detect_language(audio_features, tokens)
-        if self.options.task == "lang_id":
-            return [
-                DecodingResult(audio_features=features, language=language, language_probs=probs)
-                for features, language, probs in zip(audio_features, languages, language_probs)
-            ]
-
-        # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
-        audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
-        tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
-
-        # call the main sampling loop
-        tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)
-
-        # reshape the tensors to have (n_audio, n_group) as the first two dimensions
-        audio_features = audio_features[:: self.n_group]
-        no_speech_probs = no_speech_probs[:: self.n_group]
-        assert audio_features.shape[0] == len(no_speech_probs) == n_audio
-
-        tokens = tokens.reshape(n_audio, self.n_group, -1)
-        sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
-
-        # get the final candidates for each group, and slice between the first sampled token and EOT
-        tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
-        tokens: List[List[Tensor]] = [
-            [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens
-        ]
-
-        # select the top-ranked sample in each group
-        selected = self.sequence_ranker.rank(tokens, sum_logprobs)
-        tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
-        texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
-
-        sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
-        avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
-
-        fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs)
-        if len(set(map(len, fields))) != 1:
-            raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
-
-        return [
-            DecodingResult(
-                audio_features=features,
-                language=language,
-                tokens=tokens,
-                text=text,
-                avg_logprob=avg_logprob,
-                no_speech_prob=no_speech_prob,
-                temperature=self.options.temperature,
-                compression_ratio=compression_ratio(text),
-            )
-            for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
-        ]
-
-
-@torch.no_grad()
-def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
-    """
-    Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
-
-    Parameters
-    ----------
-    model: Whisper
-        the Whisper model instance
-
-    mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
-        A tensor containing the Mel spectrogram(s)
-
-    options: DecodingOptions
-        A dataclass that contains all necessary options for decoding 30-second segments
-
-    Returns
-    -------
-    result: Union[DecodingResult, List[DecodingResult]]
-        The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
-    """
-    single = mel.ndim == 2
-    if single:
-        mel = mel.unsqueeze(0)
-
-    result = DecodingTask(model, options).run(mel)
-    
-    if single:
-        result = result[0]
-
-    return result
diff --git a/whisperx/diarize.py b/whisperx/diarize.py
index 6e0e880..52d1bf1 100644
--- a/whisperx/diarize.py
+++ b/whisperx/diarize.py
@@ -1,8 +1,23 @@
 import numpy as np
 import pandas as pd
+from pyannote.audio import Pipeline
+
+class DiarizationPipeline:
+    def __init__(
+        self,
+        model_name="pyannote/speaker-diarization@2.1",
+        use_auth_token=None,
+    ):
+        self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token)
+
+    def __call__(self, audio, min_speakers=None, max_speakers=None):
+        segments = self.model(audio, min_speakers=min_speakers, max_speakers=max_speakers)
+        diarize_df = pd.DataFrame(segments.itertracks(yield_label=True))
+        diarize_df['start'] = diarize_df[0].apply(lambda x: x.start)
+        diarize_df['end'] = diarize_df[0].apply(lambda x: x.end)
+        return diarize_df
 
 def assign_word_speakers(diarize_df, result_segments, fill_nearest=False):
- 
     for seg in result_segments:
         wdf = seg['word-segments']
         if len(wdf['start'].dropna()) == 0:
diff --git a/whisperx/model.py b/whisperx/model.py
deleted file mode 100644
index 9bdd84e..0000000
--- a/whisperx/model.py
+++ /dev/null
@@ -1,268 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict
-from typing import Iterable, Optional
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import Tensor
-from torch import nn
-
-from .transcribe import transcribe as transcribe_function
-from .decoding import detect_language as detect_language_function, decode as decode_function
-
-
-@dataclass
-class ModelDimensions:
-    n_mels: int
-    n_audio_ctx: int
-    n_audio_state: int
-    n_audio_head: int
-    n_audio_layer: int
-    n_vocab: int
-    n_text_ctx: int
-    n_text_state: int
-    n_text_head: int
-    n_text_layer: int
-
-
-class LayerNorm(nn.LayerNorm):
-    def forward(self, x: Tensor) -> Tensor:
-        return super().forward(x.float()).type(x.dtype)
-
-
-class Linear(nn.Linear):
-    def forward(self, x: Tensor) -> Tensor:
-        return F.linear(
-            x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype)
-        )
-
-
-class Conv1d(nn.Conv1d):
-    def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
-        return super()._conv_forward(
-            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
-        )
-
-
-def sinusoids(length, channels, max_timescale=10000):
-    """Returns sinusoids for positional embedding"""
-    assert channels % 2 == 0
-    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
-    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
-    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
-    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, n_state: int, n_head: int):
-        super().__init__()
-        self.n_head = n_head
-        self.query = Linear(n_state, n_state)
-        self.key = Linear(n_state, n_state, bias=False)
-        self.value = Linear(n_state, n_state)
-        self.out = Linear(n_state, n_state)
-
-    def forward(
-        self,
-        x: Tensor,
-        xa: Optional[Tensor] = None,
-        mask: Optional[Tensor] = None,
-        kv_cache: Optional[dict] = None,
-    ):
-        q = self.query(x)
-
-        if kv_cache is None or xa is None or self.key not in kv_cache:
-            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
-            # otherwise, perform key/value projections for self- or cross-attention as usual.
-            k = self.key(x if xa is None else xa)
-            v = self.value(x if xa is None else xa)
-        else:
-            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
-            k = kv_cache[self.key]
-            v = kv_cache[self.value]
-
-        wv, qk = self.qkv_attention(q, k, v, mask)
-        return self.out(wv), qk
-
-    def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
-        n_batch, n_ctx, n_state = q.shape
-        scale = (n_state // self.n_head) ** -0.25
-        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
-        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
-        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
-
-        qk = q @ k
-        if mask is not None:
-            qk = qk + mask[:n_ctx, :n_ctx]
-        qk = qk.float()
-
-        w = F.softmax(qk, dim=-1).to(q.dtype)
-        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
-        super().__init__()
-
-        self.attn = MultiHeadAttention(n_state, n_head)
-        self.attn_ln = LayerNorm(n_state)
-
-        self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
-        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
-
-        n_mlp = n_state * 4
-        self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
-        self.mlp_ln = LayerNorm(n_state)
-
-    def forward(
-        self,
-        x: Tensor,
-        xa: Optional[Tensor] = None,
-        mask: Optional[Tensor] = None,
-        kv_cache: Optional[dict] = None,
-    ):
-        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
-        if self.cross_attn:
-            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
-        x = x + self.mlp(self.mlp_ln(x))
-        return x
-
-
-class AudioEncoder(nn.Module):
-    def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
-        super().__init__()
-        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
-        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
-        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
-
-        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
-            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
-        )
-        self.ln_post = LayerNorm(n_state)
-
-    def forward(self, x: Tensor):
-        """
-        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
-            the mel spectrogram of the audio
-        """
-        x = F.gelu(self.conv1(x))
-        x = F.gelu(self.conv2(x))
-        x = x.permute(0, 2, 1)
-
-        assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
-        x = (x + self.positional_embedding).to(x.dtype)
-
-        for block in self.blocks:
-            x = block(x)
-
-        x = self.ln_post(x)
-        return x
-
-
-class TextDecoder(nn.Module):
-    def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
-        super().__init__()
-
-        self.token_embedding = nn.Embedding(n_vocab, n_state)
-        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
-
-        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
-            [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
-        )
-        self.ln = LayerNorm(n_state)
-
-        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
-        self.register_buffer("mask", mask, persistent=False)
-
-    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
-        """
-        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
-            the text tokens
-        xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
-            the encoded audio features to be attended on
-        """
-        offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
-        x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
-        x = x.to(xa.dtype)
-
-        for block in self.blocks:
-            x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
-
-        x = self.ln(x)
-        logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
-
-        return logits
-
-
-class Whisper(nn.Module):
-    def __init__(self, dims: ModelDimensions):
-        super().__init__()
-        self.dims = dims
-        self.encoder = AudioEncoder(
-            self.dims.n_mels,
-            self.dims.n_audio_ctx,
-            self.dims.n_audio_state,
-            self.dims.n_audio_head,
-            self.dims.n_audio_layer,
-        )
-        self.decoder = TextDecoder(
-            self.dims.n_vocab,
-            self.dims.n_text_ctx,
-            self.dims.n_text_state,
-            self.dims.n_text_head,
-            self.dims.n_text_layer,
-        )
-
-    def embed_audio(self, mel: torch.Tensor):
-        return self.encoder(mel)
-
-    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
-        return self.decoder(tokens, audio_features)
-
-    def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
-        return self.decoder(tokens, self.encoder(mel))
-
-    @property
-    def device(self):
-        return next(self.parameters()).device
-
-    @property
-    def is_multilingual(self):
-        return self.dims.n_vocab == 51865
-
-    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
-        """
-        The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
-        tensors calculated for the previous positions. This method returns a dictionary that stores
-        all caches, and the necessary hooks for the key and value projection modules that save the
-        intermediate tensors to be reused during later calculations.
-
-        Returns
-        -------
-        cache : Dict[nn.Module, torch.Tensor]
-            A dictionary object mapping the key/value projection modules to its cache
-        hooks : List[RemovableHandle]
-            List of PyTorch RemovableHandle objects to stop the hooks to be called
-        """
-        cache = {**cache} if cache is not None else {}
-        hooks = []
-
-        def save_to_cache(module, _, output):
-            if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]:
-                cache[module] = output  # save as-is, for the first token or cross attention
-            else:
-                cache[module] = torch.cat([cache[module], output], dim=1).detach()
-            return cache[module]
-
-        def install_hooks(layer: nn.Module):
-            if isinstance(layer, MultiHeadAttention):
-                hooks.append(layer.key.register_forward_hook(save_to_cache))
-                hooks.append(layer.value.register_forward_hook(save_to_cache))
-
-        self.decoder.apply(install_hooks)
-        return cache, hooks
-
-    detect_language = detect_language_function
-    transcribe = transcribe_function
-    decode = decode_function
\ No newline at end of file
diff --git a/whisperx/normalizers/__init__.py b/whisperx/normalizers/__init__.py
deleted file mode 100644
index 0b10d5a..0000000
--- a/whisperx/normalizers/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .basic import BasicTextNormalizer
-from .english import EnglishTextNormalizer
diff --git a/whisperx/normalizers/basic.py b/whisperx/normalizers/basic.py
deleted file mode 100644
index ef8d249..0000000
--- a/whisperx/normalizers/basic.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import re
-import unicodedata
-
-import regex
-
-# non-ASCII letters that are not separated by "NFKD" normalization
-ADDITIONAL_DIACRITICS = {
-    "œ": "oe",
-    "Œ": "OE",
-    "ø": "o",
-    "Ø": "O",
-    "æ": "ae",
-    "Æ": "AE",
-    "ß": "ss",
-    "ẞ": "SS",
-    "đ": "d",
-    "Đ": "D",
-    "ð": "d",
-    "Ð": "D",
-    "þ": "th",
-    "Þ": "th",
-    "ł": "l",
-    "Ł": "L",
-}
-
-
-def remove_symbols_and_diacritics(s: str, keep=""):
-    """
-    Replace any other markers, symbols, and punctuations with a space,
-    and drop any diacritics (category 'Mn' and some manual mappings)
-    """
-    return "".join(
-        c
-        if c in keep
-        else ADDITIONAL_DIACRITICS[c]
-        if c in ADDITIONAL_DIACRITICS
-        else ""
-        if unicodedata.category(c) == "Mn"
-        else " "
-        if unicodedata.category(c)[0] in "MSP"
-        else c
-        for c in unicodedata.normalize("NFKD", s)
-    )
-
-
-def remove_symbols(s: str):
-    """
-    Replace any other markers, symbols, punctuations with a space, keeping diacritics
-    """
-    return "".join(
-        " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)
-    )
-
-
-class BasicTextNormalizer:
-    def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
-        self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
-        self.split_letters = split_letters
-
-    def __call__(self, s: str):
-        s = s.lower()
-        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
-        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
-        s = self.clean(s).lower()
-
-        if self.split_letters:
-            s = " ".join(regex.findall(r"\X", s, regex.U))
-
-        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
-
-        return s
diff --git a/whisperx/normalizers/english.json b/whisperx/normalizers/english.json
deleted file mode 100644
index 74a1c35..0000000
--- a/whisperx/normalizers/english.json
+++ /dev/null
@@ -1,1741 +0,0 @@
-{
-    "accessorise": "accessorize",
-    "accessorised": "accessorized",
-    "accessorises": "accessorizes",
-    "accessorising": "accessorizing",
-    "acclimatisation": "acclimatization",
-    "acclimatise": "acclimatize",
-    "acclimatised": "acclimatized",
-    "acclimatises": "acclimatizes",
-    "acclimatising": "acclimatizing",
-    "accoutrements": "accouterments",
-    "aeon": "eon",
-    "aeons": "eons",
-    "aerogramme": "aerogram",
-    "aerogrammes": "aerograms",
-    "aeroplane": "airplane",
-    "aeroplanes": "airplanes",
-    "aesthete": "esthete",
-    "aesthetes": "esthetes",
-    "aesthetic": "esthetic",
-    "aesthetically": "esthetically",
-    "aesthetics": "esthetics",
-    "aetiology": "etiology",
-    "ageing": "aging",
-    "aggrandisement": "aggrandizement",
-    "agonise": "agonize",
-    "agonised": "agonized",
-    "agonises": "agonizes",
-    "agonising": "agonizing",
-    "agonisingly": "agonizingly",
-    "almanack": "almanac",
-    "almanacks": "almanacs",
-    "aluminium": "aluminum",
-    "amortisable": "amortizable",
-    "amortisation": "amortization",
-    "amortisations": "amortizations",
-    "amortise": "amortize",
-    "amortised": "amortized",
-    "amortises": "amortizes",
-    "amortising": "amortizing",
-    "amphitheatre": "amphitheater",
-    "amphitheatres": "amphitheaters",
-    "anaemia": "anemia",
-    "anaemic": "anemic",
-    "anaesthesia": "anesthesia",
-    "anaesthetic": "anesthetic",
-    "anaesthetics": "anesthetics",
-    "anaesthetise": "anesthetize",
-    "anaesthetised": "anesthetized",
-    "anaesthetises": "anesthetizes",
-    "anaesthetising": "anesthetizing",
-    "anaesthetist": "anesthetist",
-    "anaesthetists": "anesthetists",
-    "anaesthetize": "anesthetize",
-    "anaesthetized": "anesthetized",
-    "anaesthetizes": "anesthetizes",
-    "anaesthetizing": "anesthetizing",
-    "analogue": "analog",
-    "analogues": "analogs",
-    "analyse": "analyze",
-    "analysed": "analyzed",
-    "analyses": "analyzes",
-    "analysing": "analyzing",
-    "anglicise": "anglicize",
-    "anglicised": "anglicized",
-    "anglicises": "anglicizes",
-    "anglicising": "anglicizing",
-    "annualised": "annualized",
-    "antagonise": "antagonize",
-    "antagonised": "antagonized",
-    "antagonises": "antagonizes",
-    "antagonising": "antagonizing",
-    "apologise": "apologize",
-    "apologised": "apologized",
-    "apologises": "apologizes",
-    "apologising": "apologizing",
-    "appal": "appall",
-    "appals": "appalls",
-    "appetiser": "appetizer",
-    "appetisers": "appetizers",
-    "appetising": "appetizing",
-    "appetisingly": "appetizingly",
-    "arbour": "arbor",
-    "arbours": "arbors",
-    "archeological": "archaeological",
-    "archaeologically": "archeologically",
-    "archaeologist": "archeologist",
-    "archaeologists": "archeologists",
-    "archaeology": "archeology</span>",
-    "ardour": "ardor",
-    "armour": "armor",
-    "armoured": "armored",
-    "armourer": "armorer",
-    "armourers": "armorers",
-    "armouries": "armories",
-    "armoury": "armory",
-    "artefact": "artifact",
-    "artefacts": "artifacts",
-    "authorise": "authorize",
-    "authorised": "authorized",
-    "authorises": "authorizes",
-    "authorising": "authorizing",
-    "axe": "ax",
-    "backpedalled": "backpedaled",
-    "backpedalling": "backpedaling",
-    "bannister": "banister",
-    "bannisters": "banisters",
-    "baptise": "baptize",
-    "baptised": "baptized",
-    "baptises": "baptizes",
-    "baptising": "baptizing",
-    "bastardise": "bastardize",
-    "bastardised": "bastardized",
-    "bastardises": "bastardizes",
-    "bastardising": "bastardizing",
-    "battleax": "battleaxe",
-    "baulk": "balk",
-    "baulked": "balked",
-    "baulking": "balking",
-    "baulks": "balks",
-    "bedevilled": "bedeviled",
-    "bedevilling": "bedeviling",
-    "behaviour": "behavior",
-    "behavioural": "behavioral",
-    "behaviourism": "behaviorism",
-    "behaviourist": "behaviorist",
-    "behaviourists": "behaviorists",
-    "behaviours": "behaviors",
-    "behove": "behoove",
-    "behoved": "behooved",
-    "behoves": "behooves",
-    "bejewelled": "bejeweled",
-    "belabour": "belabor",
-    "belaboured": "belabored",
-    "belabouring": "belaboring",
-    "belabours": "belabors",
-    "bevelled": "beveled",
-    "bevvies": "bevies",
-    "bevvy": "bevy",
-    "biassed": "biased",
-    "biassing": "biasing",
-    "bingeing": "binging",
-    "bougainvillaea": "bougainvillea",
-    "bougainvillaeas": "bougainvilleas",
-    "bowdlerise": "bowdlerize",
-    "bowdlerised": "bowdlerized",
-    "bowdlerises": "bowdlerizes",
-    "bowdlerising": "bowdlerizing",
-    "breathalyse": "breathalyze",
-    "breathalysed": "breathalyzed",
-    "breathalyser": "breathalyzer",
-    "breathalysers": "breathalyzers",
-    "breathalyses": "breathalyzes",
-    "breathalysing": "breathalyzing",
-    "brutalise": "brutalize",
-    "brutalised": "brutalized",
-    "brutalises": "brutalizes",
-    "brutalising": "brutalizing",
-    "busses": "buses",
-    "bussing": "busing",
-    "caesarean": "cesarean",
-    "caesareans": "cesareans",
-    "calibre": "caliber",
-    "calibres": "calibers",
-    "calliper": "caliper",
-    "callipers": "calipers",
-    "callisthenics": "calisthenics",
-    "canalise": "canalize",
-    "canalised": "canalized",
-    "canalises": "canalizes",
-    "canalising": "canalizing",
-    "cancelation": "cancellation",
-    "cancelations": "cancellations",
-    "cancelled": "canceled",
-    "cancelling": "canceling",
-    "candour": "candor",
-    "cannibalise": "cannibalize",
-    "cannibalised": "cannibalized",
-    "cannibalises": "cannibalizes",
-    "cannibalising": "cannibalizing",
-    "canonise": "canonize",
-    "canonised": "canonized",
-    "canonises": "canonizes",
-    "canonising": "canonizing",
-    "capitalise": "capitalize",
-    "capitalised": "capitalized",
-    "capitalises": "capitalizes",
-    "capitalising": "capitalizing",
-    "caramelise": "caramelize",
-    "caramelised": "caramelized",
-    "caramelises": "caramelizes",
-    "caramelising": "caramelizing",
-    "carbonise": "carbonize",
-    "carbonised": "carbonized",
-    "carbonises": "carbonizes",
-    "carbonising": "carbonizing",
-    "carolled": "caroled",
-    "carolling": "caroling",
-    "catalogue": "catalog",
-    "catalogued": "cataloged",
-    "catalogues": "catalogs",
-    "cataloguing": "cataloging",
-    "catalyse": "catalyze",
-    "catalysed": "catalyzed",
-    "catalyses": "catalyzes",
-    "catalysing": "catalyzing",
-    "categorise": "categorize",
-    "categorised": "categorized",
-    "categorises": "categorizes",
-    "categorising": "categorizing",
-    "cauterise": "cauterize",
-    "cauterised": "cauterized",
-    "cauterises": "cauterizes",
-    "cauterising": "cauterizing",
-    "cavilled": "caviled",
-    "cavilling": "caviling",
-    "centigramme": "centigram",
-    "centigrammes": "centigrams",
-    "centilitre": "centiliter",
-    "centilitres": "centiliters",
-    "centimetre": "centimeter",
-    "centimetres": "centimeters",
-    "centralise": "centralize",
-    "centralised": "centralized",
-    "centralises": "centralizes",
-    "centralising": "centralizing",
-    "centre": "center",
-    "centred": "centered",
-    "centrefold": "centerfold",
-    "centrefolds": "centerfolds",
-    "centrepiece": "centerpiece",
-    "centrepieces": "centerpieces",
-    "centres": "centers",
-    "channelled": "channeled",
-    "channelling": "channeling",
-    "characterise": "characterize",
-    "characterised": "characterized",
-    "characterises": "characterizes",
-    "characterising": "characterizing",
-    "cheque": "check",
-    "chequebook": "checkbook",
-    "chequebooks": "checkbooks",
-    "chequered": "checkered",
-    "cheques": "checks",
-    "chilli": "chili",
-    "chimaera": "chimera",
-    "chimaeras": "chimeras",
-    "chiselled": "chiseled",
-    "chiselling": "chiseling",
-    "circularise": "circularize",
-    "circularised": "circularized",
-    "circularises": "circularizes",
-    "circularising": "circularizing",
-    "civilise": "civilize",
-    "civilised": "civilized",
-    "civilises": "civilizes",
-    "civilising": "civilizing",
-    "clamour": "clamor",
-    "clamoured": "clamored",
-    "clamouring": "clamoring",
-    "clamours": "clamors",
-    "clangour": "clangor",
-    "clarinettist": "clarinetist",
-    "clarinettists": "clarinetists",
-    "collectivise": "collectivize",
-    "collectivised": "collectivized",
-    "collectivises": "collectivizes",
-    "collectivising": "collectivizing",
-    "colonisation": "colonization",
-    "colonise": "colonize",
-    "colonised": "colonized",
-    "coloniser": "colonizer",
-    "colonisers": "colonizers",
-    "colonises": "colonizes",
-    "colonising": "colonizing",
-    "colour": "color",
-    "colourant": "colorant",
-    "colourants": "colorants",
-    "coloured": "colored",
-    "coloureds": "coloreds",
-    "colourful": "colorful",
-    "colourfully": "colorfully",
-    "colouring": "coloring",
-    "colourize": "colorize",
-    "colourized": "colorized",
-    "colourizes": "colorizes",
-    "colourizing": "colorizing",
-    "colourless": "colorless",
-    "colours": "colors",
-    "commercialise": "commercialize",
-    "commercialised": "commercialized",
-    "commercialises": "commercializes",
-    "commercialising": "commercializing",
-    "compartmentalise": "compartmentalize",
-    "compartmentalised": "compartmentalized",
-    "compartmentalises": "compartmentalizes",
-    "compartmentalising": "compartmentalizing",
-    "computerise": "computerize",
-    "computerised": "computerized",
-    "computerises": "computerizes",
-    "computerising": "computerizing",
-    "conceptualise": "conceptualize",
-    "conceptualised": "conceptualized",
-    "conceptualises": "conceptualizes",
-    "conceptualising": "conceptualizing",
-    "connexion": "connection",
-    "connexions": "connections",
-    "contextualise": "contextualize",
-    "contextualised": "contextualized",
-    "contextualises": "contextualizes",
-    "contextualising": "contextualizing",
-    "cosier": "cozier",
-    "cosies": "cozies",
-    "cosiest": "coziest",
-    "cosily": "cozily",
-    "cosiness": "coziness",
-    "cosy": "cozy",
-    "councillor": "councilor",
-    "councillors": "councilors",
-    "counselled": "counseled",
-    "counselling": "counseling",
-    "counsellor": "counselor",
-    "counsellors": "counselors",
-    "crenelated": "crenellated",
-    "criminalise": "criminalize",
-    "criminalised": "criminalized",
-    "criminalises": "criminalizes",
-    "criminalising": "criminalizing",
-    "criticise": "criticize",
-    "criticised": "criticized",
-    "criticises": "criticizes",
-    "criticising": "criticizing",
-    "crueller": "crueler",
-    "cruellest": "cruelest",
-    "crystallisation": "crystallization",
-    "crystallise": "crystallize",
-    "crystallised": "crystallized",
-    "crystallises": "crystallizes",
-    "crystallising": "crystallizing",
-    "cudgelled": "cudgeled",
-    "cudgelling": "cudgeling",
-    "customise": "customize",
-    "customised": "customized",
-    "customises": "customizes",
-    "customising": "customizing",
-    "cypher": "cipher",
-    "cyphers": "ciphers",
-    "decentralisation": "decentralization",
-    "decentralise": "decentralize",
-    "decentralised": "decentralized",
-    "decentralises": "decentralizes",
-    "decentralising": "decentralizing",
-    "decriminalisation": "decriminalization",
-    "decriminalise": "decriminalize",
-    "decriminalised": "decriminalized",
-    "decriminalises": "decriminalizes",
-    "decriminalising": "decriminalizing",
-    "defence": "defense",
-    "defenceless": "defenseless",
-    "defences": "defenses",
-    "dehumanisation": "dehumanization",
-    "dehumanise": "dehumanize",
-    "dehumanised": "dehumanized",
-    "dehumanises": "dehumanizes",
-    "dehumanising": "dehumanizing",
-    "demeanour": "demeanor",
-    "demilitarisation": "demilitarization",
-    "demilitarise": "demilitarize",
-    "demilitarised": "demilitarized",
-    "demilitarises": "demilitarizes",
-    "demilitarising": "demilitarizing",
-    "demobilisation": "demobilization",
-    "demobilise": "demobilize",
-    "demobilised": "demobilized",
-    "demobilises": "demobilizes",
-    "demobilising": "demobilizing",
-    "democratisation": "democratization",
-    "democratise": "democratize",
-    "democratised": "democratized",
-    "democratises": "democratizes",
-    "democratising": "democratizing",
-    "demonise": "demonize",
-    "demonised": "demonized",
-    "demonises": "demonizes",
-    "demonising": "demonizing",
-    "demoralisation": "demoralization",
-    "demoralise": "demoralize",
-    "demoralised": "demoralized",
-    "demoralises": "demoralizes",
-    "demoralising": "demoralizing",
-    "denationalisation": "denationalization",
-    "denationalise": "denationalize",
-    "denationalised": "denationalized",
-    "denationalises": "denationalizes",
-    "denationalising": "denationalizing",
-    "deodorise": "deodorize",
-    "deodorised": "deodorized",
-    "deodorises": "deodorizes",
-    "deodorising": "deodorizing",
-    "depersonalise": "depersonalize",
-    "depersonalised": "depersonalized",
-    "depersonalises": "depersonalizes",
-    "depersonalising": "depersonalizing",
-    "deputise": "deputize",
-    "deputised": "deputized",
-    "deputises": "deputizes",
-    "deputising": "deputizing",
-    "desensitisation": "desensitization",
-    "desensitise": "desensitize",
-    "desensitised": "desensitized",
-    "desensitises": "desensitizes",
-    "desensitising": "desensitizing",
-    "destabilisation": "destabilization",
-    "destabilise": "destabilize",
-    "destabilised": "destabilized",
-    "destabilises": "destabilizes",
-    "destabilising": "destabilizing",
-    "dialled": "dialed",
-    "dialling": "dialing",
-    "dialogue": "dialog",
-    "dialogues": "dialogs",
-    "diarrhoea": "diarrhea",
-    "digitise": "digitize",
-    "digitised": "digitized",
-    "digitises": "digitizes",
-    "digitising": "digitizing",
-    "disc": "disk",
-    "discolour": "discolor",
-    "discoloured": "discolored",
-    "discolouring": "discoloring",
-    "discolours": "discolors",
-    "discs": "disks",
-    "disembowelled": "disemboweled",
-    "disembowelling": "disemboweling",
-    "disfavour": "disfavor",
-    "dishevelled": "disheveled",
-    "dishonour": "dishonor",
-    "dishonourable": "dishonorable",
-    "dishonourably": "dishonorably",
-    "dishonoured": "dishonored",
-    "dishonouring": "dishonoring",
-    "dishonours": "dishonors",
-    "disorganisation": "disorganization",
-    "disorganised": "disorganized",
-    "distil": "distill",
-    "distils": "distills",
-    "dramatisation": "dramatization",
-    "dramatisations": "dramatizations",
-    "dramatise": "dramatize",
-    "dramatised": "dramatized",
-    "dramatises": "dramatizes",
-    "dramatising": "dramatizing",
-    "draught": "draft",
-    "draughtboard": "draftboard",
-    "draughtboards": "draftboards",
-    "draughtier": "draftier",
-    "draughtiest": "draftiest",
-    "draughts": "drafts",
-    "draughtsman": "draftsman",
-    "draughtsmanship": "draftsmanship",
-    "draughtsmen": "draftsmen",
-    "draughtswoman": "draftswoman",
-    "draughtswomen": "draftswomen",
-    "draughty": "drafty",
-    "drivelled": "driveled",
-    "drivelling": "driveling",
-    "duelled": "dueled",
-    "duelling": "dueling",
-    "economise": "economize",
-    "economised": "economized",
-    "economises": "economizes",
-    "economising": "economizing",
-    "edoema": "edema",
-    "editorialise": "editorialize",
-    "editorialised": "editorialized",
-    "editorialises": "editorializes",
-    "editorialising": "editorializing",
-    "empathise": "empathize",
-    "empathised": "empathized",
-    "empathises": "empathizes",
-    "empathising": "empathizing",
-    "emphasise": "emphasize",
-    "emphasised": "emphasized",
-    "emphasises": "emphasizes",
-    "emphasising": "emphasizing",
-    "enamelled": "enameled",
-    "enamelling": "enameling",
-    "enamoured": "enamored",
-    "encyclopaedia": "encyclopedia",
-    "encyclopaedias": "encyclopedias",
-    "encyclopaedic": "encyclopedic",
-    "endeavour": "endeavor",
-    "endeavoured": "endeavored",
-    "endeavouring": "endeavoring",
-    "endeavours": "endeavors",
-    "energise": "energize",
-    "energised": "energized",
-    "energises": "energizes",
-    "energising": "energizing",
-    "enrol": "enroll",
-    "enrols": "enrolls",
-    "enthral": "enthrall",
-    "enthrals": "enthralls",
-    "epaulette": "epaulet",
-    "epaulettes": "epaulets",
-    "epicentre": "epicenter",
-    "epicentres": "epicenters",
-    "epilogue": "epilog",
-    "epilogues": "epilogs",
-    "epitomise": "epitomize",
-    "epitomised": "epitomized",
-    "epitomises": "epitomizes",
-    "epitomising": "epitomizing",
-    "equalisation": "equalization",
-    "equalise": "equalize",
-    "equalised": "equalized",
-    "equaliser": "equalizer",
-    "equalisers": "equalizers",
-    "equalises": "equalizes",
-    "equalising": "equalizing",
-    "eulogise": "eulogize",
-    "eulogised": "eulogized",
-    "eulogises": "eulogizes",
-    "eulogising": "eulogizing",
-    "evangelise": "evangelize",
-    "evangelised": "evangelized",
-    "evangelises": "evangelizes",
-    "evangelising": "evangelizing",
-    "exorcise": "exorcize",
-    "exorcised": "exorcized",
-    "exorcises": "exorcizes",
-    "exorcising": "exorcizing",
-    "extemporisation": "extemporization",
-    "extemporise": "extemporize",
-    "extemporised": "extemporized",
-    "extemporises": "extemporizes",
-    "extemporising": "extemporizing",
-    "externalisation": "externalization",
-    "externalisations": "externalizations",
-    "externalise": "externalize",
-    "externalised": "externalized",
-    "externalises": "externalizes",
-    "externalising": "externalizing",
-    "factorise": "factorize",
-    "factorised": "factorized",
-    "factorises": "factorizes",
-    "factorising": "factorizing",
-    "faecal": "fecal",
-    "faeces": "feces",
-    "familiarisation": "familiarization",
-    "familiarise": "familiarize",
-    "familiarised": "familiarized",
-    "familiarises": "familiarizes",
-    "familiarising": "familiarizing",
-    "fantasise": "fantasize",
-    "fantasised": "fantasized",
-    "fantasises": "fantasizes",
-    "fantasising": "fantasizing",
-    "favour": "favor",
-    "favourable": "favorable",
-    "favourably": "favorably",
-    "favoured": "favored",
-    "favouring": "favoring",
-    "favourite": "favorite",
-    "favourites": "favorites",
-    "favouritism": "favoritism",
-    "favours": "favors",
-    "feminise": "feminize",
-    "feminised": "feminized",
-    "feminises": "feminizes",
-    "feminising": "feminizing",
-    "fertilisation": "fertilization",
-    "fertilise": "fertilize",
-    "fertilised": "fertilized",
-    "fertiliser": "fertilizer",
-    "fertilisers": "fertilizers",
-    "fertilises": "fertilizes",
-    "fertilising": "fertilizing",
-    "fervour": "fervor",
-    "fibre": "fiber",
-    "fibreglass": "fiberglass",
-    "fibres": "fibers",
-    "fictionalisation": "fictionalization",
-    "fictionalisations": "fictionalizations",
-    "fictionalise": "fictionalize",
-    "fictionalised": "fictionalized",
-    "fictionalises": "fictionalizes",
-    "fictionalising": "fictionalizing",
-    "fillet": "filet",
-    "filleted": "fileted",
-    "filleting": "fileting",
-    "fillets": "filets",
-    "finalisation": "finalization",
-    "finalise": "finalize",
-    "finalised": "finalized",
-    "finalises": "finalizes",
-    "finalising": "finalizing",
-    "flautist": "flutist",
-    "flautists": "flutists",
-    "flavour": "flavor",
-    "flavoured": "flavored",
-    "flavouring": "flavoring",
-    "flavourings": "flavorings",
-    "flavourless": "flavorless",
-    "flavours": "flavors",
-    "flavoursome": "flavorsome",
-    "flyer / flier": "flier / flyer",
-    "foetal": "fetal",
-    "foetid": "fetid",
-    "foetus": "fetus",
-    "foetuses": "fetuses",
-    "formalisation": "formalization",
-    "formalise": "formalize",
-    "formalised": "formalized",
-    "formalises": "formalizes",
-    "formalising": "formalizing",
-    "fossilisation": "fossilization",
-    "fossilise": "fossilize",
-    "fossilised": "fossilized",
-    "fossilises": "fossilizes",
-    "fossilising": "fossilizing",
-    "fraternisation": "fraternization",
-    "fraternise": "fraternize",
-    "fraternised": "fraternized",
-    "fraternises": "fraternizes",
-    "fraternising": "fraternizing",
-    "fulfil": "fulfill",
-    "fulfilment": "fulfillment",
-    "fulfils": "fulfills",
-    "funnelled": "funneled",
-    "funnelling": "funneling",
-    "galvanise": "galvanize",
-    "galvanised": "galvanized",
-    "galvanises": "galvanizes",
-    "galvanising": "galvanizing",
-    "gambolled": "gamboled",
-    "gambolling": "gamboling",
-    "gaol": "jail",
-    "gaolbird": "jailbird",
-    "gaolbirds": "jailbirds",
-    "gaolbreak": "jailbreak",
-    "gaolbreaks": "jailbreaks",
-    "gaoled": "jailed",
-    "gaoler": "jailer",
-    "gaolers": "jailers",
-    "gaoling": "jailing",
-    "gaols": "jails",
-    "gasses": "gases",
-    "gage": "gauge",
-    "gaged": "gauged",
-    "gages": "gauges",
-    "gaging": "gauging",
-    "generalisation": "generalization",
-    "generalisations": "generalizations",
-    "generalise": "generalize",
-    "generalised": "generalized",
-    "generalises": "generalizes",
-    "generalising": "generalizing",
-    "ghettoise": "ghettoize",
-    "ghettoised": "ghettoized",
-    "ghettoises": "ghettoizes",
-    "ghettoising": "ghettoizing",
-    "gipsies": "gypsies",
-    "glamorise": "glamorize",
-    "glamorised": "glamorized",
-    "glamorises": "glamorizes",
-    "glamorising": "glamorizing",
-    "glamor": "glamour",
-    "globalisation": "globalization",
-    "globalise": "globalize",
-    "globalised": "globalized",
-    "globalises": "globalizes",
-    "globalising": "globalizing",
-    "glueing": "gluing",
-    "goitre": "goiter",
-    "goitres": "goiters",
-    "gonorrhoea": "gonorrhea",
-    "gramme": "gram",
-    "grammes": "grams",
-    "gravelled": "graveled",
-    "grey": "gray",
-    "greyed": "grayed",
-    "greying": "graying",
-    "greyish": "grayish",
-    "greyness": "grayness",
-    "greys": "grays",
-    "grovelled": "groveled",
-    "grovelling": "groveling",
-    "groyne": "groin",
-    "groynes": "groins",
-    "gruelling": "grueling",
-    "gruellingly": "gruelingly",
-    "gryphon": "griffin",
-    "gryphons": "griffins",
-    "gynaecological": "gynecological",
-    "gynaecologist": "gynecologist",
-    "gynaecologists": "gynecologists",
-    "gynaecology": "gynecology",
-    "haematological": "hematological",
-    "haematologist": "hematologist",
-    "haematologists": "hematologists",
-    "haematology": "hematology",
-    "haemoglobin": "hemoglobin",
-    "haemophilia": "hemophilia",
-    "haemophiliac": "hemophiliac",
-    "haemophiliacs": "hemophiliacs",
-    "haemorrhage": "hemorrhage",
-    "haemorrhaged": "hemorrhaged",
-    "haemorrhages": "hemorrhages",
-    "haemorrhaging": "hemorrhaging",
-    "haemorrhoids": "hemorrhoids",
-    "harbour": "harbor",
-    "harboured": "harbored",
-    "harbouring": "harboring",
-    "harbours": "harbors",
-    "harmonisation": "harmonization",
-    "harmonise": "harmonize",
-    "harmonised": "harmonized",
-    "harmonises": "harmonizes",
-    "harmonising": "harmonizing",
-    "homoeopath": "homeopath",
-    "homoeopathic": "homeopathic",
-    "homoeopaths": "homeopaths",
-    "homoeopathy": "homeopathy",
-    "homogenise": "homogenize",
-    "homogenised": "homogenized",
-    "homogenises": "homogenizes",
-    "homogenising": "homogenizing",
-    "honour": "honor",
-    "honourable": "honorable",
-    "honourably": "honorably",
-    "honoured": "honored",
-    "honouring": "honoring",
-    "honours": "honors",
-    "hospitalisation": "hospitalization",
-    "hospitalise": "hospitalize",
-    "hospitalised": "hospitalized",
-    "hospitalises": "hospitalizes",
-    "hospitalising": "hospitalizing",
-    "humanise": "humanize",
-    "humanised": "humanized",
-    "humanises": "humanizes",
-    "humanising": "humanizing",
-    "humour": "humor",
-    "humoured": "humored",
-    "humouring": "humoring",
-    "humourless": "humorless",
-    "humours": "humors",
-    "hybridise": "hybridize",
-    "hybridised": "hybridized",
-    "hybridises": "hybridizes",
-    "hybridising": "hybridizing",
-    "hypnotise": "hypnotize",
-    "hypnotised": "hypnotized",
-    "hypnotises": "hypnotizes",
-    "hypnotising": "hypnotizing",
-    "hypothesise": "hypothesize",
-    "hypothesised": "hypothesized",
-    "hypothesises": "hypothesizes",
-    "hypothesising": "hypothesizing",
-    "idealisation": "idealization",
-    "idealise": "idealize",
-    "idealised": "idealized",
-    "idealises": "idealizes",
-    "idealising": "idealizing",
-    "idolise": "idolize",
-    "idolised": "idolized",
-    "idolises": "idolizes",
-    "idolising": "idolizing",
-    "immobilisation": "immobilization",
-    "immobilise": "immobilize",
-    "immobilised": "immobilized",
-    "immobiliser": "immobilizer",
-    "immobilisers": "immobilizers",
-    "immobilises": "immobilizes",
-    "immobilising": "immobilizing",
-    "immortalise": "immortalize",
-    "immortalised": "immortalized",
-    "immortalises": "immortalizes",
-    "immortalising": "immortalizing",
-    "immunisation": "immunization",
-    "immunise": "immunize",
-    "immunised": "immunized",
-    "immunises": "immunizes",
-    "immunising": "immunizing",
-    "impanelled": "impaneled",
-    "impanelling": "impaneling",
-    "imperilled": "imperiled",
-    "imperilling": "imperiling",
-    "individualise": "individualize",
-    "individualised": "individualized",
-    "individualises": "individualizes",
-    "individualising": "individualizing",
-    "industrialise": "industrialize",
-    "industrialised": "industrialized",
-    "industrialises": "industrializes",
-    "industrialising": "industrializing",
-    "inflexion": "inflection",
-    "inflexions": "inflections",
-    "initialise": "initialize",
-    "initialised": "initialized",
-    "initialises": "initializes",
-    "initialising": "initializing",
-    "initialled": "initialed",
-    "initialling": "initialing",
-    "instal": "install",
-    "instalment": "installment",
-    "instalments": "installments",
-    "instals": "installs",
-    "instil": "instill",
-    "instils": "instills",
-    "institutionalisation": "institutionalization",
-    "institutionalise": "institutionalize",
-    "institutionalised": "institutionalized",
-    "institutionalises": "institutionalizes",
-    "institutionalising": "institutionalizing",
-    "intellectualise": "intellectualize",
-    "intellectualised": "intellectualized",
-    "intellectualises": "intellectualizes",
-    "intellectualising": "intellectualizing",
-    "internalisation": "internalization",
-    "internalise": "internalize",
-    "internalised": "internalized",
-    "internalises": "internalizes",
-    "internalising": "internalizing",
-    "internationalisation": "internationalization",
-    "internationalise": "internationalize",
-    "internationalised": "internationalized",
-    "internationalises": "internationalizes",
-    "internationalising": "internationalizing",
-    "ionisation": "ionization",
-    "ionise": "ionize",
-    "ionised": "ionized",
-    "ioniser": "ionizer",
-    "ionisers": "ionizers",
-    "ionises": "ionizes",
-    "ionising": "ionizing",
-    "italicise": "italicize",
-    "italicised": "italicized",
-    "italicises": "italicizes",
-    "italicising": "italicizing",
-    "itemise": "itemize",
-    "itemised": "itemized",
-    "itemises": "itemizes",
-    "itemising": "itemizing",
-    "jeopardise": "jeopardize",
-    "jeopardised": "jeopardized",
-    "jeopardises": "jeopardizes",
-    "jeopardising": "jeopardizing",
-    "jewelled": "jeweled",
-    "jeweller": "jeweler",
-    "jewellers": "jewelers",
-    "jewellery": "jewelry",
-    "judgement": "judgment",
-    "kilogramme": "kilogram",
-    "kilogrammes": "kilograms",
-    "kilometre": "kilometer",
-    "kilometres": "kilometers",
-    "labelled": "labeled",
-    "labelling": "labeling",
-    "labour": "labor",
-    "laboured": "labored",
-    "labourer": "laborer",
-    "labourers": "laborers",
-    "labouring": "laboring",
-    "labours": "labors",
-    "lacklustre": "lackluster",
-    "legalisation": "legalization",
-    "legalise": "legalize",
-    "legalised": "legalized",
-    "legalises": "legalizes",
-    "legalising": "legalizing",
-    "legitimise": "legitimize",
-    "legitimised": "legitimized",
-    "legitimises": "legitimizes",
-    "legitimising": "legitimizing",
-    "leukaemia": "leukemia",
-    "levelled": "leveled",
-    "leveller": "leveler",
-    "levellers": "levelers",
-    "levelling": "leveling",
-    "libelled": "libeled",
-    "libelling": "libeling",
-    "libellous": "libelous",
-    "liberalisation": "liberalization",
-    "liberalise": "liberalize",
-    "liberalised": "liberalized",
-    "liberalises": "liberalizes",
-    "liberalising": "liberalizing",
-    "licence": "license",
-    "licenced": "licensed",
-    "licences": "licenses",
-    "licencing": "licensing",
-    "likeable": "likable",
-    "lionisation": "lionization",
-    "lionise": "lionize",
-    "lionised": "lionized",
-    "lionises": "lionizes",
-    "lionising": "lionizing",
-    "liquidise": "liquidize",
-    "liquidised": "liquidized",
-    "liquidiser": "liquidizer",
-    "liquidisers": "liquidizers",
-    "liquidises": "liquidizes",
-    "liquidising": "liquidizing",
-    "litre": "liter",
-    "litres": "liters",
-    "localise": "localize",
-    "localised": "localized",
-    "localises": "localizes",
-    "localising": "localizing",
-    "louvre": "louver",
-    "louvred": "louvered",
-    "louvres": "louvers",
-    "lustre": "luster",
-    "magnetise": "magnetize",
-    "magnetised": "magnetized",
-    "magnetises": "magnetizes",
-    "magnetising": "magnetizing",
-    "manoeuvrability": "maneuverability",
-    "manoeuvrable": "maneuverable",
-    "manoeuvre": "maneuver",
-    "manoeuvred": "maneuvered",
-    "manoeuvres": "maneuvers",
-    "manoeuvring": "maneuvering",
-    "manoeuvrings": "maneuverings",
-    "marginalisation": "marginalization",
-    "marginalise": "marginalize",
-    "marginalised": "marginalized",
-    "marginalises": "marginalizes",
-    "marginalising": "marginalizing",
-    "marshalled": "marshaled",
-    "marshalling": "marshaling",
-    "marvelled": "marveled",
-    "marvelling": "marveling",
-    "marvellous": "marvelous",
-    "marvellously": "marvelously",
-    "materialisation": "materialization",
-    "materialise": "materialize",
-    "materialised": "materialized",
-    "materialises": "materializes",
-    "materialising": "materializing",
-    "maximisation": "maximization",
-    "maximise": "maximize",
-    "maximised": "maximized",
-    "maximises": "maximizes",
-    "maximising": "maximizing",
-    "meagre": "meager",
-    "mechanisation": "mechanization",
-    "mechanise": "mechanize",
-    "mechanised": "mechanized",
-    "mechanises": "mechanizes",
-    "mechanising": "mechanizing",
-    "mediaeval": "medieval",
-    "memorialise": "memorialize",
-    "memorialised": "memorialized",
-    "memorialises": "memorializes",
-    "memorialising": "memorializing",
-    "memorise": "memorize",
-    "memorised": "memorized",
-    "memorises": "memorizes",
-    "memorising": "memorizing",
-    "mesmerise": "mesmerize",
-    "mesmerised": "mesmerized",
-    "mesmerises": "mesmerizes",
-    "mesmerising": "mesmerizing",
-    "metabolise": "metabolize",
-    "metabolised": "metabolized",
-    "metabolises": "metabolizes",
-    "metabolising": "metabolizing",
-    "metre": "meter",
-    "metres": "meters",
-    "micrometre": "micrometer",
-    "micrometres": "micrometers",
-    "militarise": "militarize",
-    "militarised": "militarized",
-    "militarises": "militarizes",
-    "militarising": "militarizing",
-    "milligramme": "milligram",
-    "milligrammes": "milligrams",
-    "millilitre": "milliliter",
-    "millilitres": "milliliters",
-    "millimetre": "millimeter",
-    "millimetres": "millimeters",
-    "miniaturisation": "miniaturization",
-    "miniaturise": "miniaturize",
-    "miniaturised": "miniaturized",
-    "miniaturises": "miniaturizes",
-    "miniaturising": "miniaturizing",
-    "minibusses": "minibuses",
-    "minimise": "minimize",
-    "minimised": "minimized",
-    "minimises": "minimizes",
-    "minimising": "minimizing",
-    "misbehaviour": "misbehavior",
-    "misdemeanour": "misdemeanor",
-    "misdemeanours": "misdemeanors",
-    "misspelt": "misspelled",
-    "mitre": "miter",
-    "mitres": "miters",
-    "mobilisation": "mobilization",
-    "mobilise": "mobilize",
-    "mobilised": "mobilized",
-    "mobilises": "mobilizes",
-    "mobilising": "mobilizing",
-    "modelled": "modeled",
-    "modeller": "modeler",
-    "modellers": "modelers",
-    "modelling": "modeling",
-    "modernise": "modernize",
-    "modernised": "modernized",
-    "modernises": "modernizes",
-    "modernising": "modernizing",
-    "moisturise": "moisturize",
-    "moisturised": "moisturized",
-    "moisturiser": "moisturizer",
-    "moisturisers": "moisturizers",
-    "moisturises": "moisturizes",
-    "moisturising": "moisturizing",
-    "monologue": "monolog",
-    "monologues": "monologs",
-    "monopolisation": "monopolization",
-    "monopolise": "monopolize",
-    "monopolised": "monopolized",
-    "monopolises": "monopolizes",
-    "monopolising": "monopolizing",
-    "moralise": "moralize",
-    "moralised": "moralized",
-    "moralises": "moralizes",
-    "moralising": "moralizing",
-    "motorised": "motorized",
-    "mould": "mold",
-    "moulded": "molded",
-    "moulder": "molder",
-    "mouldered": "moldered",
-    "mouldering": "moldering",
-    "moulders": "molders",
-    "mouldier": "moldier",
-    "mouldiest": "moldiest",
-    "moulding": "molding",
-    "mouldings": "moldings",
-    "moulds": "molds",
-    "mouldy": "moldy",
-    "moult": "molt",
-    "moulted": "molted",
-    "moulting": "molting",
-    "moults": "molts",
-    "moustache": "mustache",
-    "moustached": "mustached",
-    "moustaches": "mustaches",
-    "moustachioed": "mustachioed",
-    "multicoloured": "multicolored",
-    "nationalisation": "nationalization",
-    "nationalisations": "nationalizations",
-    "nationalise": "nationalize",
-    "nationalised": "nationalized",
-    "nationalises": "nationalizes",
-    "nationalising": "nationalizing",
-    "naturalisation": "naturalization",
-    "naturalise": "naturalize",
-    "naturalised": "naturalized",
-    "naturalises": "naturalizes",
-    "naturalising": "naturalizing",
-    "neighbour": "neighbor",
-    "neighbourhood": "neighborhood",
-    "neighbourhoods": "neighborhoods",
-    "neighbouring": "neighboring",
-    "neighbourliness": "neighborliness",
-    "neighbourly": "neighborly",
-    "neighbours": "neighbors",
-    "neutralisation": "neutralization",
-    "neutralise": "neutralize",
-    "neutralised": "neutralized",
-    "neutralises": "neutralizes",
-    "neutralising": "neutralizing",
-    "normalisation": "normalization",
-    "normalise": "normalize",
-    "normalised": "normalized",
-    "normalises": "normalizes",
-    "normalising": "normalizing",
-    "odour": "odor",
-    "odourless": "odorless",
-    "odours": "odors",
-    "oesophagus": "esophagus",
-    "oesophaguses": "esophaguses",
-    "oestrogen": "estrogen",
-    "offence": "offense",
-    "offences": "offenses",
-    "omelette": "omelet",
-    "omelettes": "omelets",
-    "optimise": "optimize",
-    "optimised": "optimized",
-    "optimises": "optimizes",
-    "optimising": "optimizing",
-    "organisation": "organization",
-    "organisational": "organizational",
-    "organisations": "organizations",
-    "organise": "organize",
-    "organised": "organized",
-    "organiser": "organizer",
-    "organisers": "organizers",
-    "organises": "organizes",
-    "organising": "organizing",
-    "orthopaedic": "orthopedic",
-    "orthopaedics": "orthopedics",
-    "ostracise": "ostracize",
-    "ostracised": "ostracized",
-    "ostracises": "ostracizes",
-    "ostracising": "ostracizing",
-    "outmanoeuvre": "outmaneuver",
-    "outmanoeuvred": "outmaneuvered",
-    "outmanoeuvres": "outmaneuvers",
-    "outmanoeuvring": "outmaneuvering",
-    "overemphasise": "overemphasize",
-    "overemphasised": "overemphasized",
-    "overemphasises": "overemphasizes",
-    "overemphasising": "overemphasizing",
-    "oxidisation": "oxidization",
-    "oxidise": "oxidize",
-    "oxidised": "oxidized",
-    "oxidises": "oxidizes",
-    "oxidising": "oxidizing",
-    "paederast": "pederast",
-    "paederasts": "pederasts",
-    "paediatric": "pediatric",
-    "paediatrician": "pediatrician",
-    "paediatricians": "pediatricians",
-    "paediatrics": "pediatrics",
-    "paedophile": "pedophile",
-    "paedophiles": "pedophiles",
-    "paedophilia": "pedophilia",
-    "palaeolithic": "paleolithic",
-    "palaeontologist": "paleontologist",
-    "palaeontologists": "paleontologists",
-    "palaeontology": "paleontology",
-    "panelled": "paneled",
-    "panelling": "paneling",
-    "panellist": "panelist",
-    "panellists": "panelists",
-    "paralyse": "paralyze",
-    "paralysed": "paralyzed",
-    "paralyses": "paralyzes",
-    "paralysing": "paralyzing",
-    "parcelled": "parceled",
-    "parcelling": "parceling",
-    "parlour": "parlor",
-    "parlours": "parlors",
-    "particularise": "particularize",
-    "particularised": "particularized",
-    "particularises": "particularizes",
-    "particularising": "particularizing",
-    "passivisation": "passivization",
-    "passivise": "passivize",
-    "passivised": "passivized",
-    "passivises": "passivizes",
-    "passivising": "passivizing",
-    "pasteurisation": "pasteurization",
-    "pasteurise": "pasteurize",
-    "pasteurised": "pasteurized",
-    "pasteurises": "pasteurizes",
-    "pasteurising": "pasteurizing",
-    "patronise": "patronize",
-    "patronised": "patronized",
-    "patronises": "patronizes",
-    "patronising": "patronizing",
-    "patronisingly": "patronizingly",
-    "pedalled": "pedaled",
-    "pedalling": "pedaling",
-    "pedestrianisation": "pedestrianization",
-    "pedestrianise": "pedestrianize",
-    "pedestrianised": "pedestrianized",
-    "pedestrianises": "pedestrianizes",
-    "pedestrianising": "pedestrianizing",
-    "penalise": "penalize",
-    "penalised": "penalized",
-    "penalises": "penalizes",
-    "penalising": "penalizing",
-    "pencilled": "penciled",
-    "pencilling": "penciling",
-    "personalise": "personalize",
-    "personalised": "personalized",
-    "personalises": "personalizes",
-    "personalising": "personalizing",
-    "pharmacopoeia": "pharmacopeia",
-    "pharmacopoeias": "pharmacopeias",
-    "philosophise": "philosophize",
-    "philosophised": "philosophized",
-    "philosophises": "philosophizes",
-    "philosophising": "philosophizing",
-    "philtre": "filter",
-    "philtres": "filters",
-    "phoney": "phony",
-    "plagiarise": "plagiarize",
-    "plagiarised": "plagiarized",
-    "plagiarises": "plagiarizes",
-    "plagiarising": "plagiarizing",
-    "plough": "plow",
-    "ploughed": "plowed",
-    "ploughing": "plowing",
-    "ploughman": "plowman",
-    "ploughmen": "plowmen",
-    "ploughs": "plows",
-    "ploughshare": "plowshare",
-    "ploughshares": "plowshares",
-    "polarisation": "polarization",
-    "polarise": "polarize",
-    "polarised": "polarized",
-    "polarises": "polarizes",
-    "polarising": "polarizing",
-    "politicisation": "politicization",
-    "politicise": "politicize",
-    "politicised": "politicized",
-    "politicises": "politicizes",
-    "politicising": "politicizing",
-    "popularisation": "popularization",
-    "popularise": "popularize",
-    "popularised": "popularized",
-    "popularises": "popularizes",
-    "popularising": "popularizing",
-    "pouffe": "pouf",
-    "pouffes": "poufs",
-    "practise": "practice",
-    "practised": "practiced",
-    "practises": "practices",
-    "practising": "practicing",
-    "praesidium": "presidium",
-    "praesidiums": "presidiums",
-    "pressurisation": "pressurization",
-    "pressurise": "pressurize",
-    "pressurised": "pressurized",
-    "pressurises": "pressurizes",
-    "pressurising": "pressurizing",
-    "pretence": "pretense",
-    "pretences": "pretenses",
-    "primaeval": "primeval",
-    "prioritisation": "prioritization",
-    "prioritise": "prioritize",
-    "prioritised": "prioritized",
-    "prioritises": "prioritizes",
-    "prioritising": "prioritizing",
-    "privatisation": "privatization",
-    "privatisations": "privatizations",
-    "privatise": "privatize",
-    "privatised": "privatized",
-    "privatises": "privatizes",
-    "privatising": "privatizing",
-    "professionalisation": "professionalization",
-    "professionalise": "professionalize",
-    "professionalised": "professionalized",
-    "professionalises": "professionalizes",
-    "professionalising": "professionalizing",
-    "programme": "program",
-    "programmes": "programs",
-    "prologue": "prolog",
-    "prologues": "prologs",
-    "propagandise": "propagandize",
-    "propagandised": "propagandized",
-    "propagandises": "propagandizes",
-    "propagandising": "propagandizing",
-    "proselytise": "proselytize",
-    "proselytised": "proselytized",
-    "proselytiser": "proselytizer",
-    "proselytisers": "proselytizers",
-    "proselytises": "proselytizes",
-    "proselytising": "proselytizing",
-    "psychoanalyse": "psychoanalyze",
-    "psychoanalysed": "psychoanalyzed",
-    "psychoanalyses": "psychoanalyzes",
-    "psychoanalysing": "psychoanalyzing",
-    "publicise": "publicize",
-    "publicised": "publicized",
-    "publicises": "publicizes",
-    "publicising": "publicizing",
-    "pulverisation": "pulverization",
-    "pulverise": "pulverize",
-    "pulverised": "pulverized",
-    "pulverises": "pulverizes",
-    "pulverising": "pulverizing",
-    "pummelled": "pummel",
-    "pummelling": "pummeled",
-    "pyjama": "pajama",
-    "pyjamas": "pajamas",
-    "pzazz": "pizzazz",
-    "quarrelled": "quarreled",
-    "quarrelling": "quarreling",
-    "radicalise": "radicalize",
-    "radicalised": "radicalized",
-    "radicalises": "radicalizes",
-    "radicalising": "radicalizing",
-    "rancour": "rancor",
-    "randomise": "randomize",
-    "randomised": "randomized",
-    "randomises": "randomizes",
-    "randomising": "randomizing",
-    "rationalisation": "rationalization",
-    "rationalisations": "rationalizations",
-    "rationalise": "rationalize",
-    "rationalised": "rationalized",
-    "rationalises": "rationalizes",
-    "rationalising": "rationalizing",
-    "ravelled": "raveled",
-    "ravelling": "raveling",
-    "realisable": "realizable",
-    "realisation": "realization",
-    "realisations": "realizations",
-    "realise": "realize",
-    "realised": "realized",
-    "realises": "realizes",
-    "realising": "realizing",
-    "recognisable": "recognizable",
-    "recognisably": "recognizably",
-    "recognisance": "recognizance",
-    "recognise": "recognize",
-    "recognised": "recognized",
-    "recognises": "recognizes",
-    "recognising": "recognizing",
-    "reconnoitre": "reconnoiter",
-    "reconnoitred": "reconnoitered",
-    "reconnoitres": "reconnoiters",
-    "reconnoitring": "reconnoitering",
-    "refuelled": "refueled",
-    "refuelling": "refueling",
-    "regularisation": "regularization",
-    "regularise": "regularize",
-    "regularised": "regularized",
-    "regularises": "regularizes",
-    "regularising": "regularizing",
-    "remodelled": "remodeled",
-    "remodelling": "remodeling",
-    "remould": "remold",
-    "remoulded": "remolded",
-    "remoulding": "remolding",
-    "remoulds": "remolds",
-    "reorganisation": "reorganization",
-    "reorganisations": "reorganizations",
-    "reorganise": "reorganize",
-    "reorganised": "reorganized",
-    "reorganises": "reorganizes",
-    "reorganising": "reorganizing",
-    "revelled": "reveled",
-    "reveller": "reveler",
-    "revellers": "revelers",
-    "revelling": "reveling",
-    "revitalise": "revitalize",
-    "revitalised": "revitalized",
-    "revitalises": "revitalizes",
-    "revitalising": "revitalizing",
-    "revolutionise": "revolutionize",
-    "revolutionised": "revolutionized",
-    "revolutionises": "revolutionizes",
-    "revolutionising": "revolutionizing",
-    "rhapsodise": "rhapsodize",
-    "rhapsodised": "rhapsodized",
-    "rhapsodises": "rhapsodizes",
-    "rhapsodising": "rhapsodizing",
-    "rigour": "rigor",
-    "rigours": "rigors",
-    "ritualised": "ritualized",
-    "rivalled": "rivaled",
-    "rivalling": "rivaling",
-    "romanticise": "romanticize",
-    "romanticised": "romanticized",
-    "romanticises": "romanticizes",
-    "romanticising": "romanticizing",
-    "rumour": "rumor",
-    "rumoured": "rumored",
-    "rumours": "rumors",
-    "sabre": "saber",
-    "sabres": "sabers",
-    "saltpetre": "saltpeter",
-    "sanitise": "sanitize",
-    "sanitised": "sanitized",
-    "sanitises": "sanitizes",
-    "sanitising": "sanitizing",
-    "satirise": "satirize",
-    "satirised": "satirized",
-    "satirises": "satirizes",
-    "satirising": "satirizing",
-    "saviour": "savior",
-    "saviours": "saviors",
-    "savour": "savor",
-    "savoured": "savored",
-    "savouries": "savories",
-    "savouring": "savoring",
-    "savours": "savors",
-    "savoury": "savory",
-    "scandalise": "scandalize",
-    "scandalised": "scandalized",
-    "scandalises": "scandalizes",
-    "scandalising": "scandalizing",
-    "sceptic": "skeptic",
-    "sceptical": "skeptical",
-    "sceptically": "skeptically",
-    "scepticism": "skepticism",
-    "sceptics": "skeptics",
-    "sceptre": "scepter",
-    "sceptres": "scepters",
-    "scrutinise": "scrutinize",
-    "scrutinised": "scrutinized",
-    "scrutinises": "scrutinizes",
-    "scrutinising": "scrutinizing",
-    "secularisation": "secularization",
-    "secularise": "secularize",
-    "secularised": "secularized",
-    "secularises": "secularizes",
-    "secularising": "secularizing",
-    "sensationalise": "sensationalize",
-    "sensationalised": "sensationalized",
-    "sensationalises": "sensationalizes",
-    "sensationalising": "sensationalizing",
-    "sensitise": "sensitize",
-    "sensitised": "sensitized",
-    "sensitises": "sensitizes",
-    "sensitising": "sensitizing",
-    "sentimentalise": "sentimentalize",
-    "sentimentalised": "sentimentalized",
-    "sentimentalises": "sentimentalizes",
-    "sentimentalising": "sentimentalizing",
-    "sepulchre": "sepulcher",
-    "sepulchres": "sepulchers",
-    "serialisation": "serialization",
-    "serialisations": "serializations",
-    "serialise": "serialize",
-    "serialised": "serialized",
-    "serialises": "serializes",
-    "serialising": "serializing",
-    "sermonise": "sermonize",
-    "sermonised": "sermonized",
-    "sermonises": "sermonizes",
-    "sermonising": "sermonizing",
-    "sheikh": "sheik",
-    "shovelled": "shoveled",
-    "shovelling": "shoveling",
-    "shrivelled": "shriveled",
-    "shrivelling": "shriveling",
-    "signalise": "signalize",
-    "signalised": "signalized",
-    "signalises": "signalizes",
-    "signalising": "signalizing",
-    "signalled": "signaled",
-    "signalling": "signaling",
-    "smoulder": "smolder",
-    "smouldered": "smoldered",
-    "smouldering": "smoldering",
-    "smoulders": "smolders",
-    "snivelled": "sniveled",
-    "snivelling": "sniveling",
-    "snorkelled": "snorkeled",
-    "snorkelling": "snorkeling",
-    "snowplough": "snowplow",
-    "snowploughs": "snowplow",
-    "socialisation": "socialization",
-    "socialise": "socialize",
-    "socialised": "socialized",
-    "socialises": "socializes",
-    "socialising": "socializing",
-    "sodomise": "sodomize",
-    "sodomised": "sodomized",
-    "sodomises": "sodomizes",
-    "sodomising": "sodomizing",
-    "solemnise": "solemnize",
-    "solemnised": "solemnized",
-    "solemnises": "solemnizes",
-    "solemnising": "solemnizing",
-    "sombre": "somber",
-    "specialisation": "specialization",
-    "specialisations": "specializations",
-    "specialise": "specialize",
-    "specialised": "specialized",
-    "specialises": "specializes",
-    "specialising": "specializing",
-    "spectre": "specter",
-    "spectres": "specters",
-    "spiralled": "spiraled",
-    "spiralling": "spiraling",
-    "splendour": "splendor",
-    "splendours": "splendors",
-    "squirrelled": "squirreled",
-    "squirrelling": "squirreling",
-    "stabilisation": "stabilization",
-    "stabilise": "stabilize",
-    "stabilised": "stabilized",
-    "stabiliser": "stabilizer",
-    "stabilisers": "stabilizers",
-    "stabilises": "stabilizes",
-    "stabilising": "stabilizing",
-    "standardisation": "standardization",
-    "standardise": "standardize",
-    "standardised": "standardized",
-    "standardises": "standardizes",
-    "standardising": "standardizing",
-    "stencilled": "stenciled",
-    "stencilling": "stenciling",
-    "sterilisation": "sterilization",
-    "sterilisations": "sterilizations",
-    "sterilise": "sterilize",
-    "sterilised": "sterilized",
-    "steriliser": "sterilizer",
-    "sterilisers": "sterilizers",
-    "sterilises": "sterilizes",
-    "sterilising": "sterilizing",
-    "stigmatisation": "stigmatization",
-    "stigmatise": "stigmatize",
-    "stigmatised": "stigmatized",
-    "stigmatises": "stigmatizes",
-    "stigmatising": "stigmatizing",
-    "storey": "story",
-    "storeys": "stories",
-    "subsidisation": "subsidization",
-    "subsidise": "subsidize",
-    "subsidised": "subsidized",
-    "subsidiser": "subsidizer",
-    "subsidisers": "subsidizers",
-    "subsidises": "subsidizes",
-    "subsidising": "subsidizing",
-    "succour": "succor",
-    "succoured": "succored",
-    "succouring": "succoring",
-    "succours": "succors",
-    "sulphate": "sulfate",
-    "sulphates": "sulfates",
-    "sulphide": "sulfide",
-    "sulphides": "sulfides",
-    "sulphur": "sulfur",
-    "sulphurous": "sulfurous",
-    "summarise": "summarize",
-    "summarised": "summarized",
-    "summarises": "summarizes",
-    "summarising": "summarizing",
-    "swivelled": "swiveled",
-    "swivelling": "swiveling",
-    "symbolise": "symbolize",
-    "symbolised": "symbolized",
-    "symbolises": "symbolizes",
-    "symbolising": "symbolizing",
-    "sympathise": "sympathize",
-    "sympathised": "sympathized",
-    "sympathiser": "sympathizer",
-    "sympathisers": "sympathizers",
-    "sympathises": "sympathizes",
-    "sympathising": "sympathizing",
-    "synchronisation": "synchronization",
-    "synchronise": "synchronize",
-    "synchronised": "synchronized",
-    "synchronises": "synchronizes",
-    "synchronising": "synchronizing",
-    "synthesise": "synthesize",
-    "synthesised": "synthesized",
-    "synthesiser": "synthesizer",
-    "synthesisers": "synthesizers",
-    "synthesises": "synthesizes",
-    "synthesising": "synthesizing",
-    "syphon": "siphon",
-    "syphoned": "siphoned",
-    "syphoning": "siphoning",
-    "syphons": "siphons",
-    "systematisation": "systematization",
-    "systematise": "systematize",
-    "systematised": "systematized",
-    "systematises": "systematizes",
-    "systematising": "systematizing",
-    "tantalise": "tantalize",
-    "tantalised": "tantalized",
-    "tantalises": "tantalizes",
-    "tantalising": "tantalizing",
-    "tantalisingly": "tantalizingly",
-    "tasselled": "tasseled",
-    "technicolour": "technicolor",
-    "temporise": "temporize",
-    "temporised": "temporized",
-    "temporises": "temporizes",
-    "temporising": "temporizing",
-    "tenderise": "tenderize",
-    "tenderised": "tenderized",
-    "tenderises": "tenderizes",
-    "tenderising": "tenderizing",
-    "terrorise": "terrorize",
-    "terrorised": "terrorized",
-    "terrorises": "terrorizes",
-    "terrorising": "terrorizing",
-    "theatre": "theater",
-    "theatregoer": "theatergoer",
-    "theatregoers": "theatergoers",
-    "theatres": "theaters",
-    "theorise": "theorize",
-    "theorised": "theorized",
-    "theorises": "theorizes",
-    "theorising": "theorizing",
-    "tonne": "ton",
-    "tonnes": "tons",
-    "towelled": "toweled",
-    "towelling": "toweling",
-    "toxaemia": "toxemia",
-    "tranquillise": "tranquilize",
-    "tranquillised": "tranquilized",
-    "tranquilliser": "tranquilizer",
-    "tranquillisers": "tranquilizers",
-    "tranquillises": "tranquilizes",
-    "tranquillising": "tranquilizing",
-    "tranquillity": "tranquility",
-    "tranquillize": "tranquilize",
-    "tranquillized": "tranquilized",
-    "tranquillizer": "tranquilizer",
-    "tranquillizers": "tranquilizers",
-    "tranquillizes": "tranquilizes",
-    "tranquillizing": "tranquilizing",
-    "tranquilly": "tranquility",
-    "transistorised": "transistorized",
-    "traumatise": "traumatize",
-    "traumatised": "traumatized",
-    "traumatises": "traumatizes",
-    "traumatising": "traumatizing",
-    "travelled": "traveled",
-    "traveller": "traveler",
-    "travellers": "travelers",
-    "travelling": "traveling",
-    "travelog": "travelogue",
-    "travelogs": "travelogues",
-    "trialled": "trialed",
-    "trialling": "trialing",
-    "tricolour": "tricolor",
-    "tricolours": "tricolors",
-    "trivialise": "trivialize",
-    "trivialised": "trivialized",
-    "trivialises": "trivializes",
-    "trivialising": "trivializing",
-    "tumour": "tumor",
-    "tumours": "tumors",
-    "tunnelled": "tunneled",
-    "tunnelling": "tunneling",
-    "tyrannise": "tyrannize",
-    "tyrannised": "tyrannized",
-    "tyrannises": "tyrannizes",
-    "tyrannising": "tyrannizing",
-    "tyre": "tire",
-    "tyres": "tires",
-    "unauthorised": "unauthorized",
-    "uncivilised": "uncivilized",
-    "underutilised": "underutilized",
-    "unequalled": "unequaled",
-    "unfavourable": "unfavorable",
-    "unfavourably": "unfavorably",
-    "unionisation": "unionization",
-    "unionise": "unionize",
-    "unionised": "unionized",
-    "unionises": "unionizes",
-    "unionising": "unionizing",
-    "unorganised": "unorganized",
-    "unravelled": "unraveled",
-    "unravelling": "unraveling",
-    "unrecognisable": "unrecognizable",
-    "unrecognised": "unrecognized",
-    "unrivalled": "unrivaled",
-    "unsavoury": "unsavory",
-    "untrammelled": "untrammeled",
-    "urbanisation": "urbanization",
-    "urbanise": "urbanize",
-    "urbanised": "urbanized",
-    "urbanises": "urbanizes",
-    "urbanising": "urbanizing",
-    "utilisable": "utilizable",
-    "utilisation": "utilization",
-    "utilise": "utilize",
-    "utilised": "utilized",
-    "utilises": "utilizes",
-    "utilising": "utilizing",
-    "valour": "valor",
-    "vandalise": "vandalize",
-    "vandalised": "vandalized",
-    "vandalises": "vandalizes",
-    "vandalising": "vandalizing",
-    "vaporisation": "vaporization",
-    "vaporise": "vaporize",
-    "vaporised": "vaporized",
-    "vaporises": "vaporizes",
-    "vaporising": "vaporizing",
-    "vapour": "vapor",
-    "vapours": "vapors",
-    "verbalise": "verbalize",
-    "verbalised": "verbalized",
-    "verbalises": "verbalizes",
-    "verbalising": "verbalizing",
-    "victimisation": "victimization",
-    "victimise": "victimize",
-    "victimised": "victimized",
-    "victimises": "victimizes",
-    "victimising": "victimizing",
-    "videodisc": "videodisk",
-    "videodiscs": "videodisks",
-    "vigour": "vigor",
-    "visualisation": "visualization",
-    "visualisations": "visualizations",
-    "visualise": "visualize",
-    "visualised": "visualized",
-    "visualises": "visualizes",
-    "visualising": "visualizing",
-    "vocalisation": "vocalization",
-    "vocalisations": "vocalizations",
-    "vocalise": "vocalize",
-    "vocalised": "vocalized",
-    "vocalises": "vocalizes",
-    "vocalising": "vocalizing",
-    "vulcanised": "vulcanized",
-    "vulgarisation": "vulgarization",
-    "vulgarise": "vulgarize",
-    "vulgarised": "vulgarized",
-    "vulgarises": "vulgarizes",
-    "vulgarising": "vulgarizing",
-    "waggon": "wagon",
-    "waggons": "wagons",
-    "watercolour": "watercolor",
-    "watercolours": "watercolors",
-    "weaselled": "weaseled",
-    "weaselling": "weaseling",
-    "westernisation": "westernization",
-    "westernise": "westernize",
-    "westernised": "westernized",
-    "westernises": "westernizes",
-    "westernising": "westernizing",
-    "womanise": "womanize",
-    "womanised": "womanized",
-    "womaniser": "womanizer",
-    "womanisers": "womanizers",
-    "womanises": "womanizes",
-    "womanising": "womanizing",
-    "woollen": "woolen",
-    "woollens": "woolens",
-    "woollies": "woolies",
-    "woolly": "wooly",
-    "worshipped": "worshiped",
-    "worshipping": "worshiping",
-    "worshipper": "worshiper",
-    "yodelled": "yodeled",
-    "yodelling": "yodeling",
-    "yoghourt": "yogurt",
-    "yoghourts": "yogurts",
-    "yoghurt": "yogurt",
-    "yoghurts": "yogurts",
-    "mhm": "hmm",
-    "mmm": "hmm"
-}
\ No newline at end of file
diff --git a/whisperx/normalizers/english.py b/whisperx/normalizers/english.py
deleted file mode 100644
index d5c2bb4..0000000
--- a/whisperx/normalizers/english.py
+++ /dev/null
@@ -1,543 +0,0 @@
-import json
-import os
-import re
-from fractions import Fraction
-from typing import Iterator, List, Match, Optional, Union
-
-from more_itertools import windowed
-
-from .basic import remove_symbols_and_diacritics
-
-
-class EnglishNumberNormalizer:
-    """
-    Convert any spelled-out numbers into arabic numbers, while handling:
-
-    - remove any commas
-    - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
-    - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
-    - spell out `one` and `ones`
-    - interpret successive single-digit numbers as nominal: `one oh one` -> `101`
-    """
-
-    def __init__(self):
-        super().__init__()
-
-        self.zeros = {"o", "oh", "zero"}
-        self.ones = {
-            name: i
-            for i, name in enumerate(
-                [
-                    "one",
-                    "two",
-                    "three",
-                    "four",
-                    "five",
-                    "six",
-                    "seven",
-                    "eight",
-                    "nine",
-                    "ten",
-                    "eleven",
-                    "twelve",
-                    "thirteen",
-                    "fourteen",
-                    "fifteen",
-                    "sixteen",
-                    "seventeen",
-                    "eighteen",
-                    "nineteen",
-                ],
-                start=1,
-            )
-        }
-        self.ones_plural = {
-            "sixes" if name == "six" else name + "s": (value, "s")
-            for name, value in self.ones.items()
-        }
-        self.ones_ordinal = {
-            "zeroth": (0, "th"),
-            "first": (1, "st"),
-            "second": (2, "nd"),
-            "third": (3, "rd"),
-            "fifth": (5, "th"),
-            "twelfth": (12, "th"),
-            **{
-                name + ("h" if name.endswith("t") else "th"): (value, "th")
-                for name, value in self.ones.items()
-                if value > 3 and value != 5 and value != 12
-            },
-        }
-        self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal}
-
-        self.tens = {
-            "twenty": 20,
-            "thirty": 30,
-            "forty": 40,
-            "fifty": 50,
-            "sixty": 60,
-            "seventy": 70,
-            "eighty": 80,
-            "ninety": 90,
-        }
-        self.tens_plural = {
-            name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()
-        }
-        self.tens_ordinal = {
-            name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()
-        }
-        self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal}
-
-        self.multipliers = {
-            "hundred": 100,
-            "thousand": 1_000,
-            "million": 1_000_000,
-            "billion": 1_000_000_000,
-            "trillion": 1_000_000_000_000,
-            "quadrillion": 1_000_000_000_000_000,
-            "quintillion": 1_000_000_000_000_000_000,
-            "sextillion": 1_000_000_000_000_000_000_000,
-            "septillion": 1_000_000_000_000_000_000_000_000,
-            "octillion": 1_000_000_000_000_000_000_000_000_000,
-            "nonillion": 1_000_000_000_000_000_000_000_000_000_000,
-            "decillion": 1_000_000_000_000_000_000_000_000_000_000_000,
-        }
-        self.multipliers_plural = {
-            name + "s": (value, "s") for name, value in self.multipliers.items()
-        }
-        self.multipliers_ordinal = {
-            name + "th": (value, "th") for name, value in self.multipliers.items()
-        }
-        self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal}
-        self.decimals = {*self.ones, *self.tens, *self.zeros}
-
-        self.preceding_prefixers = {
-            "minus": "-",
-            "negative": "-",
-            "plus": "+",
-            "positive": "+",
-        }
-        self.following_prefixers = {
-            "pound": "£",
-            "pounds": "£",
-            "euro": "€",
-            "euros": "€",
-            "dollar": "$",
-            "dollars": "$",
-            "cent": "¢",
-            "cents": "¢",
-        }
-        self.prefixes = set(
-            list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())
-        )
-        self.suffixers = {
-            "per": {"cent": "%"},
-            "percent": "%",
-        }
-        self.specials = {"and", "double", "triple", "point"}
-
-        self.words = set(
-            [
-                key
-                for mapping in [
-                    self.zeros,
-                    self.ones,
-                    self.ones_suffixed,
-                    self.tens,
-                    self.tens_suffixed,
-                    self.multipliers,
-                    self.multipliers_suffixed,
-                    self.preceding_prefixers,
-                    self.following_prefixers,
-                    self.suffixers,
-                    self.specials,
-                ]
-                for key in mapping
-            ]
-        )
-        self.literal_words = {"one", "ones"}
-
-    def process_words(self, words: List[str]) -> Iterator[str]:
-        prefix: Optional[str] = None
-        value: Optional[Union[str, int]] = None
-        skip = False
-
-        def to_fraction(s: str):
-            try:
-                return Fraction(s)
-            except ValueError:
-                return None
-
-        def output(result: Union[str, int]):
-            nonlocal prefix, value
-            result = str(result)
-            if prefix is not None:
-                result = prefix + result
-            value = None
-            prefix = None
-            return result
-
-        if len(words) == 0:
-            return
-
-        for prev, current, next in windowed([None] + words + [None], 3):
-            if skip:
-                skip = False
-                continue
-
-            next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
-            has_prefix = current[0] in self.prefixes
-            current_without_prefix = current[1:] if has_prefix else current
-            if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
-                # arabic numbers (potentially with signs and fractions)
-                f = to_fraction(current_without_prefix)
-                assert f is not None
-                if value is not None:
-                    if isinstance(value, str) and value.endswith("."):
-                        # concatenate decimals / ip address components
-                        value = str(value) + str(current)
-                        continue
-                    else:
-                        yield output(value)
-
-                prefix = current[0] if has_prefix else prefix
-                if f.denominator == 1:
-                    value = f.numerator  # store integers as int
-                else:
-                    value = current_without_prefix
-            elif current not in self.words:
-                # non-numeric words
-                if value is not None:
-                    yield output(value)
-                yield output(current)
-            elif current in self.zeros:
-                value = str(value or "") + "0"
-            elif current in self.ones:
-                ones = self.ones[current]
-
-                if value is None:
-                    value = ones
-                elif isinstance(value, str) or prev in self.ones:
-                    if prev in self.tens and ones < 10:  # replace the last zero with the digit
-                        assert value[-1] == "0"
-                        value = value[:-1] + str(ones)
-                    else:
-                        value = str(value) + str(ones)
-                elif ones < 10:
-                    if value % 10 == 0:
-                        value += ones
-                    else:
-                        value = str(value) + str(ones)
-                else:  # eleven to nineteen
-                    if value % 100 == 0:
-                        value += ones
-                    else:
-                        value = str(value) + str(ones)
-            elif current in self.ones_suffixed:
-                # ordinal or cardinal; yield the number right away
-                ones, suffix = self.ones_suffixed[current]
-                if value is None:
-                    yield output(str(ones) + suffix)
-                elif isinstance(value, str) or prev in self.ones:
-                    if prev in self.tens and ones < 10:
-                        assert value[-1] == "0"
-                        yield output(value[:-1] + str(ones) + suffix)
-                    else:
-                        yield output(str(value) + str(ones) + suffix)
-                elif ones < 10:
-                    if value % 10 == 0:
-                        yield output(str(value + ones) + suffix)
-                    else:
-                        yield output(str(value) + str(ones) + suffix)
-                else:  # eleven to nineteen
-                    if value % 100 == 0:
-                        yield output(str(value + ones) + suffix)
-                    else:
-                        yield output(str(value) + str(ones) + suffix)
-                value = None
-            elif current in self.tens:
-                tens = self.tens[current]
-                if value is None:
-                    value = tens
-                elif isinstance(value, str):
-                    value = str(value) + str(tens)
-                else:
-                    if value % 100 == 0:
-                        value += tens
-                    else:
-                        value = str(value) + str(tens)
-            elif current in self.tens_suffixed:
-                # ordinal or cardinal; yield the number right away
-                tens, suffix = self.tens_suffixed[current]
-                if value is None:
-                    yield output(str(tens) + suffix)
-                elif isinstance(value, str):
-                    yield output(str(value) + str(tens) + suffix)
-                else:
-                    if value % 100 == 0:
-                        yield output(str(value + tens) + suffix)
-                    else:
-                        yield output(str(value) + str(tens) + suffix)
-            elif current in self.multipliers:
-                multiplier = self.multipliers[current]
-                if value is None:
-                    value = multiplier
-                elif isinstance(value, str) or value == 0:
-                    f = to_fraction(value)
-                    p = f * multiplier if f is not None else None
-                    if f is not None and p.denominator == 1:
-                        value = p.numerator
-                    else:
-                        yield output(value)
-                        value = multiplier
-                else:
-                    before = value // 1000 * 1000
-                    residual = value % 1000
-                    value = before + residual * multiplier
-            elif current in self.multipliers_suffixed:
-                multiplier, suffix = self.multipliers_suffixed[current]
-                if value is None:
-                    yield output(str(multiplier) + suffix)
-                elif isinstance(value, str):
-                    f = to_fraction(value)
-                    p = f * multiplier if f is not None else None
-                    if f is not None and p.denominator == 1:
-                        yield output(str(p.numerator) + suffix)
-                    else:
-                        yield output(value)
-                        yield output(str(multiplier) + suffix)
-                else:  # int
-                    before = value // 1000 * 1000
-                    residual = value % 1000
-                    value = before + residual * multiplier
-                    yield output(str(value) + suffix)
-                value = None
-            elif current in self.preceding_prefixers:
-                # apply prefix (positive, minus, etc.) if it precedes a number
-                if value is not None:
-                    yield output(value)
-
-                if next in self.words or next_is_numeric:
-                    prefix = self.preceding_prefixers[current]
-                else:
-                    yield output(current)
-            elif current in self.following_prefixers:
-                # apply prefix (dollars, cents, etc.) only after a number
-                if value is not None:
-                    prefix = self.following_prefixers[current]
-                    yield output(value)
-                else:
-                    yield output(current)
-            elif current in self.suffixers:
-                # apply suffix symbols (percent -> '%')
-                if value is not None:
-                    suffix = self.suffixers[current]
-                    if isinstance(suffix, dict):
-                        if next in suffix:
-                            yield output(str(value) + suffix[next])
-                            skip = True
-                        else:
-                            yield output(value)
-                            yield output(current)
-                    else:
-                        yield output(str(value) + suffix)
-                else:
-                    yield output(current)
-            elif current in self.specials:
-                if next not in self.words and not next_is_numeric:
-                    # apply special handling only if the next word can be numeric
-                    if value is not None:
-                        yield output(value)
-                    yield output(current)
-                elif current == "and":
-                    # ignore "and" after hundreds, thousands, etc.
-                    if prev not in self.multipliers:
-                        if value is not None:
-                            yield output(value)
-                        yield output(current)
-                elif current == "double" or current == "triple":
-                    if next in self.ones or next in self.zeros:
-                        repeats = 2 if current == "double" else 3
-                        ones = self.ones.get(next, 0)
-                        value = str(value or "") + str(ones) * repeats
-                        skip = True
-                    else:
-                        if value is not None:
-                            yield output(value)
-                        yield output(current)
-                elif current == "point":
-                    if next in self.decimals or next_is_numeric:
-                        value = str(value or "") + "."
-                else:
-                    # should all have been covered at this point
-                    raise ValueError(f"Unexpected token: {current}")
-            else:
-                # all should have been covered at this point
-                raise ValueError(f"Unexpected token: {current}")
-
-        if value is not None:
-            yield output(value)
-
-    def preprocess(self, s: str):
-        # replace "<number> and a half" with "<number> point five"
-        results = []
-
-        segments = re.split(r"\band\s+a\s+half\b", s)
-        for i, segment in enumerate(segments):
-            if len(segment.strip()) == 0:
-                continue
-            if i == len(segments) - 1:
-                results.append(segment)
-            else:
-                results.append(segment)
-                last_word = segment.rsplit(maxsplit=2)[-1]
-                if last_word in self.decimals or last_word in self.multipliers:
-                    results.append("point five")
-                else:
-                    results.append("and a half")
-
-        s = " ".join(results)
-
-        # put a space at number/letter boundary
-        s = re.sub(r"([a-z])([0-9])", r"\1 \2", s)
-        s = re.sub(r"([0-9])([a-z])", r"\1 \2", s)
-
-        # but remove spaces which could be a suffix
-        s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
-
-        return s
-
-    def postprocess(self, s: str):
-        def combine_cents(m: Match):
-            try:
-                currency = m.group(1)
-                integer = m.group(2)
-                cents = int(m.group(3))
-                return f"{currency}{integer}.{cents:02d}"
-            except ValueError:
-                return m.string
-
-        def extract_cents(m: Match):
-            try:
-                return f"¢{int(m.group(1))}"
-            except ValueError:
-                return m.string
-
-        # apply currency postprocessing; "$2 and ¢7" -> "$2.07"
-        s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s)
-        s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s)
-
-        # write "one(s)" instead of "1(s)", just for the readability
-        s = re.sub(r"\b1(s?)\b", r"one\1", s)
-
-        return s
-
-    def __call__(self, s: str):
-        s = self.preprocess(s)
-        s = " ".join(word for word in self.process_words(s.split()) if word is not None)
-        s = self.postprocess(s)
-
-        return s
-
-
-class EnglishSpellingNormalizer:
-    """
-    Applies British-American spelling mappings as listed in [1].
-
-    [1] https://www.tysto.com/uk-us-spelling-list.html
-    """
-
-    def __init__(self):
-        mapping_path = os.path.join(os.path.dirname(__file__), "english.json")
-        self.mapping = json.load(open(mapping_path))
-
-    def __call__(self, s: str):
-        return " ".join(self.mapping.get(word, word) for word in s.split())
-
-
-class EnglishTextNormalizer:
-    def __init__(self):
-        self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
-        self.replacers = {
-            # common contractions
-            r"\bwon't\b": "will not",
-            r"\bcan't\b": "can not",
-            r"\blet's\b": "let us",
-            r"\bain't\b": "aint",
-            r"\by'all\b": "you all",
-            r"\bwanna\b": "want to",
-            r"\bgotta\b": "got to",
-            r"\bgonna\b": "going to",
-            r"\bi'ma\b": "i am going to",
-            r"\bimma\b": "i am going to",
-            r"\bwoulda\b": "would have",
-            r"\bcoulda\b": "could have",
-            r"\bshoulda\b": "should have",
-            r"\bma'am\b": "madam",
-            # contractions in titles/prefixes
-            r"\bmr\b": "mister ",
-            r"\bmrs\b": "missus ",
-            r"\bst\b": "saint ",
-            r"\bdr\b": "doctor ",
-            r"\bprof\b": "professor ",
-            r"\bcapt\b": "captain ",
-            r"\bgov\b": "governor ",
-            r"\bald\b": "alderman ",
-            r"\bgen\b": "general ",
-            r"\bsen\b": "senator ",
-            r"\brep\b": "representative ",
-            r"\bpres\b": "president ",
-            r"\brev\b": "reverend ",
-            r"\bhon\b": "honorable ",
-            r"\basst\b": "assistant ",
-            r"\bassoc\b": "associate ",
-            r"\blt\b": "lieutenant ",
-            r"\bcol\b": "colonel ",
-            r"\bjr\b": "junior ",
-            r"\bsr\b": "senior ",
-            r"\besq\b": "esquire ",
-            # prefect tenses, ideally it should be any past participles, but it's harder..
-            r"'d been\b": " had been",
-            r"'s been\b": " has been",
-            r"'d gone\b": " had gone",
-            r"'s gone\b": " has gone",
-            r"'d done\b": " had done",  # "'s done" is ambiguous
-            r"'s got\b": " has got",
-            # general contractions
-            r"n't\b": " not",
-            r"'re\b": " are",
-            r"'s\b": " is",
-            r"'d\b": " would",
-            r"'ll\b": " will",
-            r"'t\b": " not",
-            r"'ve\b": " have",
-            r"'m\b": " am",
-        }
-        self.standardize_numbers = EnglishNumberNormalizer()
-        self.standardize_spellings = EnglishSpellingNormalizer()
-
-    def __call__(self, s: str):
-        s = s.lower()
-
-        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
-        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
-        s = re.sub(self.ignore_patterns, "", s)
-        s = re.sub(r"\s+'", "'", s)  # standardize when there's a space before an apostrophe
-
-        for pattern, replacement in self.replacers.items():
-            s = re.sub(pattern, replacement, s)
-
-        s = re.sub(r"(\d),(\d)", r"\1\2", s)  # remove commas between digits
-        s = re.sub(r"\.([^0-9]|$)", r" \1", s)  # remove periods not followed by numbers
-        s = remove_symbols_and_diacritics(s, keep=".%$¢€£")  # keep some symbols for numerics
-
-        s = self.standardize_numbers(s)
-        s = self.standardize_spellings(s)
-
-        # now remove prefix/suffix symbols that are not preceded/followed by numbers
-        s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s)
-        s = re.sub(r"([^0-9])%", r"\1 ", s)
-
-        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
-
-        return s
diff --git a/whisperx/tokenizer.py b/whisperx/tokenizer.py
deleted file mode 100644
index a27cb35..0000000
--- a/whisperx/tokenizer.py
+++ /dev/null
@@ -1,331 +0,0 @@
-import os
-from dataclasses import dataclass
-from functools import lru_cache
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from transformers import GPT2TokenizerFast
-
-LANGUAGES = {
-    "en": "english",
-    "zh": "chinese",
-    "de": "german",
-    "es": "spanish",
-    "ru": "russian",
-    "ko": "korean",
-    "fr": "french",
-    "ja": "japanese",
-    "pt": "portuguese",
-    "tr": "turkish",
-    "pl": "polish",
-    "ca": "catalan",
-    "nl": "dutch",
-    "ar": "arabic",
-    "sv": "swedish",
-    "it": "italian",
-    "id": "indonesian",
-    "hi": "hindi",
-    "fi": "finnish",
-    "vi": "vietnamese",
-    "he": "hebrew",
-    "uk": "ukrainian",
-    "el": "greek",
-    "ms": "malay",
-    "cs": "czech",
-    "ro": "romanian",
-    "da": "danish",
-    "hu": "hungarian",
-    "ta": "tamil",
-    "no": "norwegian",
-    "th": "thai",
-    "ur": "urdu",
-    "hr": "croatian",
-    "bg": "bulgarian",
-    "lt": "lithuanian",
-    "la": "latin",
-    "mi": "maori",
-    "ml": "malayalam",
-    "cy": "welsh",
-    "sk": "slovak",
-    "te": "telugu",
-    "fa": "persian",
-    "lv": "latvian",
-    "bn": "bengali",
-    "sr": "serbian",
-    "az": "azerbaijani",
-    "sl": "slovenian",
-    "kn": "kannada",
-    "et": "estonian",
-    "mk": "macedonian",
-    "br": "breton",
-    "eu": "basque",
-    "is": "icelandic",
-    "hy": "armenian",
-    "ne": "nepali",
-    "mn": "mongolian",
-    "bs": "bosnian",
-    "kk": "kazakh",
-    "sq": "albanian",
-    "sw": "swahili",
-    "gl": "galician",
-    "mr": "marathi",
-    "pa": "punjabi",
-    "si": "sinhala",
-    "km": "khmer",
-    "sn": "shona",
-    "yo": "yoruba",
-    "so": "somali",
-    "af": "afrikaans",
-    "oc": "occitan",
-    "ka": "georgian",
-    "be": "belarusian",
-    "tg": "tajik",
-    "sd": "sindhi",
-    "gu": "gujarati",
-    "am": "amharic",
-    "yi": "yiddish",
-    "lo": "lao",
-    "uz": "uzbek",
-    "fo": "faroese",
-    "ht": "haitian creole",
-    "ps": "pashto",
-    "tk": "turkmen",
-    "nn": "nynorsk",
-    "mt": "maltese",
-    "sa": "sanskrit",
-    "lb": "luxembourgish",
-    "my": "myanmar",
-    "bo": "tibetan",
-    "tl": "tagalog",
-    "mg": "malagasy",
-    "as": "assamese",
-    "tt": "tatar",
-    "haw": "hawaiian",
-    "ln": "lingala",
-    "ha": "hausa",
-    "ba": "bashkir",
-    "jw": "javanese",
-    "su": "sundanese",
-}
-
-# language code lookup by name, with a few language aliases
-TO_LANGUAGE_CODE = {
-    **{language: code for code, language in LANGUAGES.items()},
-    "burmese": "my",
-    "valencian": "ca",
-    "flemish": "nl",
-    "haitian": "ht",
-    "letzeburgesch": "lb",
-    "pushto": "ps",
-    "panjabi": "pa",
-    "moldavian": "ro",
-    "moldovan": "ro",
-    "sinhalese": "si",
-    "castilian": "es",
-}
-
-
-@dataclass(frozen=True)
-class Tokenizer:
-    """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
-
-    tokenizer: "GPT2TokenizerFast"
-    language: Optional[str]
-    sot_sequence: Tuple[int]
-
-    def encode(self, text, **kwargs):
-        return self.tokenizer.encode(text, **kwargs)
-
-    def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs):
-        return self.tokenizer.decode(token_ids, **kwargs)
-
-    def decode_with_timestamps(self, tokens) -> str:
-        """
-        Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
-        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
-        """
-        outputs = [[]]
-        for token in tokens:
-            if token >= self.timestamp_begin:
-                timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
-                outputs.append(timestamp)
-                outputs.append([])
-            else:
-                outputs[-1].append(token)
-        outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
-        return "".join(outputs)
-
-    @property
-    @lru_cache()
-    def eot(self) -> int:
-        return self.tokenizer.eos_token_id
-
-    @property
-    @lru_cache()
-    def sot(self) -> int:
-        return self._get_single_token_id("<|startoftranscript|>")
-
-    @property
-    @lru_cache()
-    def sot_lm(self) -> int:
-        return self._get_single_token_id("<|startoflm|>")
-
-    @property
-    @lru_cache()
-    def sot_prev(self) -> int:
-        return self._get_single_token_id("<|startofprev|>")
-
-    @property
-    @lru_cache()
-    def no_speech(self) -> int:
-        return self._get_single_token_id("<|nospeech|>")
-
-    @property
-    @lru_cache()
-    def no_timestamps(self) -> int:
-        return self._get_single_token_id("<|notimestamps|>")
-
-    @property
-    @lru_cache()
-    def timestamp_begin(self) -> int:
-        return self.tokenizer.all_special_ids[-1] + 1
-
-    @property
-    @lru_cache()
-    def language_token(self) -> int:
-        """Returns the token id corresponding to the value of the `language` field"""
-        if self.language is None:
-            raise ValueError(f"This tokenizer does not have language token configured")
-
-        additional_tokens = dict(
-            zip(
-                self.tokenizer.additional_special_tokens,
-                self.tokenizer.additional_special_tokens_ids,
-            )
-        )
-        candidate = f"<|{self.language}|>"
-        if candidate in additional_tokens:
-            return additional_tokens[candidate]
-
-        raise KeyError(f"Language {self.language} not found in tokenizer.")
-
-    @property
-    @lru_cache()
-    def all_language_tokens(self) -> Tuple[int]:
-        result = []
-        for token, token_id in zip(
-            self.tokenizer.additional_special_tokens,
-            self.tokenizer.additional_special_tokens_ids,
-        ):
-            if token.strip("<|>") in LANGUAGES:
-                result.append(token_id)
-        return tuple(result)
-
-    @property
-    @lru_cache()
-    def all_language_codes(self) -> Tuple[str]:
-        return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
-
-    @property
-    @lru_cache()
-    def sot_sequence_including_notimestamps(self) -> Tuple[int]:
-        return tuple(list(self.sot_sequence) + [self.no_timestamps])
-
-    @property
-    @lru_cache()
-    def non_speech_tokens(self) -> Tuple[int]:
-        """
-        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
-        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
-
-        - ♪♪♪
-        - ( SPEAKING FOREIGN LANGUAGE )
-        - [DAVID] Hey there,
-
-        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
-        """
-        symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
-        symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
-
-        # symbols that may be a single token or multiple tokens depending on the tokenizer.
-        # In case they're multiple tokens, suppress the first token, which is safe because:
-        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
-        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
-        miscellaneous = set("♩♪♫♬♭♮♯")
-        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
-
-        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
-        result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
-        for symbol in symbols + list(miscellaneous):
-            for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
-                if len(tokens) == 1 or symbol in miscellaneous:
-                    result.add(tokens[0])
-
-        return tuple(sorted(result))
-
-    def _get_single_token_id(self, text) -> int:
-        tokens = self.tokenizer.encode(text)
-        assert len(tokens) == 1, f"{text} is not encoded as a single token"
-        return tokens[0]
-
-
-@lru_cache(maxsize=None)
-def build_tokenizer(name: str = "gpt2"):
-    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-    path = os.path.join(os.path.dirname(__file__), "assets", name)
-    tokenizer = GPT2TokenizerFast.from_pretrained(path)
-
-    specials = [
-        "<|startoftranscript|>",
-        *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
-        "<|translate|>",
-        "<|transcribe|>",
-        "<|startoflm|>",
-        "<|startofprev|>",
-        "<|nospeech|>",
-        "<|notimestamps|>",
-    ]
-
-    tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
-    return tokenizer
-
-
-@lru_cache(maxsize=None)
-def get_tokenizer(
-    multilingual: bool,
-    *,
-    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
-    language: Optional[str] = None,
-) -> Tokenizer:
-    if language is not None:
-        language = language.lower()
-        if language not in LANGUAGES:
-            if language in TO_LANGUAGE_CODE:
-                language = TO_LANGUAGE_CODE[language]
-            else:
-                raise ValueError(f"Unsupported language: {language}")
-
-    if multilingual:
-        tokenizer_name = "multilingual"
-        task = task or "transcribe"
-        language = language or "en"
-    else:
-        tokenizer_name = "gpt2"
-        task = None
-        language = None
-
-    tokenizer = build_tokenizer(name=tokenizer_name)
-    all_special_ids: List[int] = tokenizer.all_special_ids
-    sot: int = all_special_ids[1]
-    translate: int = all_special_ids[-6]
-    transcribe: int = all_special_ids[-5]
-
-    langs = tuple(LANGUAGES.keys())
-    sot_sequence = [sot]
-    if language is not None:
-        sot_sequence.append(sot + 1 + langs.index(language))
-    if task is not None:
-        sot_sequence.append(transcribe if task == "transcribe" else translate)
-
-    return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 2efec7a..4461dfb 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -1,602 +1,58 @@
 import argparse
 import os
 import warnings
-from typing import List, Optional, Tuple, Union, Iterator, TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional, Tuple, Union
 
 import numpy as np
 import torch
-import tqdm
-from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, CHUNK_LENGTH, pad_or_trim, log_mel_spectrogram, load_audio
-from .alignment import load_align_model, align, get_trellis, backtrack, merge_repeats, merge_words
-from .decoding import DecodingOptions, DecodingResult
-from .diarize import assign_word_speakers, Segment
-from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
-from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, interpolate_nans, write_txt, write_vtt, write_srt, write_ass, write_tsv
-from .vad import Binarize
-import pandas as pd
 
-if TYPE_CHECKING:
-    from .model import Whisper
+from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE
+from whisper.utils import (
+    optional_float,
+    optional_int,
+    str2bool,
+)
 
+from .utils import get_writer
 
-
-def transcribe(
-    model: "Whisper",
-    audio: Union[str, np.ndarray, torch.Tensor],
-    *,
-    verbose: Optional[bool] = None,
-    temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
-    compression_ratio_threshold: Optional[float] = 2.4,
-    logprob_threshold: Optional[float] = -1.0,
-    no_speech_threshold: Optional[float] = 0.6,
-    condition_on_previous_text: bool = False, # turn off by default due to errors it causes
-    mel: np.ndarray = None,
-    **decode_options,
-):
-    """
-    Transcribe an audio file using Whisper
-
-    Parameters
-    ----------
-    model: Whisper
-        The Whisper model instance
-
-    audio: Union[str, np.ndarray, torch.Tensor]
-        The path to the audio file to open, or the audio waveform
-
-    verbose: bool
-        Whether to display the text being decoded to the console. If True, displays all the details,
-        If False, displays minimal details. If None, does not display anything
-
-    temperature: Union[float, Tuple[float, ...]]
-        Temperature for sampling. It can be a tuple of temperatures, which will be successfully used
-        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
-
-    compression_ratio_threshold: float
-        If the gzip compression ratio is above this value, treat as failed
-
-    logprob_threshold: float
-        If the average log probability over sampled tokens is below this value, treat as failed
-
-    no_speech_threshold: float
-        If the no_speech probability is higher than this value AND the average log probability
-        over sampled tokens is below `logprob_threshold`, consider the segment as silent
-
-    condition_on_previous_text: bool
-        if True, the previous output of the model is provided as a prompt for the next window;
-        disabling may make the text inconsistent across windows, but the model becomes less prone to
-        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
-
-    decode_options: dict
-        Keyword arguments to construct `DecodingOptions` instances
-
-    Returns
-    -------
-    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
-    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
-    """
-    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
-    if model.device == torch.device("cpu"):
-        if torch.cuda.is_available():
-            warnings.warn("Performing inference on CPU when CUDA is available")
-        if dtype == torch.float16:
-            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
-            dtype = torch.float32
-
-    if dtype == torch.float32:
-        decode_options["fp16"] = False
-
-    if mel is None:
-        mel = log_mel_spectrogram(audio)
-
-    if decode_options.get("language", None) is None:
-        if not model.is_multilingual:
-            decode_options["language"] = "en"
-        else:
-            if verbose:
-                print("Detecting language using up to the first 30 seconds. Use `--language` to specify the language")
-            segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
-            _, probs = model.detect_language(segment)
-            decode_options["language"] = max(probs, key=probs.get)
-            if verbose is not None:
-                print(f"Detected language: {LANGUAGES[decode_options['language']].title()}")
-
-    language = decode_options["language"]
-    task = decode_options.get("task", "transcribe")
-    tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
-
-    def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
-        temperatures = [temperature] if isinstance(temperature, (int, float)) else temperature
-        decode_result = None
-
-        for t in temperatures:
-            kwargs = {**decode_options}
-            if t > 0:
-                # disable beam_size and patience when t > 0
-                kwargs.pop("beam_size", None)
-                kwargs.pop("patience", None)
-            else:
-                # disable best_of when t == 0
-                kwargs.pop("best_of", None)
-
-            options = DecodingOptions(**kwargs, temperature=t)
-            decode_result = model.decode(segment, options)
-
-            needs_fallback = False
-            if compression_ratio_threshold is not None and decode_result.compression_ratio > compression_ratio_threshold:
-                needs_fallback = True  # too repetitive
-            if logprob_threshold is not None and decode_result.avg_logprob < logprob_threshold:
-                needs_fallback = True  # average log probability is too low
-
-            if not needs_fallback:
-                break
-
-        return decode_result
-
-    seek = 0
-    input_stride = exact_div(
-        N_FRAMES, model.dims.n_audio_ctx
-    )  # mel frames per output token: 2
-    time_precision = (
-        input_stride * HOP_LENGTH / SAMPLE_RATE
-    )  # time per output token: 0.02 (seconds)
-    all_tokens = []
-    all_segments = []
-    prompt_reset_since = 0
-
-    initial_prompt = decode_options.pop("initial_prompt", None) or []
-    if initial_prompt:
-        initial_prompt = tokenizer.encode(" " + initial_prompt.strip())
-        all_tokens.extend(initial_prompt)
-
-    def add_segment(
-        *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult
-    ):
-        text = tokenizer.decode([token for token in text_tokens if token < tokenizer.eot])
-        if len(text.strip()) == 0:  # skip empty text output
-            return
-
-        all_segments.append(
-            {
-                "id": len(all_segments),
-                "seek": seek,
-                "start": start,
-                "end": end,
-                "text": text,
-                "tokens": text_tokens.tolist(),
-                "temperature": result.temperature,
-                "avg_logprob": result.avg_logprob,
-                "compression_ratio": result.compression_ratio,
-                "no_speech_prob": result.no_speech_prob,
-            }
-        )
-        if verbose:
-            print(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}")
-
-    # show the progress bar when verbose is False (otherwise the transcribed text will be printed)
-    num_frames = mel.shape[-1]
-    previous_seek_value = seek
-
-    with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar:
-        while seek < num_frames:
-            timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
-            segment = pad_or_trim(mel[:, seek:], N_FRAMES).to(model.device).to(dtype)
-            segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE
-
-            decode_options["prompt"] = all_tokens[prompt_reset_since:]
-            result: DecodingResult = decode_with_fallback(segment)
-            tokens = torch.tensor(result.tokens)
-
-            if no_speech_threshold is not None:
-                # no voice activity check
-                should_skip = result.no_speech_prob > no_speech_threshold
-                if logprob_threshold is not None and result.avg_logprob > logprob_threshold:
-                    # don't skip if the logprob is high enough, despite the no_speech_prob
-                    should_skip = False
-
-                if should_skip:
-                    seek += segment.shape[-1]  # fast-forward to the next segment boundary
-                    continue
-
-            timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
-            consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0].add_(1)
-            if len(consecutive) > 0:  # if the output contains two consecutive timestamp tokens
-                last_slice = 0
-                for current_slice in consecutive:
-                    sliced_tokens = tokens[last_slice:current_slice]
-                    start_timestamp_position = (
-                        sliced_tokens[0].item() - tokenizer.timestamp_begin
-                    )
-                    end_timestamp_position = (
-                        sliced_tokens[-1].item() - tokenizer.timestamp_begin
-                    )
-
-                    # clamp end-time to at least be 1 frame after start-time
-                    end_timestamp_position = max(end_timestamp_position, start_timestamp_position + time_precision)
-
-                    add_segment(
-                        start=timestamp_offset + start_timestamp_position * time_precision,
-                        end=timestamp_offset + end_timestamp_position * time_precision,
-                        text_tokens=sliced_tokens[1:-1],
-                        result=result,
-                    )
-                    last_slice = current_slice
-                last_timestamp_position = (
-                    tokens[last_slice - 1].item() - tokenizer.timestamp_begin
-                )
-                seek += last_timestamp_position * input_stride
-                all_tokens.extend(tokens[: last_slice + 1].tolist())
-            else:
-                duration = segment_duration
-                timestamps = tokens[timestamp_tokens.nonzero().flatten()]
-                if len(timestamps) > 0 and timestamps[-1].item() != tokenizer.timestamp_begin:
-                    # no consecutive timestamps but it has a timestamp; use the last one.
-                    # single timestamp at the end means no speech after the last timestamp.
-                    last_timestamp_position = timestamps[-1].item() - tokenizer.timestamp_begin
-                    duration = last_timestamp_position * time_precision
-
-                add_segment(
-                    start=timestamp_offset,
-                    end=timestamp_offset + duration,
-                    text_tokens=tokens,
-                    result=result,
-                )
-
-                seek += segment.shape[-1]
-                all_tokens.extend(tokens.tolist())
-
-            if not condition_on_previous_text or result.temperature > 0.5:
-                # do not feed the prompt tokens if a high temperature was used
-                prompt_reset_since = len(all_tokens)
-
-            # update progress bar
-            pbar.update(min(num_frames, seek) - previous_seek_value)
-            previous_seek_value = seek
-
-    return dict(text=tokenizer.decode(all_tokens[len(initial_prompt):]), segments=all_segments, language=language)
-
-
-def merge_chunks(segments, chunk_size=CHUNK_LENGTH):
-    """
-    Merge VAD segments into larger segments of approximately size ~CHUNK_LENGTH.
-    TODO: Make sure VAD segment isn't too long, otherwise it will cause OOM when input to alignment model
-    TODO: Or sliding window alignment model over long segment.
-    """
-    curr_end = 0
-    merged_segments = []
-    seg_idxs = []
-    speaker_idxs = []
-
-    assert chunk_size > 0
-    binarize = Binarize(max_duration=chunk_size)
-    segments = binarize(segments)
-    segments_list = []
-    for speech_turn in segments.get_timeline():
-        segments_list.append(Segment(speech_turn.start, speech_turn.end, "UNKNOWN"))
-
-    assert segments_list, "segments_list is empty."
-    # Make sur the starting point is the start of the segment.
-    curr_start = segments_list[0].start
-
-    for seg in segments_list:
-        if seg.end - curr_start > chunk_size and curr_end-curr_start > 0:
-            merged_segments.append({
-                "start": curr_start,
-                "end": curr_end,
-                "segments": seg_idxs,
-            })
-            curr_start = seg.start
-            seg_idxs = []
-            speaker_idxs = []
-        curr_end = seg.end
-        seg_idxs.append((seg.start, seg.end))
-        speaker_idxs.append(seg.speaker)
-    # add final
-    merged_segments.append({ 
-                "start": curr_start,
-                "end": curr_end,
-                "segments": seg_idxs,
-            })    
-    return merged_segments
-
-
-def transcribe_with_vad(
-    model: "Whisper",
-    audio: Union[str, np.ndarray, torch.Tensor],
-    vad_pipeline,
-    mel = None,
-    verbose: Optional[bool] = None,
-    **kwargs
-):
-    """
-    Transcribe per VAD segment
-    """
-
-    if mel is None:
-        mel = log_mel_spectrogram(audio)
-    
-    prev = 0
-    output = {"segments": []}
-
-    vad_segments = vad_pipeline(audio)
-    # merge segments to approx 30s inputs to make whisper most appropraite
-    vad_segments = merge_chunks(vad_segments)
-
-    for sdx, seg_t in enumerate(vad_segments):
-        if verbose:
-            print(f"~~ Transcribing VAD chunk: ({format_timestamp(seg_t['start'])} --> {format_timestamp(seg_t['end'])}) ~~")
-        seg_f_start, seg_f_end = int(seg_t["start"] * SAMPLE_RATE / HOP_LENGTH), int(seg_t["end"] * SAMPLE_RATE / HOP_LENGTH)
-        local_f_start, local_f_end = seg_f_start - prev, seg_f_end - prev
-        mel = mel[:, local_f_start:] # seek forward
-        prev = seg_f_start
-        local_mel = mel[:, :local_f_end-local_f_start]
-        result = transcribe(model, audio, mel=local_mel, verbose=verbose, **kwargs)
-        seg_t["text"] = result["text"]
-        output["segments"].append(
-            {
-                "start": seg_t["start"],
-                "end": seg_t["end"],
-                "language": result["language"],
-                "text": result["text"],
-                "seg-text": [x["text"] for x in result["segments"]],
-                "seg-start": [x["start"] for x in result["segments"]],
-                "seg-end": [x["end"] for x in result["segments"]],
-                }
-            )
-
-    output["language"] = output["segments"][0]["language"]
-
-    return output
-
-
-def transcribe_with_vad_parallel(
-    model: "Whisper",
-    audio: Union[str, np.ndarray, torch.Tensor],
-    vad_pipeline,
-    mel = None,
-    verbose: Optional[bool] = None,
-    batch_size = -1,
-    **kwargs
-):
-    """
-    Transcribe per VAD segment
-    """
-
-    if mel is None:
-        mel = log_mel_spectrogram(audio)
-    
-    vad_segments = vad_pipeline(audio)
-    # merge segments to approx 30s inputs to make whisper most appropraite
-    vad_segments = merge_chunks(vad_segments)
-
-    ################################
-    ### START of parallelization ###
-    ################################
-
-    # pad mel to a same length
-    start_seconds = [i['start'] for i in vad_segments]
-    end_seconds = [i['end'] for i in vad_segments]
-    duration_list = np.array(end_seconds) - np.array(start_seconds)
-    max_length = round(30 / (HOP_LENGTH / SAMPLE_RATE))
-    offset_list = np.array(start_seconds)
-    chunks = []
-
-    for start_ts, end_ts in zip(start_seconds, end_seconds):
-        start_ts = round(start_ts / (HOP_LENGTH / SAMPLE_RATE))
-        end_ts = round(end_ts / (HOP_LENGTH / SAMPLE_RATE))
-        chunk = mel[:, start_ts:end_ts]
-        chunk = torch.nn.functional.pad(chunk, (0, max_length-chunk.shape[-1]))
-        chunks.append(chunk)
-    
-    mel_chunk = torch.stack(chunks, dim=0).to(model.device)
-    # using 'decode_options1': only support single temperature decoding (no fallbacks)
-    # result_list2 = model.decode(mel_chunk, decode_options1)
-
-    # prepare DecodingOptions
-    temperatures = kwargs.pop("temperature", None)
-    compression_ratio_threshold = kwargs.pop("compression_ratio_threshold", None)
-    logprob_threshold = kwargs.pop("logprob_threshold", None)
-    no_speech_threshold = kwargs.pop("no_speech_threshold", None)
-    condition_on_previous_text = kwargs.pop("condition_on_previous_text", None)
-    initial_prompt = kwargs.pop("initial_prompt", None)
-    
-    t = 0  # TODO: does not upport temperature sweeping
-    if t > 0:
-        # disable beam_size and patience when t > 0
-        kwargs.pop("beam_size", None)
-        kwargs.pop("patience", None)
-    else:
-        # disable best_of when t == 0
-        kwargs.pop("best_of", None)
-
-    options = DecodingOptions(**kwargs, temperature=t)
-    mel_chunk_batches = torch.split(mel_chunk, split_size_or_sections=batch_size)
-    decode_result = []
-    for mel_chunk_batch in mel_chunk_batches:
-        decode_result.extend(model.decode(mel_chunk_batch, options))
-    
-    ##############################
-    ### END of parallelization ###
-    ##############################
-
-    # post processing: get segments rfom batch-decoded results
-    input_stride = exact_div(
-        N_FRAMES, model.dims.n_audio_ctx
-    )  # mel frames per output token: 2
-    language = kwargs["language"]
-    task = kwargs["task"]
-    tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
-
-    output = post_process_results(
-        vad_segments,
-        decode_result, 
-        duration_list, 
-        offset_list,
-        input_stride,
-        language,
-        tokenizer,
-        no_speech_threshold=no_speech_threshold,
-        logprob_threshold=logprob_threshold,
-        verbose=verbose)
-    return output
-
-
-def post_process_results(
-    vad_segments,
-    result_list, 
-    duration_list, 
-    offset_list,
-    input_stride,
-    language,
-    tokenizer,
-    no_speech_threshold = None,
-    logprob_threshold = None,
-    verbose: Optional[bool] = None,
-    ):
-
-    seek = 0
-    time_precision = (
-        input_stride * HOP_LENGTH / SAMPLE_RATE
-    )  # time per output token: 0.02 (seconds)
-    all_tokens = []
-    all_segments = []
-    output = {"segments": []}
-
-    def add_segment(
-        *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult
-    ):
-        text = tokenizer.decode([token for token in text_tokens if token < tokenizer.eot])
-        if len(text.strip()) == 0:  # skip empty text output
-            return
-
-        all_segments.append(
-            {
-                "id": len(all_segments),
-                "seek": seek,
-                "start": start,
-                "end": end,
-                "text": text,
-                "tokens": text_tokens.tolist(),
-                "temperature": result.temperature,
-                "avg_logprob": result.avg_logprob,
-                "compression_ratio": result.compression_ratio,
-                "no_speech_prob": result.no_speech_prob,
-            }
-        )
-        if verbose:
-            print(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}")
-
-    # process the output
-    for seg_t, result, segment_duration, timestamp_offset in zip(vad_segments, result_list, duration_list, offset_list):
-        all_tokens = []
-        all_segments = []
-
-        # segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE
-        segment_shape = int(segment_duration / (HOP_LENGTH / SAMPLE_RATE))
-        tokens = torch.tensor(result.tokens)
-
-        if no_speech_threshold is not None:
-            # no voice activity check
-            should_skip = result.no_speech_prob > no_speech_threshold
-            if logprob_threshold is not None and result.avg_logprob > logprob_threshold:
-                # don't skip if the logprob is high enough, despite the no_speech_prob
-                should_skip = False
-
-            if should_skip:
-                seek += segment_shape  # fast-forward to the next segment boundary
-                continue
-        
-        timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
-        consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0].add_(1)
-        
-        if len(consecutive) > 0:  # if the output contains two consecutive timestamp tokens
-            last_slice = 0
-            for current_slice in consecutive:
-                sliced_tokens = tokens[last_slice:current_slice]
-                start_timestamp_position = (
-                    sliced_tokens[0].item() - tokenizer.timestamp_begin
-                )
-                end_timestamp_position = (
-                    sliced_tokens[-1].item() - tokenizer.timestamp_begin
-                )
-                add_segment(
-                    start=timestamp_offset + start_timestamp_position * time_precision,
-                    end=timestamp_offset + end_timestamp_position * time_precision,
-                    text_tokens=sliced_tokens[1:-1],
-                    result=result,
-                )
-                last_slice = current_slice
-            last_timestamp_position = (
-                tokens[last_slice - 1].item() - tokenizer.timestamp_begin
-            )
-            seek += last_timestamp_position * input_stride
-            all_tokens.extend(tokens[: last_slice + 1].tolist())
-        else:
-            duration = segment_duration
-            timestamps = tokens[timestamp_tokens.nonzero().flatten()]
-            if len(timestamps) > 0 and timestamps[-1].item() != tokenizer.timestamp_begin:
-                # no consecutive timestamps but it has a timestamp; use the last one.
-                # single timestamp at the end means no speech after the last timestamp.
-                last_timestamp_position = timestamps[-1].item() - tokenizer.timestamp_begin
-                duration = last_timestamp_position * time_precision
-
-            add_segment(
-                start=timestamp_offset,
-                end=timestamp_offset + duration,
-                text_tokens=tokens,
-                result=result,
-            )
-
-            seek += segment_shape
-            all_tokens.extend(tokens.tolist())
-
-        result = dict(text=tokenizer.decode(all_tokens), segments=all_segments, language=language)
-        output["segments"].append(
-            {
-                "start": seg_t["start"],
-                "end": seg_t["end"],
-                "language": result["language"],
-                "text": result["text"],
-                "seg-text": [x["text"] for x in result["segments"]],
-                "seg-start": [x["start"] for x in result["segments"]],
-                "seg-end": [x["end"] for x in result["segments"]],
-                }
-            )
-
-    output["language"] = output["segments"][0]["language"]
-
-    return output
-
+from .asr import transcribe, transcribe_with_vad
+from .alignment import load_align_model, align
+from .diarize import DiarizationPipeline
+from .vad import load_vad_model
 
 def cli():
-    from . import available_models
+    from whisper import available_models
 
+    # fmt: off
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
     parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
     parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
     parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
-    # alignment params
-    parser.add_argument("--align_model", default=None, help="Name of phoneme-level ASR model to do alignment")
-    parser.add_argument("--align_extend", default=2, type=float, help="Seconds before and after to extend the whisper segments for alignment")
-    parser.add_argument("--align_from_prev", default=True, type=bool, help="Whether to clip the alignment start time of current segment to the end time of the last aligned word of the previous segment")
-    parser.add_argument("--interpolate_method", default="nearest", choices=["nearest", "linear", "ignore"], help="For word .srt, method to assign timestamps to non-aligned words, or merge them into neighbouring.")
-    # vad params
-    parser.add_argument("--vad_filter", action="store_true", help="Whether to first perform VAD filtering to target only transcribe within VAD. Produces more accurate alignment + timestamp, requires more GPU memory & compute.")
-    parser.add_argument("--parallel_bs", default=-1, type=int, help="Enable parallel transcribing if > 1")
-    # diarization params
-    parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word")
-    parser.add_argument("--min_speakers", default=None, type=int)
-    parser.add_argument("--max_speakers", default=None, type=int)
-    # output save params
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
-    parser.add_argument("--output_type", default="all", choices=["all", "srt", "srt-word", "vtt", "txt", "tsv", "ass", "ass-char", "pickle", "vad"], help="File type for desired output save")
-
+    parser.add_argument("--output_format", "-f", type=str, default="all", choices=["all", "srt", "srt-word", "vtt", "txt", "tsv", "ass", "ass-char", "pickle", "vad"], help="format of the output file; if not specified, all available formats will be produced")
     parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
 
     parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
     parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
 
+    # alignment params
+    parser.add_argument("--align_model", default=None, help="Name of phoneme-level ASR model to do alignment")
+    parser.add_argument("--align_extend", default=2, type=float, help="Seconds before and after to extend the whisper segments for alignment (if not using VAD).")
+    parser.add_argument("--align_from_prev", default=True, type=bool, help="Whether to clip the alignment start time of current segment to the end time of the last aligned word of the previous segment (if not using VAD)")
+    parser.add_argument("--interpolate_method", default="nearest", choices=["nearest", "linear", "ignore"], help="For word .srt, method to assign timestamps to non-aligned words, or merge them into neighbouring.")
+    parser.add_argument("--no_align", action='store_true', help="Do not perform phoneme alignment")
+
+    # vad params
+    parser.add_argument("--vad_filter", action="store_true", help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747")
+    parser.add_argument("--vad_onset", type=float, default=0.767, help="Onset threshold for VAD (see pyannote.audio)")
+    parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio).")
+
+    # diarization params
+    parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word")
+    parser.add_argument("--min_speakers", default=None, type=int)
+    parser.add_argument("--max_speakers", default=None, type=int)
+
     parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
     parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
     parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
@@ -605,160 +61,114 @@ def cli():
 
     parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
     parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
-    parser.add_argument("--condition_on_previous_text", type=str2bool, default=False, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
+    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
     parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
 
     parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
     parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
     parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
     parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
+    parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
+    parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
+    parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
+
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
-    
+    parser.add_argument("--model_flush", action="store_true", help="Flush memory of each stage after use, more GPU memory efficient, but slower when there are multiple audio files")
+    # fmt: on
+
     args = parser.parse_args().__dict__
     model_name: str = args.pop("model")
     model_dir: str = args.pop("model_dir")
     output_dir: str = args.pop("output_dir")
-    output_type: str = args.pop("output_type")
+    output_format: str = args.pop("output_format")
     device: str = args.pop("device")
+    model_flush: bool = args.pop("model_flush")
+    os.makedirs(output_dir, exist_ok=True)
 
     align_model: str = args.pop("align_model")
     align_extend: float = args.pop("align_extend")
     align_from_prev: bool = args.pop("align_from_prev")
-    interpolate_method: bool = args.pop("interpolate_method")
-    
+    interpolate_method: str = args.pop("interpolate_method")
+    no_align: bool = args.pop("no_align")
+
     hf_token: str = args.pop("hf_token")
     vad_filter: bool = args.pop("vad_filter")
-    parallel_bs: int = args.pop("parallel_bs")
+    vad_onset: float = args.pop("vad_onset")
+    vad_offset: float = args.pop("vad_offset")
 
     diarize: bool = args.pop("diarize")
     min_speakers: int = args.pop("min_speakers")
     max_speakers: int = args.pop("max_speakers")
 
-    vad_pipeline = None
     if vad_filter:
-        if hf_token is None:
-            print("Warning, no huggingface token used, needs to be saved in environment variable, otherwise will throw error loading VAD model...")
-        from pyannote.audio import Inference, Model
-        vad_pipeline = Inference(
-            Model.from_pretrained("pyannote/segmentation",
-                                  use_auth_token=hf_token),
-            pre_aggregation_hook=lambda segmentation: segmentation,
-            use_auth_token=hf_token,
-            device=torch.device(device),
-        )
-
-    diarize_pipeline = None
-    if diarize:
         if hf_token is None:
             print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
         from pyannote.audio import Pipeline
-        diarize_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
-                                    use_auth_token=hf_token)
+        from pyannote.audio import Model, Pipeline
+        vad_model = load_vad_model(torch.device(device), vad_onset, vad_offset, use_auth_token=hf_token)
+    else:
+        vad_model = None
+
+    if diarize:
+        if hf_token is None:
+            print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
+        diarize_model = DiarizationPipeline(use_auth_token=hf_token)
+    else:
+        diarize_model = None
+
+    if no_align:
+        align_model, align_metadata = None, None
+    else:
+        align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified
+        align_model, align_metadata = load_align_model(align_language, device, model_name=align_model)     
 
-    os.makedirs(output_dir, exist_ok=True)
 
     if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
         if args["language"] is not None:
-            warnings.warn(f'{model_name} is an English-only model but receipted "{args["language"]}"; using English instead.')
+            warnings.warn(
+                f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead."
+            )
         args["language"] = "en"
 
     temperature = args.pop("temperature")
-    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
-    if temperature_increment_on_fallback is not None:
-        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
+    if (increment := args.pop("temperature_increment_on_fallback")) is not None:
+        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment))
     else:
         temperature = [temperature]
 
-    threads = args.pop("threads")
-    if threads > 0:
+    if (threads := args.pop("threads")) > 0:
         torch.set_num_threads(threads)
 
-    from . import load_model
+    from whisper import load_model
+
     model = load_model(model_name, device=device, download_root=model_dir)
 
-    align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified
-    align_model, align_metadata = load_align_model(align_language, device, model_name=align_model)
-
+    writer = get_writer(output_format, output_dir)
     for audio_path in args.pop("audio"):
-        if vad_filter:
-            if parallel_bs > 1:
-                print("Performing VAD and parallel transcribing ...")
-                result = transcribe_with_vad_parallel(model, audio_path, vad_pipeline, temperature=temperature, batch_size=parallel_bs, **args)
-            else:
-                print("Performing VAD...")
-                result = transcribe_with_vad(model, audio_path, vad_pipeline, temperature=temperature, **args)
+
+        if vad_model is not None:
+            print("Performing VAD...")
+            result = transcribe_with_vad(model, audio_path, vad_model, temperature=temperature, **args)
         else:
             print("Performing transcription...")
             result = transcribe(model, audio_path, temperature=temperature, **args)
 
-        if result["language"] != align_metadata["language"]:
-            # load new language
-            print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
-            align_model, align_metadata = load_align_model(result["language"], device)
+        if align_model is not None:
+            if result["language"] != align_metadata["language"]:
+                # load new language
+                print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
+                align_model, align_metadata = load_align_model(result["language"], device)
+
+            result = align(result["segments"], align_model, align_metadata, audio_path, device,
+                extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
+
+        # if diarize_model is not None:
+        #     diarize_segments = diarize_model(audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
+        #     results_segments, word_segments = assign_word_speakers(diarize_segments, )
+
+        writer(result, audio_path)
 
 
-        print("Performing alignment...")
-        result_aligned = align(result["segments"], align_model, align_metadata, audio_path, device,
-                                extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
-        audio_basename = os.path.basename(audio_path)
-
-        if diarize:
-            print("Performing diarization...")
-            diarize_segments = diarize_pipeline(audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
-            diarize_df = pd.DataFrame(diarize_segments.itertracks(yield_label=True))
-            diarize_df['start'] = diarize_df[0].apply(lambda x: x.start)
-            diarize_df['end'] = diarize_df[0].apply(lambda x: x.end)
-            # assumes each utterance is single speaker (needs fix)
-            result_segments, word_segments = assign_word_speakers(diarize_df, result_aligned["segments"], fill_nearest=True)
-            result_aligned["segments"] = result_segments
-            result_aligned["word_segments"] = word_segments
-
-        # save TXT
-        if output_type in ["txt", "all"]:
-            with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt:
-                write_txt(result_aligned["segments"], file=txt)
-
-        # save VTT
-        if output_type in ["vtt", "all"]:
-            with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt:
-                write_vtt(result_aligned["segments"], file=vtt)
-
-        # save SRT
-        if output_type in ["srt", "all"]:
-            with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt:
-                write_srt(result_aligned["segments"], file=srt)
-
-        # save TSV
-        if output_type in ["tsv", "all"]:
-            with open(os.path.join(output_dir, audio_basename + ".tsv"), "w", encoding="utf-8") as srt:
-                write_tsv(result_aligned["segments"], file=srt)
-
-        # save SRT word-level
-        if output_type in ["srt-word", "all"]:
-            # save per-word SRT
-            with open(os.path.join(output_dir, audio_basename + ".word.srt"), "w", encoding="utf-8") as srt:
-                write_srt(result_aligned["word_segments"], file=srt)
-
-        # save ASS
-        if output_type in ["ass", "all"]:
-            with open(os.path.join(output_dir, audio_basename + ".ass"), "w", encoding="utf-8") as ass:
-                write_ass(result_aligned["segments"], file=ass)
-        
-        # # save ASS character-level
-        if output_type in ["ass-char"]:
-            with open(os.path.join(output_dir, audio_basename + ".char.ass"), "w", encoding="utf-8") as ass:
-                write_ass(result_aligned["segments"], file=ass, resolution="char")
-
-        # save word tsv
-        if output_type in ["pickle"]:
-            exp_fp = os.path.join(output_dir, audio_basename + ".pkl")
-            pd.DataFrame(result_aligned["segments"]).to_pickle(exp_fp)
-
-        # save word tsv
-        if output_type in ["vad"]:
-            exp_fp = os.path.join(output_dir, audio_basename + ".sad")
-            wrd_segs = pd.concat([x["word-segments"] for x in result_aligned["segments"]])[['start','end']]
-            wrd_segs.to_csv(exp_fp, sep='\t', header=None, index=False)
 if __name__ == "__main__":
-    cli()
+    cli()
\ No newline at end of file
diff --git a/whisperx/utils.py b/whisperx/utils.py
index 805db26..84f03a9 100644
--- a/whisperx/utils.py
+++ b/whisperx/utils.py
@@ -4,48 +4,12 @@ from typing import Callable, TextIO, Iterator, Tuple
 import pandas as pd
 import numpy as np
 
-def exact_div(x, y):
-    assert x % y == 0
-    return x // y
-
-
-def str2bool(string):
-    str2val = {"True": True, "False": False}
-    if string in str2val:
-        return str2val[string]
+def interpolate_nans(x, method='nearest'):
+    if x.notnull().sum() > 1:
+        return x.interpolate(method=method).ffill().bfill()
     else:
-        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
-
-
-def optional_int(string):
-    return None if string == "None" else int(string)
-
-
-def optional_float(string):
-    return None if string == "None" else float(string)
-
-
-def compression_ratio(text) -> float:
-    text_bytes = text.encode("utf-8")
-    return len(text_bytes) / len(zlib.compress(text_bytes))
-
-
-def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
-    assert seconds >= 0, "non-negative timestamp expected"
-    milliseconds = round(seconds * 1000.0)
-
-    hours = milliseconds // 3_600_000
-    milliseconds -= hours * 3_600_000
-
-    minutes = milliseconds // 60_000
-    milliseconds -= minutes * 60_000
-
-    seconds = milliseconds // 1_000
-    milliseconds -= seconds * 1_000
-
-    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
-    return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
-
+        return x.ffill().bfill()
+    
 
 def write_txt(transcript: Iterator[dict], file: TextIO):
     for segment in transcript:
@@ -250,8 +214,92 @@ def write_ass(transcript: Iterator[dict],
 
     file.write(ass_str)
 
-def interpolate_nans(x, method='nearest'):
-    if x.notnull().sum() > 1:
-        return x.interpolate(method=method).ffill().bfill()
-    else:
-        return x.ffill().bfill()
\ No newline at end of file
+
+from whisper.utils import SubtitlesWriter, ResultWriter, WriteTXT, WriteVTT, WriteSRT, WriteTSV, WriteJSON, format_timestamp
+
+class WriteASS(ResultWriter):
+    extension: str = "ass"
+
+    def write_result(self, result: dict, file: TextIO):
+        write_ass(result["segments"], file, resoltuion="word")
+
+class WriteASSchar(ResultWriter):
+    extension: str = "ass"
+
+    def write_result(self, result: dict, file: TextIO):
+        write_ass(result["segments"], file, resoltuion="char")
+
+class WritePickle(ResultWriter):
+    extension: str = "ass"
+
+    def write_result(self, result: dict, file: TextIO):
+        pd.DataFrame(result["segments"]).to_pickle(file)
+
+class WriteSRTWord(ResultWriter):
+    extension: str = ".word.srt"
+    always_include_hours: bool = True
+    decimal_marker: str = ","
+
+    def iterate_result(self, result: dict):
+        for segment in result["word_segments"]:
+            segment_start = self.format_timestamp(segment["start"])
+            segment_end = self.format_timestamp(segment["end"])
+            segment_text = segment["text"].strip().replace("-->", "->")
+
+            if word_timings := segment.get("words", None):
+                all_words = [timing["word"] for timing in word_timings]
+                all_words[0] = all_words[0].strip()  # remove the leading space, if any
+                last = segment_start
+                for i, this_word in enumerate(word_timings):
+                    start = self.format_timestamp(this_word["start"])
+                    end = self.format_timestamp(this_word["end"])
+                    if last != start:
+                        yield last, start, segment_text
+
+                    yield start, end, "".join(
+                        [
+                            f"<u>{word}</u>" if j == i else word
+                            for j, word in enumerate(all_words)
+                        ]
+                    )
+                    last = end
+
+                if last != segment_end:
+                    yield last, segment_end, segment_text
+            else:
+                yield segment_start, segment_end, segment_text
+
+    def write_result(self, result: dict, file: TextIO):
+        for i, (start, end, text) in enumerate(self.iterate_result(result), start=1):
+            print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True)
+
+    def format_timestamp(self, seconds: float):
+        return format_timestamp(
+            seconds=seconds,
+            always_include_hours=self.always_include_hours,
+            decimal_marker=self.decimal_marker,
+        )
+
+def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]:
+    writers = {
+        "txt": WriteTXT,
+        "vtt": WriteVTT,
+        "srt": WriteSRT,
+        "tsv": WriteTSV,
+        # "json": WriteJSON,
+        "ass": WriteASS,
+        # "ass-char": WriteASSchar,
+        # "pickle": WritePickle,
+        "srt-word": WriteSRTWord,
+    }
+
+    if output_format == "all":
+        all_writers = [writer(output_dir) for writer in writers.values()]
+
+        def write_all(result: dict, file: TextIO):
+            for writer in all_writers:
+                writer(result, file)
+
+        return write_all
+
+    return writers[output_format](output_dir)
\ No newline at end of file
diff --git a/whisperx/vad.py b/whisperx/vad.py
index 2932a13..69ad8ee 100644
--- a/whisperx/vad.py
+++ b/whisperx/vad.py
@@ -1,10 +1,32 @@
+import os
 import pandas as pd
 import numpy as np
-from pyannote.core import Annotation, Segment, SlidingWindowFeature, Timeline
+import torch
+from typing import Optional, Callable, Union, Text
+from pyannote.audio.core.io import AudioFile
+from pyannote.core import Annotation, Segment, SlidingWindowFeature
+from pyannote.audio.pipelines.utils import PipelineModel
+from pyannote.audio import Model, Pipeline
+from pyannote.audio.pipelines import VoiceActivityDetection
+from .diarize import Segment as SegmentX
+
 from typing import List, Tuple, Optional
 
+def load_vad_model(device, vad_onset, vad_offset, use_auth_token=None):
+    vad_model = Model.from_pretrained("pyannote/segmentation", use_auth_token=use_auth_token)
+    hyperparameters = {"onset": vad_onset, 
+                    "offset": vad_offset,
+                    "min_duration_on": 0.1,
+                    "min_duration_off": 0.1}
+    vad_pipeline = VoiceActivitySegmentation(segmentation=vad_model, device=torch.device(device))
+    vad_pipeline.instantiate(hyperparameters)
+
+    return vad_pipeline
+
 class Binarize:
-    """Binarize detection scores using hysteresis thresholding
+    """Binarize detection scores using hysteresis thresholding, with min-cut operation
+    to ensure not segments are longer than max_duration.
+
     Parameters
     ----------
     onset : float, optional
@@ -28,6 +50,9 @@ class Binarize:
     Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of
     RNN-based Voice Activity Detection", InterSpeech 2015.
 
+    Modified by Max Bain to include WhisperX's min-cut operation 
+    https://arxiv.org/abs/2303.00747
+    
     Pyannote-audio
     """
 
@@ -136,6 +161,51 @@ class Binarize:
         return active
 
 
+class VoiceActivitySegmentation(VoiceActivityDetection):
+    def __init__(
+        self,
+        segmentation: PipelineModel = "pyannote/segmentation",
+        fscore: bool = False,
+        use_auth_token: Union[Text, None] = None,
+        **inference_kwargs,
+    ):
+
+        super().__init__(segmentation=segmentation, fscore=fscore, use_auth_token=use_auth_token, **inference_kwargs)
+
+    def apply(self, file: AudioFile, hook: Optional[Callable] = None) -> Annotation:
+        """Apply voice activity detection
+
+        Parameters
+        ----------
+        file : AudioFile
+            Processed file.
+        hook : callable, optional
+            Hook called after each major step of the pipeline with the following
+            signature: hook("step_name", step_artefact, file=file)
+
+        Returns
+        -------
+        speech : Annotation
+            Speech regions.
+        """
+
+        # setup hook (e.g. for debugging purposes)
+        hook = self.setup_hook(file, hook=hook)
+
+        # apply segmentation model (only if needed)
+        # output shape is (num_chunks, num_frames, 1)
+        if self.training:
+            if self.CACHED_SEGMENTATION in file:
+                segmentations = file[self.CACHED_SEGMENTATION]
+            else:
+                segmentations = self._segmentation(file)
+                file[self.CACHED_SEGMENTATION] = segmentations
+        else:
+            segmentations: SlidingWindowFeature = self._segmentation(file)
+
+        return segmentations
+
+
 def merge_vad(vad_arr, pad_onset=0.0, pad_offset=0.0, min_duration_off=0.0, min_duration_on=0.0):
 
     active = Annotation()
@@ -157,21 +227,49 @@ def merge_vad(vad_arr, pad_onset=0.0, pad_offset=0.0, min_duration_off=0.0, min_
     active_segs = pd.DataFrame([x['segment'] for x in active['content']])
     return active_segs
 
+def merge_chunks(segments, chunk_size):
+    """
+    Merge operation described in paper
+    """
+    curr_end = 0
+    merged_segments = []
+    seg_idxs = []
+    speaker_idxs = []
+
+    assert chunk_size > 0
+    binarize = Binarize(max_duration=chunk_size)
+    segments = binarize(segments)
+    segments_list = []
+    for speech_turn in segments.get_timeline():
+        segments_list.append(SegmentX(speech_turn.start, speech_turn.end, "UNKNOWN"))
+
+    assert segments_list, "segments_list is empty."
+    # Make sur the starting point is the start of the segment.
+    curr_start = segments_list[0].start
+
+    for seg in segments_list:
+        if seg.end - curr_start > chunk_size and curr_end-curr_start > 0:
+            merged_segments.append({
+                "start": curr_start,
+                "end": curr_end,
+                "segments": seg_idxs,
+            })
+            curr_start = seg.start
+            seg_idxs = []
+            speaker_idxs = []
+        curr_end = seg.end
+        seg_idxs.append((seg.start, seg.end))
+        speaker_idxs.append(seg.speaker)
+    # add final
+    merged_segments.append({ 
+                "start": curr_start,
+                "end": curr_end,
+                "segments": seg_idxs,
+            })    
+    return merged_segments
+
 
 if __name__ == "__main__":
-    # from pyannote.audio import Inference
-    # hook = lambda segmentation: segmentation
-    # inference = Inference("pyannote/segmentation", pre_aggregation_hook=hook)
-    # audio = "/tmp/11962.wav" 
-    # scores = inference(audio)
-    # binarize = Binarize(max_duration=15)
-    # anno = binarize(scores)
-    # res = []
-    # for ann in anno.get_timeline():
-    #     res.append((ann.start, ann.end))
-
-    # res = pd.DataFrame(res)
-    # res[2] = res[1] - res[0]
     import pandas as pd
     input_fp = "tt298650_sync.wav"
     df = pd.read_csv(f"/work/maxbain/tmp/{input_fp}.sad", sep=" ", header=None)

From ae4a9de3072593868d34de1c44d5f8adf82d0493 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Thu, 30 Mar 2023 18:57:55 +0100
Subject: [PATCH 04/48] add vad model external dl

---
 whisperx/asr.py        |  3 +--
 whisperx/transcribe.py | 24 +++++++++++++++++-------
 whisperx/vad.py        | 38 +++++++++++++++++++++++++++++++++++---
 3 files changed, 53 insertions(+), 12 deletions(-)

diff --git a/whisperx/asr.py b/whisperx/asr.py
index 661a28e..27213ef 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -1,10 +1,9 @@
 import warnings
 from typing import TYPE_CHECKING, Optional, Tuple, Union
-import tempfile
 import numpy as np
 import torch
 import tqdm
-
+import ffmpeg
 from whisper.audio import (
     FRAMES_PER_SECOND,
     HOP_LENGTH,
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 4461dfb..2c4da32 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -2,22 +2,22 @@ import argparse
 import os
 import warnings
 from typing import TYPE_CHECKING, Optional, Tuple, Union
-
 import numpy as np
 import torch
-
+import tempfile 
+import ffmpeg
 from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE
+from whisper.audio import SAMPLE_RATE
 from whisper.utils import (
     optional_float,
     optional_int,
     str2bool,
 )
 
-from .utils import get_writer
-
-from .asr import transcribe, transcribe_with_vad
 from .alignment import load_align_model, align
+from .asr import transcribe, transcribe_with_vad
 from .diarize import DiarizationPipeline
+from .utils import get_writer
 from .vad import load_vad_model
 
 def cli():
@@ -74,7 +74,7 @@ def cli():
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
 
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
-    parser.add_argument("--model_flush", action="store_true", help="Flush memory of each stage after use, more GPU memory efficient, but slower when there are multiple audio files")
+    parser.add_argument("--model_flush", action="store_true", help="Flush memory from each model after use, reduces GPU requirement but slower processing >1 audio file.")
     # fmt: on
 
     args = parser.parse_args().__dict__
@@ -148,8 +148,18 @@ def cli():
     for audio_path in args.pop("audio"):
 
         if vad_model is not None:
+            if not audio_path.endswith(".wav"):
+                print("VAD requires .wav format, converting to wav as a tempfile...")
+                tfile = tempfile.NamedTemporaryFile(delete=True, suffix=".wav")
+                ffmpeg.input(audio_path, threads=0).output(tfile.name, ac=1, ar=SAMPLE_RATE).run(cmd=["ffmpeg"])
+                vad_audio_path = tfile.name
+            else:
+                vad_audio_path = audio_path
             print("Performing VAD...")
-            result = transcribe_with_vad(model, audio_path, vad_model, temperature=temperature, **args)
+            result = transcribe_with_vad(model, vad_audio_path, vad_model, temperature=temperature, **args)
+            
+            if tfile is not None:
+                tfile.close()
         else:
             print("Performing transcription...")
             result = transcribe(model, audio_path, temperature=temperature, **args)
diff --git a/whisperx/vad.py b/whisperx/vad.py
index 69ad8ee..fc291dd 100644
--- a/whisperx/vad.py
+++ b/whisperx/vad.py
@@ -1,19 +1,51 @@
 import os
+import urllib
 import pandas as pd
 import numpy as np
 import torch
+import hashlib
+from tqdm import tqdm
 from typing import Optional, Callable, Union, Text
 from pyannote.audio.core.io import AudioFile
 from pyannote.core import Annotation, Segment, SlidingWindowFeature
 from pyannote.audio.pipelines.utils import PipelineModel
-from pyannote.audio import Model, Pipeline
+from pyannote.audio import Model
 from pyannote.audio.pipelines import VoiceActivityDetection
 from .diarize import Segment as SegmentX
-
 from typing import List, Tuple, Optional
 
+VAD_SEGMENTATION_URL = "https://whisperx.s3.eu-west-2.amazonaws.com/model_weights/segmentation/0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea/pytorch_model.bin"
+
 def load_vad_model(device, vad_onset, vad_offset, use_auth_token=None):
-    vad_model = Model.from_pretrained("pyannote/segmentation", use_auth_token=use_auth_token)
+    model_dir = torch.hub._get_torch_home()
+    model_fp = os.path.join(model_dir, "whisperx-vad-segmentation.bin")
+    if os.path.exists(model_fp) and not os.path.isfile(model_fp):
+        raise RuntimeError(f"{model_fp} exists and is not a regular file")
+
+    if not os.path.isfile(model_fp):
+        with urllib.request.urlopen(VAD_SEGMENTATION_URL) as source, open(model_fp, "wb") as output:
+            with tqdm(
+                total=int(source.info().get("Content-Length")),
+                ncols=80,
+                unit="iB",
+                unit_scale=True,
+                unit_divisor=1024,
+            ) as loop:
+                while True:
+                    buffer = source.read(8192)
+                    if not buffer:
+                        break
+
+                    output.write(buffer)
+                    loop.update(len(buffer))
+
+    model_bytes = open(model_fp, "rb").read()
+    if hashlib.sha256(model_bytes).hexdigest() != VAD_SEGMENTATION_URL.split('/')[-2]:
+        raise RuntimeError(
+            "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
+        )
+
+    vad_model = Model.from_pretrained(model_fp, use_auth_token=use_auth_token)
     hyperparameters = {"onset": vad_onset, 
                     "offset": vad_offset,
                     "min_duration_on": 0.1,

From d0fa0280452feb9e8fd77c64e964838e3e02bf1a Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Thu, 30 Mar 2023 19:24:42 +0100
Subject: [PATCH 05/48] fix tfile naming

---
 whisperx/transcribe.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 2c4da32..04eb201 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -45,8 +45,8 @@ def cli():
 
     # vad params
     parser.add_argument("--vad_filter", action="store_true", help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747")
-    parser.add_argument("--vad_onset", type=float, default=0.767, help="Onset threshold for VAD (see pyannote.audio)")
-    parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio).")
+    parser.add_argument("--vad_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected")
+    parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected.")
 
     # diarization params
     parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word")
@@ -75,6 +75,7 @@ def cli():
 
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
     parser.add_argument("--model_flush", action="store_true", help="Flush memory from each model after use, reduces GPU requirement but slower processing >1 audio file.")
+    parser.add_argument("--tmp_dir", default=None, help="Temporary directory to write audio file if input if not .wav format (only for VAD).")
     # fmt: on
 
     args = parser.parse_args().__dict__
@@ -146,23 +147,21 @@ def cli():
 
     writer = get_writer(output_format, output_dir)
     for audio_path in args.pop("audio"):
-
+        input_audio_path = audio_path
         if vad_model is not None:
             if not audio_path.endswith(".wav"):
                 print("VAD requires .wav format, converting to wav as a tempfile...")
                 tfile = tempfile.NamedTemporaryFile(delete=True, suffix=".wav")
                 ffmpeg.input(audio_path, threads=0).output(tfile.name, ac=1, ar=SAMPLE_RATE).run(cmd=["ffmpeg"])
-                vad_audio_path = tfile.name
-            else:
-                vad_audio_path = audio_path
+                input_audio_path = tfile.name
             print("Performing VAD...")
-            result = transcribe_with_vad(model, vad_audio_path, vad_model, temperature=temperature, **args)
+            result = transcribe_with_vad(model, input_audio_path, vad_model, temperature=temperature, **args)
             
             if tfile is not None:
                 tfile.close()
         else:
             print("Performing transcription...")
-            result = transcribe(model, audio_path, temperature=temperature, **args)
+            result = transcribe(model, input_audio_path, temperature=temperature, **args)
 
         if align_model is not None:
             if result["language"] != align_metadata["language"]:
@@ -170,11 +169,11 @@ def cli():
                 print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
                 align_model, align_metadata = load_align_model(result["language"], device)
 
-            result = align(result["segments"], align_model, align_metadata, audio_path, device,
+            result = align(result["segments"], align_model, align_metadata, input_audio_path, device,
                 extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
 
         # if diarize_model is not None:
-        #     diarize_segments = diarize_model(audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
+        #     diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
         #     results_segments, word_segments = assign_word_speakers(diarize_segments, )
 
         writer(result, audio_path)

From b9ca701d6943b7e83a6caa0c6b4adda7a258ecc6 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Fri, 31 Mar 2023 23:02:38 +0100
Subject: [PATCH 06/48] .wav conversion, handle audio with no detected speech

---
 figures/pipeline.png   | Bin 0 -> 122529 bytes
 whisperx/asr.py        |   4 +++-
 whisperx/transcribe.py |  45 +++++++++++++++++++++++++----------------
 whisperx/utils.py      |  18 ++++++++++++++---
 whisperx/vad.py        |   5 ++++-
 5 files changed, 50 insertions(+), 22 deletions(-)
 create mode 100644 figures/pipeline.png

diff --git a/figures/pipeline.png b/figures/pipeline.png
new file mode 100644
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diff --git a/whisperx/asr.py b/whisperx/asr.py
index 27213ef..ac16459 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -395,8 +395,10 @@ def transcribe_with_vad(
 
     # merge segments to approx 30s inputs to make whisper most appropraite
     vad_segments = merge_chunks(vad_segments, chunk_size=CHUNK_LENGTH)
+    if len(vad_segments) == 0:
+        return output
 
-    print("Performing transcription...")
+    print(">>Performing transcription...")
     for sdx, seg_t in enumerate(vad_segments):
         if verbose:
             print(f"~~ Transcribing VAD chunk: ({format_timestamp(seg_t['start'])} --> {format_timestamp(seg_t['end'])}) ~~")
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 04eb201..0548bfc 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -16,7 +16,7 @@ from whisper.utils import (
 
 from .alignment import load_align_model, align
 from .asr import transcribe, transcribe_with_vad
-from .diarize import DiarizationPipeline
+from .diarize import DiarizationPipeline, assign_word_speakers
 from .utils import get_writer
 from .vad import load_vad_model
 
@@ -44,7 +44,7 @@ def cli():
     parser.add_argument("--no_align", action='store_true', help="Do not perform phoneme alignment")
 
     # vad params
-    parser.add_argument("--vad_filter", action="store_true", help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747")
+    parser.add_argument("--vad_filter", default=True, help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747")
     parser.add_argument("--vad_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected")
     parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected.")
 
@@ -61,7 +61,7 @@ def cli():
 
     parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
     parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
-    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
+    parser.add_argument("--condition_on_previous_text", type=str2bool, default=False, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
     parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
 
     parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
@@ -74,7 +74,7 @@ def cli():
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
 
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
-    parser.add_argument("--model_flush", action="store_true", help="Flush memory from each model after use, reduces GPU requirement but slower processing >1 audio file.")
+    # parser.add_argument("--model_flush", action="store_true", help="Flush memory from each model after use, reduces GPU requirement but slower processing >1 audio file.")
     parser.add_argument("--tmp_dir", default=None, help="Temporary directory to write audio file if input if not .wav format (only for VAD).")
     # fmt: on
 
@@ -84,9 +84,13 @@ def cli():
     output_dir: str = args.pop("output_dir")
     output_format: str = args.pop("output_format")
     device: str = args.pop("device")
-    model_flush: bool = args.pop("model_flush")
+    # model_flush: bool = args.pop("model_flush")
     os.makedirs(output_dir, exist_ok=True)
 
+    tmp_dir: str = args.pop("tmp_dir")
+    if tmp_dir is not None:
+        os.makedirs(tmp_dir, exist_ok=True)
+
     align_model: str = args.pop("align_model")
     align_extend: float = args.pop("align_extend")
     align_from_prev: bool = args.pop("align_from_prev")
@@ -124,6 +128,11 @@ def cli():
         align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified
         align_model, align_metadata = load_align_model(align_language, device, model_name=align_model)     
 
+    # if model_flush:
+    #     print(">>Model flushing activated... Only loading model after ASR stage")
+    #     del align_model
+    #     align_model = ""
+
 
     if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
         if args["language"] is not None:
@@ -148,34 +157,36 @@ def cli():
     writer = get_writer(output_format, output_dir)
     for audio_path in args.pop("audio"):
         input_audio_path = audio_path
+        tfile = None
         if vad_model is not None:
             if not audio_path.endswith(".wav"):
-                print("VAD requires .wav format, converting to wav as a tempfile...")
+                print(">>VAD requires .wav format, converting to wav as a tempfile...")
                 tfile = tempfile.NamedTemporaryFile(delete=True, suffix=".wav")
                 ffmpeg.input(audio_path, threads=0).output(tfile.name, ac=1, ar=SAMPLE_RATE).run(cmd=["ffmpeg"])
                 input_audio_path = tfile.name
-            print("Performing VAD...")
+            print(">>Performing VAD...")
             result = transcribe_with_vad(model, input_audio_path, vad_model, temperature=temperature, **args)
-            
-            if tfile is not None:
-                tfile.close()
         else:
-            print("Performing transcription...")
+            print(">>Performing transcription...")
             result = transcribe(model, input_audio_path, temperature=temperature, **args)
 
-        if align_model is not None:
-            if result["language"] != align_metadata["language"]:
+        if align_model is not None and len(result["segments"]) > 0:
+            if result.get("language", "en") != align_metadata["language"]:
                 # load new language
                 print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
                 align_model, align_metadata = load_align_model(result["language"], device)
-
+            print(">>Performing alignment...")
             result = align(result["segments"], align_model, align_metadata, input_audio_path, device,
                 extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
 
-        # if diarize_model is not None:
-        #     diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
-        #     results_segments, word_segments = assign_word_speakers(diarize_segments, )
 
+
+        if diarize_model is not None:
+            diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
+            results_segments, word_segments = assign_word_speakers(diarize_segments)
+
+        if tfile is not None:
+            tfile.close()
         writer(result, audio_path)
 
 
diff --git a/whisperx/utils.py b/whisperx/utils.py
index 84f03a9..992960b 100644
--- a/whisperx/utils.py
+++ b/whisperx/utils.py
@@ -270,6 +270,8 @@ class WriteSRTWord(ResultWriter):
                 yield segment_start, segment_end, segment_text
 
     def write_result(self, result: dict, file: TextIO):
+        if "word_segments" not in result:
+            return
         for i, (start, end, text) in enumerate(self.iterate_result(result), start=1):
             print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True)
 
@@ -286,11 +288,16 @@ def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO],
         "vtt": WriteVTT,
         "srt": WriteSRT,
         "tsv": WriteTSV,
-        # "json": WriteJSON,
         "ass": WriteASS,
+        "srt-word": WriteSRTWord,
         # "ass-char": WriteASSchar,
         # "pickle": WritePickle,
-        "srt-word": WriteSRTWord,
+        # "json": WriteJSON,
+    }
+
+    writers_other = {
+        "pkl": WritePickle,
+        "ass-char": WriteASSchar
     }
 
     if output_format == "all":
@@ -302,4 +309,9 @@ def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO],
 
         return write_all
 
-    return writers[output_format](output_dir)
\ No newline at end of file
+    if output_format in writers:
+        return writers[output_format](output_dir)
+    elif output_format in writers_other:
+        return writers_other[output_format](output_dir)
+    else:
+        raise ValueError(f"Output format '{output_format}' not supported, choose from {writers.keys()} and {writers_other.keys()}")
diff --git a/whisperx/vad.py b/whisperx/vad.py
index fc291dd..aa86060 100644
--- a/whisperx/vad.py
+++ b/whisperx/vad.py
@@ -275,7 +275,10 @@ def merge_chunks(segments, chunk_size):
     for speech_turn in segments.get_timeline():
         segments_list.append(SegmentX(speech_turn.start, speech_turn.end, "UNKNOWN"))
 
-    assert segments_list, "segments_list is empty."
+    if len(segments_list) == 0:
+        print("No active speech found in audio")
+        return []
+    # assert segments_list, "segments_list is empty."
     # Make sur the starting point is the start of the segment.
     curr_start = segments_list[0].start
 

From 11a78d7ced804467d4d88c0d9a378f69cb55030a Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Sat, 1 Apr 2023 00:06:40 +0100
Subject: [PATCH 07/48] handle tmp wav file better

---
 README.md              | 59 +++++++++++++++++++++++++++++++++---------
 whisperx/diarize.py    |  7 ++++-
 whisperx/transcribe.py | 27 ++++++++++++-------
 whisperx/utils.py      |  2 +-
 4 files changed, 71 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 487b9a1..e96a905 100644
--- a/README.md
+++ b/README.md
@@ -29,7 +29,7 @@
 </p>
 
 
-<img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/211200186-8b779e26-0bfd-4127-aee2-5a9238b95e1f.png">
+<img width="1216" align="center" alt="whisperx-arch" src="figures/pipeline.png">
 
 
 <p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy using forced alignment.
@@ -39,7 +39,7 @@
 
 <h2 align="left", id="what-is-it">What is it 🔎</h2>
 
-This repository refines the timestamps of openAI's Whisper model via forced aligment with phoneme-based ASR models (e.g. wav2vec2.0), multilingual use-case.
+This repository refines the timestamps of openAI's Whisper model via forced aligment with phoneme-based ASR models (e.g. wav2vec2.0) and VAD preprocesssing, multilingual use-case.
 
 
 **Whisper** is an ASR model [developed by OpenAI](https://github.com/openai/whisper), trained on a large dataset of diverse audio. Whilst it does produces highly accurate transcriptions, the corresponding timestamps are at the utterance-level, not per word, and can be inaccurate by several seconds.
@@ -48,11 +48,13 @@ This repository refines the timestamps of openAI's Whisper model via forced alig
 
 **Forced Alignment** refers to the process by which orthographic transcriptions are aligned to audio recordings to automatically generate phone level segmentation.
 
+**Voice Activity Detection (VAD)** is the detection of the presence or absence of human speech.
+
 <h2 align="left", id="highlights">New🚨</h2>
 
-- Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed.* Repo will be updated soon with this efficient batch inference.
-- Batch processing: Add `--vad_filter --parallel_bs [int]` for transcribing long audio file in batches (only supported with VAD filtering). Replace `[int]` with a batch size that fits your GPU memory, e.g. `--parallel_bs 16`.
-- VAD filtering: Voice Activity Detection (VAD) from [Pyannote.audio](https://huggingface.co/pyannote/voice-activity-detection) is used as a preprocessing step to remove reliance on whisper timestamps and only transcribe audio segments containing speech. add `--vad_filter` flag, increases timestamp accuracy and robustness (requires more GPU mem due to 30s inputs in wav2vec2)
+- v2 released, code cleanup, imports whisper library, batched inference from paper not included (contact for licensing / batched model API). VAD filtering is now turned on by default, as in the paper.
+- Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed (not provided in this repo).
+- VAD filtering: Voice Activity Detection (VAD) from [Pyannote.audio](https://huggingface.co/pyannote/voice-activity-detection) is used as a preprocessing step to remove reliance on whisper timestamps and only transcribe audio segments containing speech. add `--vad_filter True` flag, increases timestamp accuracy and robustness (requires more GPU mem due to 30s inputs in wav2vec2)
 - Character level timestamps (see `*.char.ass` file output)
 - Diarization (still in beta, add `--diarize`)
 
@@ -89,9 +91,9 @@ Run whisper on example segment (using default params)
     whisperx examples/sample01.wav
 
 
-For increased timestamp accuracy, at the cost of higher gpu mem, use bigger models and VAD filtering e.g.
+For increased timestamp accuracy, at the cost of higher gpu mem, use bigger models (bigger alignment model not found to be that helpful, see paper) e.g.
 
-    whisperx examples/sample01.wav --model large-v2 --vad_filter --align_model WAV2VEC2_ASR_LARGE_LV60K_960H
+    whisperx examples/sample01.wav --model large-v2 --align_model WAV2VEC2_ASR_LARGE_LV60K_960H
 
 Result using *WhisperX* with forced alignment to wav2vec2.0 large:
 
@@ -153,9 +155,8 @@ In addition to forced alignment, the following two modifications have been made
 
 <h2 align="left" id="limitations">Limitations ⚠️</h2>
 
-- Not thoroughly tested, especially for non-english, results may vary -- please post issue to let me know the results on your data
 - Whisper normalises spoken numbers e.g. "fifty seven" to arabic numerals "57". Need to perform this normalization after alignment, so the phonemes can be aligned. Currently just ignores numbers.
-- If not using VAD filter, whisperx assumes the initial whisper timestamps are accurate to some degree (within margin of 2 seconds, adjust if needed -- bigger margins more prone to alignment errors)
+- If setting `--vad_filter False`, then whisperx assumes the initial whisper timestamps are accurate to some degree (within margin of 2 seconds, adjust if needed -- bigger margins more prone to alignment errors)
 - Overlapping speech is not handled particularly well by whisper nor whisperx
 - Diariazation is far from perfect.
 
@@ -180,21 +181,23 @@ The next major upgrade we are working on is whisper with speaker diarization, so
 
 * [x] Incorporating  speaker diarization
 
-* [x] Inference speedup with batch processing
+* [ ] Automatic .wav conversion to make VAD compatible
+
+* [ ] Model flush, for low gpu mem resources
 
 * [ ] Improve diarization (word level). *Harder than first thought...*
 
 
 <h2 align="left" id="contact">Contact/Support 📇</h2>
 
-Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk for queries
+Contact maxhbain@gmail.com for queries and licensing / early access to a model API with batched inference (transcribe 1hr audio in under 1min).
 
 <a href="https://www.buymeacoffee.com/maxhbain" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
 
 
 <h2 align="left" id="acks">Acknowledgements 🙏</h2>
 
-This work, and my PhD, is supported by the [VGG (Visual Geometry Group)](https://www.robots.ox.ac.uk/~vgg/) and University of Oxford.
+This work, and my PhD, is supported by the [VGG (Visual Geometry Group)](https://www.robots.ox.ac.uk/~vgg/) and the University of Oxford.
 
 
 
@@ -214,3 +217,35 @@ If you use this in your research, please cite the paper:
 }
 ```
 
+as well the following works, used in each stage of the pipeline:
+
+```bibtex
+@article{radford2022robust,
+  title={Robust speech recognition via large-scale weak supervision},
+  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+  journal={arXiv preprint arXiv:2212.04356},
+  year={2022}
+}
+```
+
+```bibtex
+@article{baevski2020wav2vec,
+  title={wav2vec 2.0: A framework for self-supervised learning of speech representations},
+  author={Baevski, Alexei and Zhou, Yuhao and Mohamed, Abdelrahman and Auli, Michael},
+  journal={Advances in neural information processing systems},
+  volume={33},
+  pages={12449--12460},
+  year={2020}
+}
+```
+
+```bibtex
+@inproceedings{bredin2020pyannote,
+  title={Pyannote. audio: neural building blocks for speaker diarization},
+  author={Bredin, Herv{\'e} and Yin, Ruiqing and Coria, Juan Manuel and Gelly, Gregory and Korshunov, Pavel and Lavechin, Marvin and Fustes, Diego and Titeux, Hadrien and Bouaziz, Wassim and Gill, Marie-Philippe},
+  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={7124--7128},
+  year={2020},
+  organization={IEEE}
+}
+```
\ No newline at end of file
diff --git a/whisperx/diarize.py b/whisperx/diarize.py
index 52d1bf1..34dfc63 100644
--- a/whisperx/diarize.py
+++ b/whisperx/diarize.py
@@ -41,7 +41,12 @@ def assign_word_speakers(diarize_df, result_segments, fill_nearest=False):
                 speaker = None
             speakers.append(speaker)
         seg['word-segments']['speaker'] = speakers
-        seg["speaker"] = pd.Series(speakers).value_counts().index[0]
+
+        speaker_count = pd.Series(speakers).value_counts()
+        if len(speaker_count) == 0:
+            seg["speaker"]= "UNKNOWN"
+        else:
+            seg["speaker"] = speaker_count.index[0]
 
     # create word level segments for .srt
     word_seg = []
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 0548bfc..9c0c4b5 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -107,8 +107,6 @@ def cli():
     max_speakers: int = args.pop("max_speakers")
 
     if vad_filter:
-        if hf_token is None:
-            print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
         from pyannote.audio import Pipeline
         from pyannote.audio import Model, Pipeline
         vad_model = load_vad_model(torch.device(device), vad_onset, vad_offset, use_auth_token=hf_token)
@@ -158,18 +156,25 @@ def cli():
     for audio_path in args.pop("audio"):
         input_audio_path = audio_path
         tfile = None
+
+        # >> VAD & ASR
         if vad_model is not None:
             if not audio_path.endswith(".wav"):
                 print(">>VAD requires .wav format, converting to wav as a tempfile...")
-                tfile = tempfile.NamedTemporaryFile(delete=True, suffix=".wav")
-                ffmpeg.input(audio_path, threads=0).output(tfile.name, ac=1, ar=SAMPLE_RATE).run(cmd=["ffmpeg"])
-                input_audio_path = tfile.name
+                # tfile = tempfile.NamedTemporaryFile(delete=True, suffix=".wav")
+                audio_basename = os.path.splitext(os.path.basename(audio_path))[0]
+                if tmp_dir is not None:
+                    input_audio_path = os.path.join(tmp_dir, audio_basename + ".wav")
+                else:
+                    input_audio_path = os.path.join(os.path.dirname(audio_path), audio_basename + ".wav")
+                ffmpeg.input(audio_path, threads=0).output(input_audio_path, ac=1, ar=SAMPLE_RATE).run(cmd=["ffmpeg"])
             print(">>Performing VAD...")
             result = transcribe_with_vad(model, input_audio_path, vad_model, temperature=temperature, **args)
         else:
             print(">>Performing transcription...")
             result = transcribe(model, input_audio_path, temperature=temperature, **args)
 
+        # >> Align
         if align_model is not None and len(result["segments"]) > 0:
             if result.get("language", "en") != align_metadata["language"]:
                 # load new language
@@ -179,16 +184,18 @@ def cli():
             result = align(result["segments"], align_model, align_metadata, input_audio_path, device,
                 extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
 
-
-
+        # >> Diarize
         if diarize_model is not None:
             diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
-            results_segments, word_segments = assign_word_speakers(diarize_segments)
+            results_segments, word_segments = assign_word_speakers(diarize_segments, result["segments"])
+            result = {"segments": results_segments, "word_segments": word_segments}
+
 
-        if tfile is not None:
-            tfile.close()
         writer(result, audio_path)
 
+        # cleanup
+        if input_audio_path != audio_path:
+            os.remove(input_audio_path)
 
 if __name__ == "__main__":
     cli()
\ No newline at end of file
diff --git a/whisperx/utils.py b/whisperx/utils.py
index 992960b..bde3558 100644
--- a/whisperx/utils.py
+++ b/whisperx/utils.py
@@ -236,7 +236,7 @@ class WritePickle(ResultWriter):
         pd.DataFrame(result["segments"]).to_pickle(file)
 
 class WriteSRTWord(ResultWriter):
-    extension: str = ".word.srt"
+    extension: str = "word.srt"
     always_include_hours: bool = True
     decimal_marker: str = ","
 

From bc2776017e9775a39b529ba89cf1dd8bca971db0 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Sat, 1 Apr 2023 00:09:29 +0100
Subject: [PATCH 08/48] v2 lets go

---
 README.md | 7 +++++++
 setup.py  | 2 +-
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e96a905..16e898e 100644
--- a/README.md
+++ b/README.md
@@ -78,6 +78,13 @@ $ pip install -e .
 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
 
 
+### Setup not working???
+Safest to use install pytorch as follows (for gpu)
+
+`conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 -c pytorch
+`
+
+
 ### Voice Activity Detection Filtering & Diarization
 To **enable VAD filtering and Diarization**, include your Hugging Face access token that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the following models: [Segmentation](https://huggingface.co/pyannote/segmentation) , [Voice Activity Detection (VAD)](https://huggingface.co/pyannote/voice-activity-detection) , and [Speaker Diarization](https://huggingface.co/pyannote/speaker-diarization)
 
diff --git a/setup.py b/setup.py
index 4aa1f3f..d6472e1 100644
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@ setup(
     version="2.0",
     description="Time-Accurate Automatic Speech Recognition using Whisper.",
     readme="README.md",
-    python_requires=">=3.7",
+    python_requires=">=3.8",
     author="Max Bain",
     url="https://github.com/m-bain/whisperx",
     license="MIT",

From 189aeac83e8b84885d7b072761a139a04ce31f7f Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Sat, 1 Apr 2023 00:10:45 +0100
Subject: [PATCH 09/48] v2 lets goo

---
 whisperx/alignment.py |  5 +----
 whisperx/vad.py       | 16 +---------------
 2 files changed, 2 insertions(+), 19 deletions(-)

diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index a906cf7..783a540 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -372,10 +372,7 @@ def align(
     for sdx, srow in segments_arr.iterrows():
 
         seg_idx = int(srow["segment-idx"])
-        try:
-            sub_start = int(srow["subsegment-idx-start"])
-        except:
-            import pdb; pdb.set_trace()
+        sub_start = int(srow["subsegment-idx-start"])
         sub_end = int(srow["subsegment-idx-end"])
 
         seg = transcript[seg_idx]
diff --git a/whisperx/vad.py b/whisperx/vad.py
index aa86060..6fac656 100644
--- a/whisperx/vad.py
+++ b/whisperx/vad.py
@@ -301,18 +301,4 @@ def merge_chunks(segments, chunk_size):
                 "end": curr_end,
                 "segments": seg_idxs,
             })    
-    return merged_segments
-
-
-if __name__ == "__main__":
-    import pandas as pd
-    input_fp = "tt298650_sync.wav"
-    df = pd.read_csv(f"/work/maxbain/tmp/{input_fp}.sad", sep=" ", header=None)
-    print(len(df))
-    N = 0.15
-    g = df[0].sub(df[1].shift())
-    input_base = input_fp.split('.')[0]
-    df = df.groupby(g.gt(N).cumsum()).agg({0:'min', 1:'max'})
-    df.to_csv(f"/work/maxbain/tmp/{input_base}.lab", header=None, index=False, sep=" ")
-    print(df)
-    import pdb; pdb.set_trace()
\ No newline at end of file
+    return merged_segments
\ No newline at end of file

From 634799b3be5a96d9a4156ec5fc5a34ce45101752 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Fri, 31 Mar 2023 16:15:40 -0700
Subject: [PATCH 10/48] hf token only for diarization

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 16e898e..7e410f2 100644
--- a/README.md
+++ b/README.md
@@ -85,8 +85,8 @@ Safest to use install pytorch as follows (for gpu)
 `
 
 
-### Voice Activity Detection Filtering & Diarization
-To **enable VAD filtering and Diarization**, include your Hugging Face access token that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the following models: [Segmentation](https://huggingface.co/pyannote/segmentation) , [Voice Activity Detection (VAD)](https://huggingface.co/pyannote/voice-activity-detection) , and [Speaker Diarization](https://huggingface.co/pyannote/speaker-diarization)
+### Speaker Diarization
+To **enable Speaker. Diarization**, include your Hugging Face access token that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the following models: [Segmentation](https://huggingface.co/pyannote/segmentation) , [Voice Activity Detection (VAD)](https://huggingface.co/pyannote/voice-activity-detection) , and [Speaker Diarization](https://huggingface.co/pyannote/speaker-diarization)
 
 
 <h2 align="left" id="example">Usage 💬 (command line)</h2>
@@ -255,4 +255,4 @@ as well the following works, used in each stage of the pipeline:
   year={2020},
   organization={IEEE}
 }
-```
\ No newline at end of file
+```

From 4af345434a041d80b4b338f50ffb32d9005cd1fe Mon Sep 17 00:00:00 2001
From: Ryan <50505980+Ryan5453@users.noreply.github.com>
Date: Fri, 31 Mar 2023 19:36:38 -0400
Subject: [PATCH 11/48] Update requirements.txt

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3b7ae35..69bca21 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,4 @@ more-itertools
 transformers>=4.19.0
 ffmpeg-python==0.2.0
 pyannote.audio
-whisper
\ No newline at end of file
+openai-whisper

From a582a594932be9e7584afff1f266e15f2f59c383 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Sat, 1 Apr 2023 13:05:40 -0700
Subject: [PATCH 12/48] mkdir for torch cache in case it doesnt exist

---
 whisperx/vad.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/whisperx/vad.py b/whisperx/vad.py
index 6fac656..0f15630 100644
--- a/whisperx/vad.py
+++ b/whisperx/vad.py
@@ -18,6 +18,7 @@ VAD_SEGMENTATION_URL = "https://whisperx.s3.eu-west-2.amazonaws.com/model_weight
 
 def load_vad_model(device, vad_onset, vad_offset, use_auth_token=None):
     model_dir = torch.hub._get_torch_home()
+    os.makedirs(model_dir, exist_ok = True)
     model_fp = os.path.join(model_dir, "whisperx-vad-segmentation.bin")
     if os.path.exists(model_fp) and not os.path.isfile(model_fp):
         raise RuntimeError(f"{model_fp} exists and is not a regular file")
@@ -301,4 +302,4 @@ def merge_chunks(segments, chunk_size):
                 "end": curr_end,
                 "segments": seg_idxs,
             })    
-    return merged_segments
\ No newline at end of file
+    return merged_segments

From c8be6ac94db8b10fd7c1eedc6ad89965f28b9a08 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Mon, 3 Apr 2023 12:18:31 -0700
Subject: [PATCH 13/48] update python example

---
 README.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 7e410f2..2bffa43 100644
--- a/README.md
+++ b/README.md
@@ -130,12 +130,13 @@ See more examples in other languages [here](EXAMPLES.md).
 
 ```python
 import whisperx
+import whisper
 
 device = "cuda" 
 audio_file = "audio.mp3"
 
 # transcribe with original whisper
-model = whisperx.load_model("large", device)
+model = whisper.load_model("large", device)
 result = model.transcribe(audio_file)
 
 print(result["segments"]) # before alignment
@@ -157,9 +158,6 @@ In addition to forced alignment, the following two modifications have been made
 
 1. `--condition_on_prev_text` is set to `False` by default (reduces hallucination)
 
-2. Clamping segment `end_time` to be at least 0.02s (one time precision) later than `start_time` (prevents segments with negative duration)
-
-
 <h2 align="left" id="limitations">Limitations ⚠️</h2>
 
 - Whisper normalises spoken numbers e.g. "fifty seven" to arabic numerals "57". Need to perform this normalization after alignment, so the phonemes can be aligned. Currently just ignores numbers.

From 40948a3d0097a2d404c5a4034b4cf3b79f43bdd4 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Tue, 4 Apr 2023 12:42:34 -0700
Subject: [PATCH 14/48] fix whisper version to 20230314 for no breaking

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 69bca21..139ee56 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,4 @@ more-itertools
 transformers>=4.19.0
 ffmpeg-python==0.2.0
 pyannote.audio
-openai-whisper
+openai-whisper==20230314

From 70a4a0a25caa7a715f320681bc0cd5d7e241e79f Mon Sep 17 00:00:00 2001
From: Kevin Dias <diasks2@gmail.com>
Date: Wed, 5 Apr 2023 10:50:49 +0900
Subject: [PATCH 15/48] Fix typo

---
 whisperx/utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/whisperx/utils.py b/whisperx/utils.py
index bde3558..14e298b 100644
--- a/whisperx/utils.py
+++ b/whisperx/utils.py
@@ -221,13 +221,13 @@ class WriteASS(ResultWriter):
     extension: str = "ass"
 
     def write_result(self, result: dict, file: TextIO):
-        write_ass(result["segments"], file, resoltuion="word")
+        write_ass(result["segments"], file, resolution="word")
 
 class WriteASSchar(ResultWriter):
     extension: str = "ass"
 
     def write_result(self, result: dict, file: TextIO):
-        write_ass(result["segments"], file, resoltuion="char")
+        write_ass(result["segments"], file, resolution="char")
 
 class WritePickle(ResultWriter):
     extension: str = "ass"

From 4146e56d5b2e84f1ae92576cdc68ce86d463bef2 Mon Sep 17 00:00:00 2001
From: dev-nomi <noman.khalid.freelance@gmail.com>
Date: Wed, 5 Apr 2023 17:11:29 +0500
Subject: [PATCH 16/48] Added vad_filter type

---
 whisperx/transcribe.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 9c0c4b5..e8fa47b 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -44,7 +44,7 @@ def cli():
     parser.add_argument("--no_align", action='store_true', help="Do not perform phoneme alignment")
 
     # vad params
-    parser.add_argument("--vad_filter", default=True, help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747")
+    parser.add_argument("--vad_filter", type=str2bool, default=True, help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747")
     parser.add_argument("--vad_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected")
     parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected.")
 

From bb15c9428f23df70636ae000d388697fea5ae2fa Mon Sep 17 00:00:00 2001
From: invisprints <invisprints@outlook.com>
Date: Sun, 9 Apr 2023 15:58:55 +0800
Subject: [PATCH 17/48] opti the inference loop

---
 whisperx/transcribe.py | 75 ++++++++++++++++++++++++++----------------
 1 file changed, 47 insertions(+), 28 deletions(-)

diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index e8fa47b..ed918e0 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -1,5 +1,6 @@
 import argparse
 import os
+import gc
 import warnings
 from typing import TYPE_CHECKING, Optional, Tuple, Union
 import numpy as np
@@ -113,19 +114,6 @@ def cli():
     else:
         vad_model = None
 
-    if diarize:
-        if hf_token is None:
-            print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
-        diarize_model = DiarizationPipeline(use_auth_token=hf_token)
-    else:
-        diarize_model = None
-
-    if no_align:
-        align_model, align_metadata = None, None
-    else:
-        align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified
-        align_model, align_metadata = load_align_model(align_language, device, model_name=align_model)     
-
     # if model_flush:
     #     print(">>Model flushing activated... Only loading model after ASR stage")
     #     del align_model
@@ -150,9 +138,12 @@ def cli():
 
     from whisper import load_model
 
-    model = load_model(model_name, device=device, download_root=model_dir)
-
     writer = get_writer(output_format, output_dir)
+
+    # Part 1: VAD & ASR Loop
+    results = []
+    tmp_results = []
+    model = load_model(model_name, device=device, download_root=model_dir)
     for audio_path in args.pop("audio"):
         input_audio_path = audio_path
         tfile = None
@@ -161,7 +152,6 @@ def cli():
         if vad_model is not None:
             if not audio_path.endswith(".wav"):
                 print(">>VAD requires .wav format, converting to wav as a tempfile...")
-                # tfile = tempfile.NamedTemporaryFile(delete=True, suffix=".wav")
                 audio_basename = os.path.splitext(os.path.basename(audio_path))[0]
                 if tmp_dir is not None:
                     input_audio_path = os.path.join(tmp_dir, audio_basename + ".wav")
@@ -173,24 +163,53 @@ def cli():
         else:
             print(">>Performing transcription...")
             result = transcribe(model, input_audio_path, temperature=temperature, **args)
+        
+        results.append((result, input_audio_path))
 
-        # >> Align
-        if align_model is not None and len(result["segments"]) > 0:
-            if result.get("language", "en") != align_metadata["language"]:
-                # load new language
-                print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
-                align_model, align_metadata = load_align_model(result["language"], device)
-            print(">>Performing alignment...")
-            result = align(result["segments"], align_model, align_metadata, input_audio_path, device,
-                extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
+    # Unload Whisper and VAD
+    del model
+    del vad_model
+    gc.collect()
+    torch.cuda.empty_cache()
 
-        # >> Diarize
-        if diarize_model is not None:
+    # Part 2: Align Loop
+    if not no_align:
+        tmp_results = results
+        results = []
+        align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified
+        align_model, align_metadata = load_align_model(align_language, device, model_name=align_model)
+        for result, input_audio_path in tmp_results:
+            # >> Align
+            if align_model is not None and len(result["segments"]) > 0:
+                if result.get("language", "en") != align_metadata["language"]:
+                    # load new language
+                    print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
+                    align_model, align_metadata = load_align_model(result["language"], device)
+                print(">>Performing alignment...")
+                result = align(result["segments"], align_model, align_metadata, input_audio_path, device,
+                    extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
+            results.append((result, input_audio_path))
+
+        # Unload align model
+        del align_model
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    # >> Diarize
+    if diarize:
+        if hf_token is None:
+            print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
+        tmp_results = results
+        results = []
+        diarize_model = DiarizationPipeline(use_auth_token=hf_token)
+        for result, input_audio_path in tmp_results:
             diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
             results_segments, word_segments = assign_word_speakers(diarize_segments, result["segments"])
             result = {"segments": results_segments, "word_segments": word_segments}
+            results.append((result, input_audio_path))
 
-
+    # >> Write
+    for result, audio_path in results:
         writer(result, audio_path)
 
         # cleanup

From 6a72b61564dbea7242b8d4eab74af375df516759 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Tue, 11 Apr 2023 20:15:37 +0100
Subject: [PATCH 18/48] clamp end_timestamp to prevent infinite loop

---
 whisperx/asr.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/whisperx/asr.py b/whisperx/asr.py
index ac16459..e78d77c 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -269,6 +269,10 @@ def transcribe(
                     end_timestamp_pos = (
                         sliced_tokens[-1].item() - tokenizer.timestamp_begin
                     )
+                    
+                    # clamp end-time to at least be 1 frame after start-time
+                    end_timestamp_pos = max(end_timestamp_pos, start_timestamp_pos + time_precision)
+
                     current_segments.append(
                         new_segment(
                             start=time_offset + start_timestamp_pos * time_precision,
@@ -426,4 +430,4 @@ def transcribe_with_vad(
 
     output["language"] = output["segments"][0]["language"]
 
-    return output
\ No newline at end of file
+    return output

From cf252a8592c651648c781d0d6d6f1bc1a2f1c217 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Fri, 14 Apr 2023 15:02:58 +0100
Subject: [PATCH 19/48] allow custom path for vad model

---
 whisperx/vad.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/whisperx/vad.py b/whisperx/vad.py
index 0f15630..933d270 100644
--- a/whisperx/vad.py
+++ b/whisperx/vad.py
@@ -16,10 +16,11 @@ from typing import List, Tuple, Optional
 
 VAD_SEGMENTATION_URL = "https://whisperx.s3.eu-west-2.amazonaws.com/model_weights/segmentation/0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea/pytorch_model.bin"
 
-def load_vad_model(device, vad_onset, vad_offset, use_auth_token=None):
+def load_vad_model(device, vad_onset, vad_offset, use_auth_token=None, model_fp=None):
     model_dir = torch.hub._get_torch_home()
     os.makedirs(model_dir, exist_ok = True)
-    model_fp = os.path.join(model_dir, "whisperx-vad-segmentation.bin")
+    if model_fp is None:
+        model_fp = os.path.join(model_dir, "whisperx-vad-segmentation.bin")
     if os.path.exists(model_fp) and not os.path.isfile(model_fp):
         raise RuntimeError(f"{model_fp} exists and is not a regular file")
 

From da458863d7ee608385d1ac4de843d7755a6865a5 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Fri, 14 Apr 2023 21:40:36 +0100
Subject: [PATCH 20/48] allow custom model_dir for torchaudio models

---
 whisperx/alignment.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index 783a540..c15310b 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -40,7 +40,7 @@ DEFAULT_ALIGN_MODELS_HF = {
 }
 
 
-def load_align_model(language_code, device, model_name=None):
+def load_align_model(language_code, device, model_name=None, model_dir=None):
     if model_name is None:
         # use default model
         if language_code in DEFAULT_ALIGN_MODELS_TORCH:
@@ -55,7 +55,7 @@ def load_align_model(language_code, device, model_name=None):
     if model_name in torchaudio.pipelines.__all__:
         pipeline_type = "torchaudio"
         bundle = torchaudio.pipelines.__dict__[model_name]
-        align_model = bundle.get_model().to(device)
+        align_model = bundle.get_model(dl_kwargs={"model_dir": model_dir}).to(device)
         labels = bundle.get_labels()
         align_dictionary = {c.lower(): i for i, c in enumerate(labels)}
     else:

From 558d98053541d23d8ccb0ce7c1842c65087c2846 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Mon, 24 Apr 2023 21:08:43 +0100
Subject: [PATCH 21/48] v3 init

---
 README.md                       |  89 ++--
 requirements.txt                |  16 +-
 setup.py                        |   2 +-
 whisperx/__init__.py            |   4 +-
 whisperx/alignment.py           |  66 ++-
 whisperx/asr.py                 | 789 +++++++++++++++-----------------
 whisperx/assets/mel_filters.npz | Bin 0 -> 2048 bytes
 whisperx/audio.py               | 147 ++++++
 whisperx/transcribe.py          | 132 +++---
 whisperx/utils.py               | 616 ++++++++++++++-----------
 whisperx/vad.py                 |  19 +-
 11 files changed, 1034 insertions(+), 846 deletions(-)
 create mode 100644 whisperx/assets/mel_filters.npz
 create mode 100644 whisperx/audio.py

diff --git a/README.md b/README.md
index 2bffa43..b5b95c5 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,7 @@
 <img width="1216" align="center" alt="whisperx-arch" src="figures/pipeline.png">
 
 
-<p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy using forced alignment.
+<p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy + quality via forced phoneme alignment and speech-activity batching.
 
 </p>
 
@@ -52,6 +52,7 @@ This repository refines the timestamps of openAI's Whisper model via forced alig
 
 <h2 align="left", id="highlights">New🚨</h2>
 
+- v3 released, 70x speed-up open-sourced. Using batched whisper with [faster-whisper](https://github.com/guillaumekln/faster-whisper) backend!
 - v2 released, code cleanup, imports whisper library, batched inference from paper not included (contact for licensing / batched model API). VAD filtering is now turned on by default, as in the paper.
 - Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed (not provided in this repo).
 - VAD filtering: Voice Activity Detection (VAD) from [Pyannote.audio](https://huggingface.co/pyannote/voice-activity-detection) is used as a preprocessing step to remove reliance on whisper timestamps and only transcribe audio segments containing speech. add `--vad_filter True` flag, increases timestamp accuracy and robustness (requires more GPU mem due to 30s inputs in wav2vec2)
@@ -60,7 +61,25 @@ This repository refines the timestamps of openAI's Whisper model via forced alig
 
 
 <h2 align="left" id="setup">Setup ⚙️</h2>
-Install this package using
+Tested for PyTorch 0.11, Python 3.8 (use other versions at your own risk!)
+
+GPU execution requires the NVIDIA libraries cuBLAS 11.x and cuDNN 8.x to be installed on the system. Please refer to the [CTranslate2 documentation](https://opennmt.net/CTranslate2/installation.html).
+
+
+### 1. Create Python3.8 environment
+
+`conda create --name whisperx python=3.8`
+
+`conda activate whisperx`
+
+
+### 2. Install PyTorch 0.11.0, e.g. for Linux and Windows:
+
+`conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch`
+
+See other methods [here.](https://pytorch.org/get-started/previous-versions/#wheel-4)
+
+### 3. Install this repo
 
 `pip install git+https://github.com/m-bain/whisperx.git`
 
@@ -78,13 +97,6 @@ $ pip install -e .
 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
 
 
-### Setup not working???
-Safest to use install pytorch as follows (for gpu)
-
-`conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 -c pytorch
-`
-
-
 ### Speaker Diarization
 To **enable Speaker. Diarization**, include your Hugging Face access token that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the following models: [Segmentation](https://huggingface.co/pyannote/segmentation) , [Voice Activity Detection (VAD)](https://huggingface.co/pyannote/voice-activity-detection) , and [Speaker Diarization](https://huggingface.co/pyannote/speaker-diarization)
 
@@ -130,14 +142,15 @@ See more examples in other languages [here](EXAMPLES.md).
 
 ```python
 import whisperx
-import whisper
 
 device = "cuda" 
 audio_file = "audio.mp3"
 
 # transcribe with original whisper
-model = whisper.load_model("large", device)
-result = model.transcribe(audio_file)
+model = whisperx.load_model("large-v2", device)
+
+audio = whisperx.load_audio(audio_file)
+result = model.transcribe(audio, batch_size=8)
 
 print(result["segments"]) # before alignment
 
@@ -145,7 +158,7 @@ print(result["segments"]) # before alignment
 model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
 
 # align whisper output
-result_aligned = whisperx.align(result["segments"], model_a, metadata, audio_file, device)
+result_aligned = whisperx.align(result["segments"], model_a, metadata, audio, device)
 
 print(result_aligned["segments"]) # after alignment
 print(result_aligned["word_segments"]) # after alignment
@@ -186,9 +199,15 @@ The next major upgrade we are working on is whisper with speaker diarization, so
 
 * [x] Incorporating  speaker diarization
 
-* [ ] Automatic .wav conversion to make VAD compatible
+* [x] Model flush, for low gpu mem resources
 
-* [ ] Model flush, for low gpu mem resources
+* [x] Faster-whisper backend
+
+* [ ] Add benchmarking code (TEDLIUM for spd/WER & word segmentation)
+
+* [ ] Allow silero-vad as alternative VAD option
+
+* [ ] Add max-line etc. see (openai's whisper utils.py)
 
 * [ ] Improve diarization (word level). *Harder than first thought...*
 
@@ -205,10 +224,13 @@ Contact maxhbain@gmail.com for queries and licensing / early access to a model A
 This work, and my PhD, is supported by the [VGG (Visual Geometry Group)](https://www.robots.ox.ac.uk/~vgg/) and the University of Oxford.
 
 
-
 Of course, this is builds on [openAI's whisper](https://github.com/openai/whisper).
 And borrows important alignment code from [PyTorch tutorial on forced alignment](https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html)
 
+Valuable VAD & Diarization Models from (pyannote.audio)[https://github.com/pyannote/pyannote-audio]
+
+Great backend from (faster-whisper)[https://github.com/guillaumekln/faster-whisper] and (CTranslate2)[https://github.com/OpenNMT/CTranslate2]
+
 
 <h2 align="left" id="cite">Citation</h2>
 If you use this in your research, please cite the paper:
@@ -220,37 +242,4 @@ If you use this in your research, please cite the paper:
   journal={arXiv preprint, arXiv:2303.00747},
   year={2023}
 }
-```
-
-as well the following works, used in each stage of the pipeline:
-
-```bibtex
-@article{radford2022robust,
-  title={Robust speech recognition via large-scale weak supervision},
-  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
-  journal={arXiv preprint arXiv:2212.04356},
-  year={2022}
-}
-```
-
-```bibtex
-@article{baevski2020wav2vec,
-  title={wav2vec 2.0: A framework for self-supervised learning of speech representations},
-  author={Baevski, Alexei and Zhou, Yuhao and Mohamed, Abdelrahman and Auli, Michael},
-  journal={Advances in neural information processing systems},
-  volume={33},
-  pages={12449--12460},
-  year={2020}
-}
-```
-
-```bibtex
-@inproceedings{bredin2020pyannote,
-  title={Pyannote. audio: neural building blocks for speaker diarization},
-  author={Bredin, Herv{\'e} and Yin, Ruiqing and Coria, Juan Manuel and Gelly, Gregory and Korshunov, Pavel and Lavechin, Marvin and Fustes, Diego and Titeux, Hadrien and Bouaziz, Wassim and Gill, Marie-Philippe},
-  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
-  pages={7124--7128},
-  year={2020},
-  organization={IEEE}
-}
-```
+```
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 139ee56..2747e2d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,8 @@
-numpy
-pandas
-torch >=1.9
-torchaudio >=0.10,<1.0
-tqdm
-more-itertools
-transformers>=4.19.0
-ffmpeg-python==0.2.0
+torch==1.11.0
+torchaudio==0.11.0
 pyannote.audio
-openai-whisper==20230314
+faster-whisper
+transformers
+ffmpeg-python==0.2.0
+pandas
+setuptools==65.6.3
\ No newline at end of file
diff --git a/setup.py b/setup.py
index d6472e1..2060d42 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ from setuptools import setup, find_packages
 setup(
     name="whisperx",
     py_modules=["whisperx"],
-    version="2.0",
+    version="3.0.0",
     description="Time-Accurate Automatic Speech Recognition using Whisper.",
     readme="README.md",
     python_requires=">=3.8",
diff --git a/whisperx/__init__.py b/whisperx/__init__.py
index 985ed32..d0294b9 100644
--- a/whisperx/__init__.py
+++ b/whisperx/__init__.py
@@ -1,3 +1,3 @@
-from .transcribe import transcribe, transcribe_with_vad
+from .transcribe import load_model
 from .alignment import load_align_model, align
-from .vad import load_vad_model
\ No newline at end of file
+from .audio import load_audio
\ No newline at end of file
diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index c15310b..09f044f 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -2,16 +2,17 @@
 Forced Alignment with Whisper
 C. Max Bain
 """
+from dataclasses import dataclass
+from typing import Iterator, Union
+
 import numpy as np
 import pandas as pd
-from typing import List, Union, Iterator, TYPE_CHECKING
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-import torchaudio
 import torch
-from dataclasses import dataclass
-from whisper.audio import SAMPLE_RATE, load_audio
-from .utils import interpolate_nans
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
+from .audio import SAMPLE_RATE, load_audio
+from .utils import interpolate_nans
 
 LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]
 
@@ -391,34 +392,42 @@ def align(
         if 'level_1' in cseg: del cseg['level_1']
         if 'level_0' in cseg: del cseg['level_0']
         cseg.reset_index(inplace=True)
-        aligned_segments.append(
-            {
-                "start": srow["start"],
-                "end": srow["end"],
-                "text": text,
-                "word-segments": wseg,
-                "char-segments": cseg
-            }
-        )
 
         def get_raw_text(word_row):
             return seg["seg-text"][word_row.name][int(word_row["segment-text-start"]):int(word_row["segment-text-end"])+1]
 
+        word_list = []
         wdx = 0
         curr_text = get_raw_text(wseg.iloc[wdx])
+        if not curr_text.startswith(" "):
+            curr_text = " " + curr_text
+            
         if len(wseg) > 1:
             for _, wrow in wseg.iloc[1:].iterrows():
                 if wrow['start'] != wseg.iloc[wdx]['start']:
+                    word_start = wseg.iloc[wdx]['start']
+                    word_end = wseg.iloc[wdx]['end']
+
                     aligned_segments_word.append(
                         {
                             "text": curr_text.strip(),
-                            "start": wseg.iloc[wdx]["start"],
-                            "end": wseg.iloc[wdx]["end"],
+                            "start": word_start,
+                            "end": word_end
                         }
                     )
-                    curr_text = ""
-                curr_text += " " + get_raw_text(wrow)
+
+                    word_list.append(
+                        {
+                            "word": curr_text.rstrip(),
+                            "start": word_start,
+                            "end": word_end,
+                        }
+                    )
+
+                    curr_text = " "
+                curr_text += get_raw_text(wrow) + " "
                 wdx += 1
+
         aligned_segments_word.append(
             {
                 "text": curr_text.strip(),
@@ -427,6 +436,25 @@ def align(
             }
         )
 
+        word_list.append(
+            {
+                "word": curr_text.rstrip(),
+                "start": word_start,
+                "end": word_end,
+            }
+        )
+
+        aligned_segments.append(
+            {
+                "start": srow["start"],
+                "end": srow["end"],
+                "text": text,
+                "words": word_list,
+                # "word-segments": wseg,
+                # "char-segments": cseg
+            }
+        )
+    
     
     return {"segments": aligned_segments, "word_segments": aligned_segments_word}
 
diff --git a/whisperx/asr.py b/whisperx/asr.py
index e78d77c..d9cbff0 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -1,433 +1,406 @@
+import os
 import warnings
-from typing import TYPE_CHECKING, Optional, Tuple, Union
+from typing import List, Union
+
+import ctranslate2
+import faster_whisper
 import numpy as np
 import torch
-import tqdm
-import ffmpeg
-from whisper.audio import (
-    FRAMES_PER_SECOND,
-    HOP_LENGTH,
-    N_FRAMES,
-    N_SAMPLES,
-    SAMPLE_RATE,
-    CHUNK_LENGTH,
-    log_mel_spectrogram,
-    pad_or_trim,
-    load_audio
-)
-from whisper.decoding import DecodingOptions, DecodingResult
-from whisper.timing import add_word_timestamps
-from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
-from whisper.utils import (
-    exact_div,
-    format_timestamp,
-    make_safe,
-)
+from transformers import Pipeline
+from transformers.pipelines.pt_utils import PipelineIterator
 
-if TYPE_CHECKING:
-    from whisper.model import Whisper
+from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
+from .vad import load_vad_model, merge_chunks
 
-from .vad import merge_chunks
 
-def transcribe(
-    model: "Whisper",
-    audio: Union[str, np.ndarray, torch.Tensor] = None,
-    mel: np.ndarray = None,
-    verbose: Optional[bool] = None,
-    temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
-    compression_ratio_threshold: Optional[float] = 2.4,
-    logprob_threshold: Optional[float] = -1.0,
-    no_speech_threshold: Optional[float] = 0.6,
-    condition_on_previous_text: bool = True,
-    initial_prompt: Optional[str] = None,
-    word_timestamps: bool = False,
-    prepend_punctuations: str = "\"'“¿([{-",
-    append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
-    **decode_options,
-):
-    """
-    Transcribe an audio file using Whisper.
-    We redefine the Whisper transcribe function to allow mel input (for sequential slicing of audio)
+def load_model(whisper_arch, device, compute_type="float16", asr_options=None, language=None,
+               vad_options=None, model=None):
+    '''Load a Whisper model for inference.
+    Args:
+        whisper_arch: str - The name of the Whisper model to load.
+        device: str - The device to load the model on.
+        compute_type: str - The compute type to use for the model.
+        options: dict - A dictionary of options to use for the model.
+        language: str - The language of the model. (use English for now)
+    Returns:
+        A Whisper pipeline.
+    '''    
 
-    Parameters
-    ----------
-    model: Whisper
-        The Whisper model instance
+    if whisper_arch.endswith(".en"):
+        language = "en"
 
-    audio: Union[str, np.ndarray, torch.Tensor]
-        The path to the audio file to open, or the audio waveform
-
-    mel: np.ndarray
-        Mel spectrogram of audio segment.
-        
-    verbose: bool
-        Whether to display the text being decoded to the console. If True, displays all the details,
-        If False, displays minimal details. If None, does not display anything
-
-    temperature: Union[float, Tuple[float, ...]]
-        Temperature for sampling. It can be a tuple of temperatures, which will be successively used
-        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
-
-    compression_ratio_threshold: float
-        If the gzip compression ratio is above this value, treat as failed
-
-    logprob_threshold: float
-        If the average log probability over sampled tokens is below this value, treat as failed
-
-    no_speech_threshold: float
-        If the no_speech probability is higher than this value AND the average log probability
-        over sampled tokens is below `logprob_threshold`, consider the segment as silent
-
-    condition_on_previous_text: bool
-        if True, the previous output of the model is provided as a prompt for the next window;
-        disabling may make the text inconsistent across windows, but the model becomes less prone to
-        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
-
-    word_timestamps: bool
-        Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
-        and include the timestamps for each word in each segment.
-
-    prepend_punctuations: str
-        If word_timestamps is True, merge these punctuation symbols with the next word
-
-    append_punctuations: str
-        If word_timestamps is True, merge these punctuation symbols with the previous word
-
-    initial_prompt: Optional[str]
-        Optional text to provide as a prompt for the first window. This can be used to provide, or
-        "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
-        to make it more likely to predict those word correctly.
-
-    decode_options: dict
-        Keyword arguments to construct `DecodingOptions` instances
-
-    Returns
-    -------
-    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
-    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
-    """
-    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
-    if model.device == torch.device("cpu"):
-        if torch.cuda.is_available():
-            warnings.warn("Performing inference on CPU when CUDA is available")
-        if dtype == torch.float16:
-            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
-            dtype = torch.float32
-
-    if dtype == torch.float32:
-        decode_options["fp16"] = False
-
-    # Pad 30-seconds of silence to the input audio, for slicing
-    if mel is None:
-        if audio is None:
-            raise ValueError("Transcribe needs either audio or mel as input, currently both are none.")
-        mel = log_mel_spectrogram(audio, padding=N_SAMPLES)
-    content_frames = mel.shape[-1] - N_FRAMES
-
-    if decode_options.get("language", None) is None:
-        if not model.is_multilingual:
-            decode_options["language"] = "en"
-        else:
-            if verbose:
-                print(
-                    "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
-                )
-            mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
-            _, probs = model.detect_language(mel_segment)
-            decode_options["language"] = max(probs, key=probs.get)
-            if verbose is not None:
-                print(
-                    f"Detected language: {LANGUAGES[decode_options['language']].title()}"
-                )
-
-    language: str = decode_options["language"]
-    task: str = decode_options.get("task", "transcribe")
-    tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
-
-    if word_timestamps and task == "translate":
-        warnings.warn("Word-level timestamps on translations may not be reliable.")
-
-    def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
-        temperatures = (
-            [temperature] if isinstance(temperature, (int, float)) else temperature
-        )
-        decode_result = None
-
-        for t in temperatures:
-            kwargs = {**decode_options}
-            if t > 0:
-                # disable beam_size and patience when t > 0
-                kwargs.pop("beam_size", None)
-                kwargs.pop("patience", None)
-            else:
-                # disable best_of when t == 0
-                kwargs.pop("best_of", None)
-
-            options = DecodingOptions(**kwargs, temperature=t)
-            decode_result = model.decode(segment, options)
-
-            needs_fallback = False
-            if (
-                compression_ratio_threshold is not None
-                and decode_result.compression_ratio > compression_ratio_threshold
-            ):
-                needs_fallback = True  # too repetitive
-            if (
-                logprob_threshold is not None
-                and decode_result.avg_logprob < logprob_threshold
-            ):
-                needs_fallback = True  # average log probability is too low
-
-            if not needs_fallback:
-                break
-
-        return decode_result
-
-    seek = 0
-    input_stride = exact_div(
-        N_FRAMES, model.dims.n_audio_ctx
-    )  # mel frames per output token: 2
-    time_precision = (
-        input_stride * HOP_LENGTH / SAMPLE_RATE
-    )  # time per output token: 0.02 (seconds)
-    all_tokens = []
-    all_segments = []
-    prompt_reset_since = 0
-
-    if initial_prompt is not None:
-        initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
-        all_tokens.extend(initial_prompt_tokens)
+    model = WhisperModel(whisper_arch, device=device, compute_type=compute_type)
+    if language is not None:
+        tokenizer = faster_whisper.tokenizer.Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task="transcribe", language=language)
     else:
-        initial_prompt_tokens = []
+        print("No language specified, language will be first be detected for each audio file (increases inference time).")
+        tokenizer = None
 
-    def new_segment(
-        *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult
+    default_asr_options =  {
+        "beam_size": 5,
+        "best_of": 5,
+        "patience": 1,
+        "length_penalty": 1,
+        "temperatures": [0.0, 0.2, 0.4, 0.6, 0.8, 1.0],
+        "compression_ratio_threshold": 2.4,
+        "log_prob_threshold": -1.0,
+        "no_speech_threshold": 0.6,
+        "condition_on_previous_text": False,
+        "initial_prompt": None,
+        "prefix": None,
+        "suppress_blank": True,
+        "suppress_tokens": [-1],
+        "without_timestamps": True,
+        "max_initial_timestamp": 0.0,
+        "word_timestamps": False,
+        "prepend_punctuations": "\"'“¿([{-",
+        "append_punctuations": "\"'.。,，!！?？:：”)]}、"
+    }
+
+    if asr_options is not None:
+        default_asr_options.update(asr_options)
+    default_asr_options = faster_whisper.transcribe.TranscriptionOptions(**default_asr_options)
+
+    default_vad_options = {
+        "vad_onset": 0.500,
+        "vad_offset": 0.363
+    }
+
+    if vad_options is not None:
+        default_vad_options.update(vad_options)
+
+    vad_model = load_vad_model(torch.device(device), use_auth_token=None, **default_vad_options)
+
+    return FasterWhisperPipeline(model, vad_model, default_asr_options, tokenizer)
+
+
+
+class WhisperModel(faster_whisper.WhisperModel):
+    '''
+    FasterWhisperModel provides batched inference for faster-whisper.
+    Currently only works in non-timestamp mode.
+    '''
+
+    def generate_segment_batched(self, features: np.ndarray, tokenizer: faster_whisper.tokenizer.Tokenizer, options: faster_whisper.transcribe.TranscriptionOptions, encoder_output = None):
+        batch_size = features.shape[0]
+        all_tokens = []
+        prompt_reset_since = 0
+        if options.initial_prompt is not None:
+            initial_prompt = " " + options.initial_prompt.strip()
+            initial_prompt_tokens = tokenizer.encode(initial_prompt)
+            all_tokens.extend(initial_prompt_tokens)
+        previous_tokens = all_tokens[prompt_reset_since:]
+        prompt = self.get_prompt(
+            tokenizer,
+            previous_tokens,
+            without_timestamps=options.without_timestamps,
+            prefix=options.prefix,
+        )
+
+        encoder_output = self.encode(features)
+
+        max_initial_timestamp_index = int(
+            round(options.max_initial_timestamp / self.time_precision)
+        )
+
+        result = self.model.generate(
+                encoder_output,
+                [prompt] * batch_size,
+                # length_penalty=options.length_penalty,
+                # max_length=self.max_length,
+                # return_scores=True,
+                # return_no_speech_prob=True,
+                # suppress_blank=options.suppress_blank,
+                # suppress_tokens=options.suppress_tokens,
+                # max_initial_timestamp_index=max_initial_timestamp_index,
+            )
+    
+        tokens_batch = [x.sequences_ids[0] for x in result]
+
+        def decode_batch(tokens: List[List[int]]) -> str:
+            res = []
+            for tk in tokens:
+                res.append([token for token in tk if token < tokenizer.eot])
+            # text_tokens = [token for token in tokens if token < self.eot]
+            return tokenizer.tokenizer.decode_batch(res)
+
+        text = decode_batch(tokens_batch)
+
+        return text
+    
+    def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
+        # When the model is running on multiple GPUs, the encoder output should be moved
+        # to the CPU since we don't know which GPU will handle the next job.
+        to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
+        # unsqueeze if batch size = 1
+        if len(features.shape) == 2:
+            features = np.expand_dims(features, 0)
+        features = faster_whisper.transcribe.get_ctranslate2_storage(features)
+        
+        return self.model.encode(features, to_cpu=to_cpu)
+    
+class FasterWhisperPipeline(Pipeline):
+    def __init__(
+            self,
+            model,
+            vad,
+            options,
+            tokenizer=None,
+            device: Union[int, str, "torch.device"] = -1,
+            framework = "pt",
+            **kwargs
     ):
-        tokens = tokens.tolist()
-        text_tokens = [token for token in tokens if token < tokenizer.eot]
-        return {
-            "seek": seek,
-            "start": start,
-            "end": end,
-            "text": tokenizer.decode(text_tokens),
-            "tokens": tokens,
-            "temperature": result.temperature,
-            "avg_logprob": result.avg_logprob,
-            "compression_ratio": result.compression_ratio,
-            "no_speech_prob": result.no_speech_prob,
-        }
-
-
-    # show the progress bar when verbose is False (if True, transcribed text will be printed)
-    with tqdm.tqdm(
-        total=content_frames, unit="frames", disable=verbose is not False
-    ) as pbar:
-        while seek < content_frames:
-            time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
-            mel_segment = mel[:, seek : seek + N_FRAMES]
-            segment_size = min(N_FRAMES, content_frames - seek)
-            segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
-            mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
-
-            decode_options["prompt"] = all_tokens[prompt_reset_since:]
-            result: DecodingResult = decode_with_fallback(mel_segment)
-            tokens = torch.tensor(result.tokens)
-            if no_speech_threshold is not None:
-                # no voice activity check
-                should_skip = result.no_speech_prob > no_speech_threshold
-                if (
-                    logprob_threshold is not None
-                    and result.avg_logprob > logprob_threshold
-                ):
-                    # don't skip if the logprob is high enough, despite the no_speech_prob
-                    should_skip = False
-
-                if should_skip:
-                    seek += segment_size  # fast-forward to the next segment boundary
-                    continue
-
-            previous_seek = seek
-            current_segments = []
-
-            timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
-            single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]
-
-            consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
-            consecutive.add_(1)
-            if len(consecutive) > 0:
-                # if the output contains two consecutive timestamp tokens
-                slices = consecutive.tolist()
-                if single_timestamp_ending:
-                    slices.append(len(tokens))
-
-                last_slice = 0
-                for current_slice in slices:
-                    sliced_tokens = tokens[last_slice:current_slice]
-                    start_timestamp_pos = (
-                        sliced_tokens[0].item() - tokenizer.timestamp_begin
-                    )
-                    end_timestamp_pos = (
-                        sliced_tokens[-1].item() - tokenizer.timestamp_begin
-                    )
-                    
-                    # clamp end-time to at least be 1 frame after start-time
-                    end_timestamp_pos = max(end_timestamp_pos, start_timestamp_pos + time_precision)
-
-                    current_segments.append(
-                        new_segment(
-                            start=time_offset + start_timestamp_pos * time_precision,
-                            end=time_offset + end_timestamp_pos * time_precision,
-                            tokens=sliced_tokens,
-                            result=result,
-                        )
-                    )
-                    last_slice = current_slice
-
-                if single_timestamp_ending:
-                    # single timestamp at the end means no speech after the last timestamp.
-                    seek += segment_size
-                else:
-                    # otherwise, ignore the unfinished segment and seek to the last timestamp
-                    last_timestamp_pos = (
-                        tokens[last_slice - 1].item() - tokenizer.timestamp_begin
-                    )
-                    seek += last_timestamp_pos * input_stride
+        self.model = model
+        self.tokenizer = tokenizer
+        self.options = options
+        self._batch_size = kwargs.pop("batch_size", None)
+        self._num_workers = 1
+        self._preprocess_params, self._forward_params, self._postprocess_params = self._sanitize_parameters(**kwargs)
+        self.call_count = 0
+        self.framework = framework
+        if self.framework == "pt":
+            if isinstance(device, torch.device):
+                self.device = device
+            elif isinstance(device, str):
+                self.device = torch.device(device)
+            elif device < 0:
+                self.device = torch.device("cpu")
             else:
-                duration = segment_duration
-                timestamps = tokens[timestamp_tokens.nonzero().flatten()]
-                if (
-                    len(timestamps) > 0
-                    and timestamps[-1].item() != tokenizer.timestamp_begin
-                ):
-                    # no consecutive timestamps but it has a timestamp; use the last one.
-                    last_timestamp_pos = (
-                        timestamps[-1].item() - tokenizer.timestamp_begin
-                    )
-                    duration = last_timestamp_pos * time_precision
+                self.device = torch.device(f"cuda:{device}")
+        else:
+            self.device = device
+        
+        super(Pipeline, self).__init__()
+        self.vad_model = vad
 
-                current_segments.append(
-                    new_segment(
-                        start=time_offset,
-                        end=time_offset + duration,
-                        tokens=tokens,
-                        result=result,
-                    )
-                )
-                seek += segment_size
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs = {}
+        if "tokenizer" in kwargs:
+            preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
+        return preprocess_kwargs, {}, {}
 
-            if not condition_on_previous_text or result.temperature > 0.5:
-                # do not feed the prompt tokens if a high temperature was used
-                prompt_reset_since = len(all_tokens)
+    def preprocess(self, audio):
+        audio = audio['inputs']
+        features = log_mel_spectrogram(audio, padding=N_SAMPLES - audio.shape[0])
+        return {'inputs': features}
 
-            if word_timestamps:
-                add_word_timestamps(
-                    segments=current_segments,
-                    model=model,
-                    tokenizer=tokenizer,
-                    mel=mel_segment,
-                    num_frames=segment_size,
-                    prepend_punctuations=prepend_punctuations,
-                    append_punctuations=append_punctuations,
-                )
-                word_end_timestamps = [
-                    w["end"] for s in current_segments for w in s["words"]
-                ]
-                if not single_timestamp_ending and len(word_end_timestamps) > 0:
-                    seek_shift = round(
-                        (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND
-                    )
-                    if seek_shift > 0:
-                        seek = previous_seek + seek_shift
+    def _forward(self, model_inputs):
+        outputs = self.model.generate_segment_batched(model_inputs['inputs'], self.tokenizer, self.options)
+        return {'text': outputs}
+    
+    def postprocess(self, model_outputs):
+        return model_outputs
 
-            if verbose:
-                for segment in current_segments:
-                    start, end, text = segment["start"], segment["end"], segment["text"]
-                    line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
-                    print(make_safe(line))
+    def get_iterator(
+        self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params
+    ):
+        dataset = PipelineIterator(inputs, self.preprocess, preprocess_params)
+        if "TOKENIZERS_PARALLELISM" not in os.environ:
+            os.environ["TOKENIZERS_PARALLELISM"] = "false"
+        # TODO hack by collating feature_extractor and image_processor
 
-            # if a segment is instantaneous or does not contain text, clear it
-            for i, segment in enumerate(current_segments):
-                if segment["start"] == segment["end"] or segment["text"].strip() == "":
-                    segment["text"] = ""
-                    segment["tokens"] = []
-                    segment["words"] = []
+        def stack(items):
+            return {'inputs': torch.stack([x['inputs'] for x in items])}
+        dataloader = torch.utils.data.DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=stack)
+        model_iterator = PipelineIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size)
+        final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params)
+        return final_iterator
 
-            all_segments.extend(
-                [
-                    {"id": i, **segment}
-                    for i, segment in enumerate(
-                        current_segments, start=len(all_segments)
-                    )
-                ]
-            )
-            all_tokens.extend(
-                [token for segment in current_segments for token in segment["tokens"]]
-            )
+    def transcribe(
+        self, audio: Union[str, np.ndarray], batch_size=None
+    ):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        
+        def data(audio, segments):
+            for seg in segments:
+                f1 = int(seg['start'] * SAMPLE_RATE)
+                f2 = int(seg['end'] * SAMPLE_RATE)
+                # print(f2-f1)
+                yield {'inputs': audio[f1:f2]}
 
-            # update progress bar
-            pbar.update(min(content_frames, seek) - previous_seek)
+        vad_segments = self.vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
+        vad_segments = merge_chunks(vad_segments, 30)
 
+        del_tokenizer = False
+        if self.tokenizer is None:
+            language = self.detect_language(audio)
+            self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer, self.model.model.is_multilingual, task="transcribe", language=language)
+            del_tokenizer = True
+        else:
+            language = self.tokenizer.language_code
 
-    return dict(
-        text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
-        segments=all_segments,
-        language=language,
-    )
-
-
-def transcribe_with_vad(
-    model: "Whisper",
-    audio: str,
-    vad_pipeline,
-    mel = None,
-    verbose: Optional[bool] = None,
-    **kwargs
-):
-    """
-    Transcribe per VAD segment
-    """
-
-    vad_segments = vad_pipeline(audio)
-
-    # if not torch.is_tensor(audio):
-        # if isinstance(audio, str):
-    audio = load_audio(audio)
-    audio = torch.from_numpy(audio)
-
-    prev = 0
-    output = {"segments": []}
-
-    # merge segments to approx 30s inputs to make whisper most appropraite
-    vad_segments = merge_chunks(vad_segments, chunk_size=CHUNK_LENGTH)
-    if len(vad_segments) == 0:
-        return output
-
-    print(">>Performing transcription...")
-    for sdx, seg_t in enumerate(vad_segments):
-        if verbose:
-            print(f"~~ Transcribing VAD chunk: ({format_timestamp(seg_t['start'])} --> {format_timestamp(seg_t['end'])}) ~~")
-        seg_f_start, seg_f_end = int(seg_t["start"] * SAMPLE_RATE), int(seg_t["end"] * SAMPLE_RATE)
-        local_f_start, local_f_end = seg_f_start - prev, seg_f_end - prev
-        audio = audio[local_f_start:] # seek forward
-        seg_audio = audio[:local_f_end-local_f_start] # seek forward
-        prev = seg_f_start
-        local_mel = log_mel_spectrogram(seg_audio, padding=N_SAMPLES)
-        # need to pad
-
-        result = transcribe(model, audio, mel=local_mel, verbose=verbose, **kwargs)
-        seg_t["text"] = result["text"]
-        output["segments"].append(
-            {
-                "start": seg_t["start"],
-                "end": seg_t["end"],
-                "language": result["language"],
-                "text": result["text"],
-                "seg-text": [x["text"] for x in result["segments"]],
-                "seg-start": [x["start"] for x in result["segments"]],
-                "seg-end": [x["end"] for x in result["segments"]],
+        segments = []
+        batch_size = batch_size or self._batch_size
+        for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size)):
+            text = out['text']
+            if batch_size in [0, 1, None]:
+                text = text[0]
+            segments.append(
+                {
+                    "text": out['text'],
+                    "start": round(vad_segments[idx]['start'], 3),
+                    "end": round(vad_segments[idx]['end'], 3)
                 }
             )
+        
+        if del_tokenizer:
+            self.tokenizer = None
 
-    output["language"] = output["segments"][0]["language"]
+        return {"segments": segments, "language": language}
 
-    return output
+
+    def detect_language(self, audio: np.ndarray):
+        segment = log_mel_spectrogram(audio[: N_SAMPLES], padding=0)
+        encoder_output = self.model.encode(segment)
+        results = self.model.model.detect_language(encoder_output)
+        language_token, language_probability = results[0][0]
+        language = language_token[2:-2]
+        print(f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio...")
+        return language
+
+if __name__ == "__main__":
+    main_type = "simple"
+    import time
+
+    import jiwer
+    from tqdm import tqdm
+    from whisper.normalizers import EnglishTextNormalizer
+
+    from benchmark.tedlium import parse_tedlium_annos
+
+    if main_type == "complex":
+        from faster_whisper.tokenizer import Tokenizer
+        from faster_whisper.transcribe import TranscriptionOptions
+        from faster_whisper.vad import (SpeechTimestampsMap,
+                                        get_speech_timestamps)
+
+        from whisperx.vad import load_vad_model, merge_chunks
+
+        from .audio import SAMPLE_RATE, load_audio, log_mel_spectrogram
+        faster_t_options = TranscriptionOptions(
+        beam_size=5,
+        best_of=5,
+        patience=1,
+        length_penalty=1,
+        temperatures=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0],
+        compression_ratio_threshold=2.4,
+        log_prob_threshold=-1.0,
+        no_speech_threshold=0.6,
+        condition_on_previous_text=False,
+        initial_prompt=None,
+        prefix=None,
+        suppress_blank=True,
+        suppress_tokens=[-1],
+        without_timestamps=True,
+        max_initial_timestamp=0.0,
+        word_timestamps=False,
+        prepend_punctuations="\"'“¿([{-",
+        append_punctuations="\"'.。,，!！?？:：”)]}、"
+    )
+        whisper_arch = "large-v2"
+        device = "cuda"
+        batch_size = 16
+        model = WhisperModel(whisper_arch, device="cuda", compute_type="float16",)
+        tokenizer = Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task="transcribe", language="en")
+        model = FasterWhisperPipeline(model, tokenizer, faster_t_options, device=-1)
+        fn = "DanielKahneman_2010.wav"
+        wav_dir = f"/tmp/test/wav/"
+        vad_model = load_vad_model("cuda", 0.6, 0.3)
+        audio = load_audio(os.path.join(wav_dir, fn))
+        vad_segments = vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
+        vad_segments = merge_chunks(vad_segments, 30)
+
+        def data(audio, segments):
+            for seg in segments:
+                f1 = int(seg['start'] * SAMPLE_RATE)
+                f2 = int(seg['end'] * SAMPLE_RATE)
+                # print(f2-f1)
+                yield {'inputs': audio[f1:f2]}
+        vad_method="pyannote"
+
+        wav_dir = f"/tmp/test/wav/"
+        wer_li = []
+        time_li = []
+        for fn in os.listdir(wav_dir):
+            if fn == "RobertGupta_2010U.wav":
+                continue
+            base_fn = fn.split('.')[0]
+            audio_fp = os.path.join(wav_dir, fn)
+
+            audio = load_audio(audio_fp)
+            t1 = time.time()
+            if vad_method == "pyannote":
+                vad_segments = vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
+                vad_segments = merge_chunks(vad_segments, 30)
+            elif vad_method == "silero":
+                vad_segments = get_speech_timestamps(audio, threshold=0.5, max_speech_duration_s=30)
+                vad_segments = [{"start": x["start"] / SAMPLE_RATE, "end": x["end"] / SAMPLE_RATE} for x in vad_segments]
+                new_segs = []
+                curr_start = vad_segments[0]['start']
+                curr_end = vad_segments[0]['end']
+                for seg in vad_segments[1:]:
+                    if seg['end'] - curr_start > 30:
+                        new_segs.append({"start": curr_start, "end": curr_end})
+                        curr_start = seg['start']
+                        curr_end = seg['end']
+                    else:
+                        curr_end = seg['end']
+                new_segs.append({"start": curr_start, "end": curr_end})
+                vad_segments = new_segs
+            text = []
+            # for idx, out in tqdm(enumerate(model(data(audio_fp, vad_segments), batch_size=batch_size)), total=len(vad_segments)):
+            for idx, out in enumerate(model(data(audio, vad_segments), batch_size=batch_size)):
+                text.append(out['text'])
+            t2 = time.time()
+            if batch_size == 1:
+                text = [x[0] for x in text]
+            text = " ".join(text)
+
+            normalizer = EnglishTextNormalizer()
+            text = normalizer(text)
+            gt_corpus = normalizer(parse_tedlium_annos(base_fn, "/tmp/test/"))
+
+            wer_result = jiwer.wer(gt_corpus, text)
+            print("WER: %.2f \t time: %.2f \t [%s]" % (wer_result * 100, t2-t1, fn))
+
+            wer_li.append(wer_result)
+            time_li.append(t2-t1)
+        print("# Avg Mean...")
+        print("WER: %.2f" % (sum(wer_li) * 100/len(wer_li)))
+        print("Time: %.2f" % (sum(time_li)/len(time_li)))
+    elif main_type == "simple":
+        model = load_model(
+            "large-v2",
+            device="cuda",
+            language="en",
+        )
+
+        wav_dir = f"/tmp/test/wav/"
+        wer_li = []
+        time_li = []
+        for fn in os.listdir(wav_dir):
+            if fn == "RobertGupta_2010U.wav":
+                continue
+            # fn = "DanielKahneman_2010.wav"
+            base_fn = fn.split('.')[0]
+            audio_fp = os.path.join(wav_dir, fn)
+
+            audio = load_audio(audio_fp)
+            t1 = time.time()
+            out = model.transcribe(audio_fp, batch_size=8)["segments"]
+            t2 = time.time()
+
+            text = " ".join([x['text'] for x in out])
+            normalizer = EnglishTextNormalizer()
+            text = normalizer(text)
+            gt_corpus = normalizer(parse_tedlium_annos(base_fn, "/tmp/test/"))
+
+            wer_result = jiwer.wer(gt_corpus, text)
+            print("WER: %.2f \t time: %.2f \t [%s]" % (wer_result * 100, t2-t1, fn))
+
+            wer_li.append(wer_result)
+            time_li.append(t2-t1)
+        print("# Avg Mean...")
+        print("WER: %.2f" % (sum(wer_li) * 100/len(wer_li)))
+        print("Time: %.2f" % (sum(time_li)/len(time_li)))
diff --git a/whisperx/assets/mel_filters.npz b/whisperx/assets/mel_filters.npz
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diff --git a/whisperx/audio.py b/whisperx/audio.py
new file mode 100644
index 0000000..513ab7c
--- /dev/null
+++ b/whisperx/audio.py
@@ -0,0 +1,147 @@
+import os
+from functools import lru_cache
+from typing import Optional, Union
+
+import ffmpeg
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from .utils import exact_div
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)  # 3000 frames in a mel spectrogram input
+
+N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2  # the initial convolutions has stride 2
+FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH)  # 10ms per audio frame
+TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audio token
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+
+    sr: int
+        The sample rate to resample the audio if necessary
+
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+    try:
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if torch.is_tensor(array):
+        if array.shape[axis] > length:
+            array = array.index_select(
+                dim=axis, index=torch.arange(length, device=array.device)
+            )
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+    else:
+        if array.shape[axis] > length:
+            array = array.take(indices=range(length), axis=axis)
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = np.pad(array, pad_widths)
+
+    return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+    """
+    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+    Allows decoupling librosa dependency; saved using:
+
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+        )
+    """
+    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+    with np.load(
+        os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
+    ) as f:
+        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
+def log_mel_spectrogram(
+    audio: Union[str, np.ndarray, torch.Tensor],
+    n_mels: int = N_MELS,
+    padding: int = 0,
+    device: Optional[Union[str, torch.device]] = None,
+):
+    """
+    Compute the log-Mel spectrogram of
+
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+
+    n_mels: int
+        The number of Mel-frequency filters, only 80 is supported
+
+    padding: int
+        Number of zero samples to pad to the right
+
+    device: Optional[Union[str, torch.device]]
+        If given, the audio tensor is moved to this device before STFT
+
+    Returns
+    -------
+    torch.Tensor, shape = (80, n_frames)
+        A Tensor that contains the Mel spectrogram
+    """
+    if not torch.is_tensor(audio):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio = torch.from_numpy(audio)
+
+    if device is not None:
+        audio = audio.to(device)
+    if padding > 0:
+        audio = F.pad(audio, (0, padding))
+    window = torch.hann_window(N_FFT).to(audio.device)
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+    magnitudes = stft[..., :-1].abs() ** 2
+
+    filters = mel_filters(audio.device, n_mels)
+    mel_spec = filters @ magnitudes
+
+    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    return log_spec
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index ed918e0..c3719ba 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -1,37 +1,30 @@
 import argparse
-import os
 import gc
+import os
 import warnings
-from typing import TYPE_CHECKING, Optional, Tuple, Union
+
 import numpy as np
 import torch
-import tempfile 
-import ffmpeg
-from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE
-from whisper.audio import SAMPLE_RATE
-from whisper.utils import (
-    optional_float,
-    optional_int,
-    str2bool,
-)
 
-from .alignment import load_align_model, align
-from .asr import transcribe, transcribe_with_vad
+from .alignment import align, load_align_model
+from .asr import load_model
+from .audio import load_audio
 from .diarize import DiarizationPipeline, assign_word_speakers
-from .utils import get_writer
-from .vad import load_vad_model
+from .utils import (LANGUAGES, TO_LANGUAGE_CODE, get_writer, optional_float,
+                    optional_int, str2bool)
+
 
 def cli():
-    from whisper import available_models
-
     # fmt: off
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
-    parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
+    parser.add_argument("--model", default="small", help="name of the Whisper model to use")
     parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
     parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
+    parser.add_argument("--batch_size", default=8, type=int, help="device to use for PyTorch inference")
+
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
-    parser.add_argument("--output_format", "-f", type=str, default="all", choices=["all", "srt", "srt-word", "vtt", "txt", "tsv", "ass", "ass-char", "pickle", "vad"], help="format of the output file; if not specified, all available formats will be produced")
+    parser.add_argument("--output_format", "-f", type=str, default="all", choices=["all", "srt", "vtt", "txt", "tsv", "json"], help="format of the output file; if not specified, all available formats will be produced")
     parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
 
     parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
@@ -39,13 +32,10 @@ def cli():
 
     # alignment params
     parser.add_argument("--align_model", default=None, help="Name of phoneme-level ASR model to do alignment")
-    parser.add_argument("--align_extend", default=2, type=float, help="Seconds before and after to extend the whisper segments for alignment (if not using VAD).")
-    parser.add_argument("--align_from_prev", default=True, type=bool, help="Whether to clip the alignment start time of current segment to the end time of the last aligned word of the previous segment (if not using VAD)")
     parser.add_argument("--interpolate_method", default="nearest", choices=["nearest", "linear", "ignore"], help="For word .srt, method to assign timestamps to non-aligned words, or merge them into neighbouring.")
     parser.add_argument("--no_align", action='store_true', help="Do not perform phoneme alignment")
 
     # vad params
-    parser.add_argument("--vad_filter", type=str2bool, default=True, help="Whether to pre-segment audio with VAD, highly recommended! Produces more accurate alignment + timestamp see WhisperX paper https://arxiv.org/abs/2303.00747")
     parser.add_argument("--vad_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected")
     parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected.")
 
@@ -69,9 +59,14 @@ def cli():
     parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
     parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
     parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
-    parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
-    parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
-    parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
+
+    parser.add_argument("--max_line_width", type=optional_int, default=None, help="(not possible with --no_align) the maximum number of characters in a line before breaking the line")
+    parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --no_align) the maximum number of lines in a segment")
+    parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
+
+    # parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
+    # parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
+    # parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
 
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
@@ -81,7 +76,7 @@ def cli():
 
     args = parser.parse_args().__dict__
     model_name: str = args.pop("model")
-    model_dir: str = args.pop("model_dir")
+    batch_size: int = args.pop("batch_size")
     output_dir: str = args.pop("output_dir")
     output_format: str = args.pop("output_format")
     device: str = args.pop("device")
@@ -93,13 +88,10 @@ def cli():
         os.makedirs(tmp_dir, exist_ok=True)
 
     align_model: str = args.pop("align_model")
-    align_extend: float = args.pop("align_extend")
-    align_from_prev: bool = args.pop("align_from_prev")
     interpolate_method: str = args.pop("interpolate_method")
     no_align: bool = args.pop("no_align")
 
     hf_token: str = args.pop("hf_token")
-    vad_filter: bool = args.pop("vad_filter")
     vad_onset: float = args.pop("vad_onset")
     vad_offset: float = args.pop("vad_offset")
 
@@ -107,18 +99,7 @@ def cli():
     min_speakers: int = args.pop("min_speakers")
     max_speakers: int = args.pop("max_speakers")
 
-    if vad_filter:
-        from pyannote.audio import Pipeline
-        from pyannote.audio import Model, Pipeline
-        vad_model = load_vad_model(torch.device(device), vad_onset, vad_offset, use_auth_token=hf_token)
-    else:
-        vad_model = None
-
-    # if model_flush:
-    #     print(">>Model flushing activated... Only loading model after ASR stage")
-    #     del align_model
-    #     align_model = ""
-
+    # TODO: check model loading works.
 
     if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
         if args["language"] is not None:
@@ -136,39 +117,43 @@ def cli():
     if (threads := args.pop("threads")) > 0:
         torch.set_num_threads(threads)
 
-    from whisper import load_model
+    asr_options = {
+        "beam_size": args.pop("beam_size"),
+        "patience": args.pop("patience"),
+        "length_penalty": args.pop("length_penalty"),
+        "temperatures": temperature,
+        "compression_ratio_threshold": args.pop("compression_ratio_threshold"),
+        "log_prob_threshold": args.pop("logprob_threshold"),
+        "no_speech_threshold": args.pop("no_speech_threshold"),
+        "condition_on_previous_text": False,
+        "initial_prompt": args.pop("initial_prompt"),
+    }
 
     writer = get_writer(output_format, output_dir)
-
+    word_options = ["highlight_words", "max_line_count", "max_line_width"]
+    if no_align:
+        for option in word_options:
+            if args[option]:
+                parser.error(f"--{option} requires --word_timestamps True")
+    if args["max_line_count"] and not args["max_line_width"]:
+        warnings.warn("--max_line_count has no effect without --max_line_width")
+    writer_args = {arg: args.pop(arg) for arg in word_options}
+    
     # Part 1: VAD & ASR Loop
     results = []
     tmp_results = []
-    model = load_model(model_name, device=device, download_root=model_dir)
-    for audio_path in args.pop("audio"):
-        input_audio_path = audio_path
-        tfile = None
+    # model = load_model(model_name, device=device, download_root=model_dir)
+    model = load_model(model_name, device=device, language=args['language'], asr_options=asr_options, vad_options={"vad_onset": vad_onset, "vad_offset": vad_offset},)
 
+    for audio_path in args.pop("audio"):
+        audio = load_audio(audio_path)
         # >> VAD & ASR
-        if vad_model is not None:
-            if not audio_path.endswith(".wav"):
-                print(">>VAD requires .wav format, converting to wav as a tempfile...")
-                audio_basename = os.path.splitext(os.path.basename(audio_path))[0]
-                if tmp_dir is not None:
-                    input_audio_path = os.path.join(tmp_dir, audio_basename + ".wav")
-                else:
-                    input_audio_path = os.path.join(os.path.dirname(audio_path), audio_basename + ".wav")
-                ffmpeg.input(audio_path, threads=0).output(input_audio_path, ac=1, ar=SAMPLE_RATE).run(cmd=["ffmpeg"])
-            print(">>Performing VAD...")
-            result = transcribe_with_vad(model, input_audio_path, vad_model, temperature=temperature, **args)
-        else:
-            print(">>Performing transcription...")
-            result = transcribe(model, input_audio_path, temperature=temperature, **args)
-        
-        results.append((result, input_audio_path))
+        print(">>Performing transcription...")
+        result = model.transcribe(audio, batch_size=batch_size)
+        results.append((result, audio_path))
 
     # Unload Whisper and VAD
     del model
-    del vad_model
     gc.collect()
     torch.cuda.empty_cache()
 
@@ -178,17 +163,22 @@ def cli():
         results = []
         align_language = args["language"] if args["language"] is not None else "en" # default to loading english if not specified
         align_model, align_metadata = load_align_model(align_language, device, model_name=align_model)
-        for result, input_audio_path in tmp_results:
+        for result, audio_path in tmp_results:
             # >> Align
+            if len(tmp_results) > 1:
+                input_audio = audio_path
+            else:
+                # lazily load audio from part 1
+                input_audio = audio
+
             if align_model is not None and len(result["segments"]) > 0:
                 if result.get("language", "en") != align_metadata["language"]:
                     # load new language
                     print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
                     align_model, align_metadata = load_align_model(result["language"], device)
                 print(">>Performing alignment...")
-                result = align(result["segments"], align_model, align_metadata, input_audio_path, device,
-                    extend_duration=align_extend, start_from_previous=align_from_prev, interpolate_method=interpolate_method)
-            results.append((result, input_audio_path))
+                result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method)
+            results.append((result, audio_path))
 
         # Unload align model
         del align_model
@@ -210,11 +200,7 @@ def cli():
 
     # >> Write
     for result, audio_path in results:
-        writer(result, audio_path)
-
-        # cleanup
-        if input_audio_path != audio_path:
-            os.remove(input_audio_path)
+        writer(result, audio_path, writer_args)
 
 if __name__ == "__main__":
     cli()
\ No newline at end of file
diff --git a/whisperx/utils.py b/whisperx/utils.py
index 14e298b..3401a84 100644
--- a/whisperx/utils.py
+++ b/whisperx/utils.py
@@ -1,280 +1,301 @@
+import json
 import os
+import re
+import sys
 import zlib
-from typing import Callable, TextIO, Iterator, Tuple
-import pandas as pd
-import numpy as np
+from typing import Callable, Optional, TextIO
 
-def interpolate_nans(x, method='nearest'):
-    if x.notnull().sum() > 1:
-        return x.interpolate(method=method).ffill().bfill()
+LANGUAGES = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "he": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+    **{language: code for code, language in LANGUAGES.items()},
+    "burmese": "my",
+    "valencian": "ca",
+    "flemish": "nl",
+    "haitian": "ht",
+    "letzeburgesch": "lb",
+    "pushto": "ps",
+    "panjabi": "pa",
+    "moldavian": "ro",
+    "moldovan": "ro",
+    "sinhalese": "si",
+    "castilian": "es",
+}
+
+
+system_encoding = sys.getdefaultencoding()
+
+if system_encoding != "utf-8":
+
+    def make_safe(string):
+        # replaces any character not representable using the system default encoding with an '?',
+        # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729).
+        return string.encode(system_encoding, errors="replace").decode(system_encoding)
+
+else:
+
+    def make_safe(string):
+        # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding
+        return string
+
+
+def exact_div(x, y):
+    assert x % y == 0
+    return x // y
+
+
+def str2bool(string):
+    str2val = {"True": True, "False": False}
+    if string in str2val:
+        return str2val[string]
     else:
-        return x.ffill().bfill()
-    
-
-def write_txt(transcript: Iterator[dict], file: TextIO):
-    for segment in transcript:
-        print(segment['text'].strip(), file=file, flush=True)
+        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
 
 
-def write_vtt(transcript: Iterator[dict], file: TextIO):
-    print("WEBVTT\n", file=file)
-    for segment in transcript:
-        print(
-            f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-            f"{segment['text'].strip().replace('-->', '->')}\n",
-            file=file,
-            flush=True,
+def optional_int(string):
+    return None if string == "None" else int(string)
+
+
+def optional_float(string):
+    return None if string == "None" else float(string)
+
+
+def compression_ratio(text) -> float:
+    text_bytes = text.encode("utf-8")
+    return len(text_bytes) / len(zlib.compress(text_bytes))
+
+
+def format_timestamp(
+    seconds: float, always_include_hours: bool = False, decimal_marker: str = "."
+):
+    assert seconds >= 0, "non-negative timestamp expected"
+    milliseconds = round(seconds * 1000.0)
+
+    hours = milliseconds // 3_600_000
+    milliseconds -= hours * 3_600_000
+
+    minutes = milliseconds // 60_000
+    milliseconds -= minutes * 60_000
+
+    seconds = milliseconds // 1_000
+    milliseconds -= seconds * 1_000
+
+    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
+    return (
+        f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+    )
+
+
+class ResultWriter:
+    extension: str
+
+    def __init__(self, output_dir: str):
+        self.output_dir = output_dir
+
+    def __call__(self, result: dict, audio_path: str, options: dict):
+        audio_basename = os.path.basename(audio_path)
+        audio_basename = os.path.splitext(audio_basename)[0]
+        output_path = os.path.join(
+            self.output_dir, audio_basename + "." + self.extension
         )
 
-def write_tsv(transcript: Iterator[dict], file: TextIO):
-    print("start", "end", "text", sep="\t", file=file)
-    for segment in transcript:
-        print(segment['start'], file=file, end="\t")
-        print(segment['end'], file=file, end="\t")
-        print(segment['text'].strip().replace("\t", " "), file=file, flush=True)
+        with open(output_path, "w", encoding="utf-8") as f:
+            self.write_result(result, file=f, options=options)
+
+    def write_result(self, result: dict, file: TextIO, options: dict):
+        raise NotImplementedError
 
 
-def write_srt(transcript: Iterator[dict], file: TextIO):
-    """
-    Write a transcript to a file in SRT format.
+class WriteTXT(ResultWriter):
+    extension: str = "txt"
 
-    Example usage:
-        from pathlib import Path
-        from whisper.utils import write_srt
-
-        result = transcribe(model, audio_path, temperature=temperature, **args)
-
-        # save SRT
-        audio_basename = Path(audio_path).stem
-        with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
-            write_srt(result["segments"], file=srt)
-    """
-    for i, segment in enumerate(transcript, start=1):
-        # write srt lines
-        print(
-            f"{i}\n"
-            f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
-            f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
-            f"{segment['text'].strip().replace('-->', '->')}\n",
-            file=file,
-            flush=True,
-        )
+    def write_result(self, result: dict, file: TextIO, options: dict):
+        for segment in result["segments"]:
+            print(segment["text"].strip(), file=file, flush=True)
 
 
-def write_ass(transcript: Iterator[dict],
-            file: TextIO,
-            resolution: str = "word",
-            color: str = None, underline=True,
-            prefmt: str = None, suffmt: str = None,
-            font: str = None, font_size: int = 24,
-            strip=True, **kwargs):
-    """
-    Credit: https://github.com/jianfch/stable-ts/blob/ff79549bd01f764427879f07ecd626c46a9a430a/stable_whisper/text_output.py
-        Generate Advanced SubStation Alpha (ass) file from results to
-    display both phrase-level & word-level timestamp simultaneously by:
-     -using segment-level timestamps display phrases as usual
-     -using word-level timestamps change formats (e.g. color/underline) of the word in the displayed segment
-    Note: ass file is used in the same way as srt, vtt, etc.
-    Parameters
-    ----------
-    transcript: dict
-        results from modified model
-    file: TextIO
-        file object to write to
-    resolution: str
-        "word" or "char", timestamp resolution to highlight.
-    color: str
-        color code for a word at its corresponding timestamp
-        <bbggrr> reverse order hexadecimal RGB value (e.g. FF0000 is full intensity blue. Default: 00FF00)
-    underline: bool
-        whether to underline a word at its corresponding timestamp
-    prefmt: str
-        used to specify format for word-level timestamps (must be use with 'suffmt' and overrides 'color'&'underline')
-        appears as such in the .ass file:
-            Hi, {<prefmt>}how{<suffmt>} are you?
-        reference [Appendix A: Style override codes] in http://www.tcax.org/docs/ass-specs.htm
-    suffmt: str
-        used to specify format for word-level timestamps (must be use with 'prefmt' and overrides 'color'&'underline')
-        appears as such in the .ass file:
-            Hi, {<prefmt>}how{<suffmt>} are you?
-        reference [Appendix A: Style override codes] in http://www.tcax.org/docs/ass-specs.htm
-    font: str
-        word font (default: Arial)
-    font_size: int
-        word font size (default: 48)
-    kwargs:
-        used for format styles:
-        'Name', 'Fontname', 'Fontsize', 'PrimaryColour', 'SecondaryColour', 'OutlineColour', 'BackColour', 'Bold',
-        'Italic', 'Underline', 'StrikeOut', 'ScaleX', 'ScaleY', 'Spacing', 'Angle', 'BorderStyle', 'Outline',
-        'Shadow', 'Alignment', 'MarginL', 'MarginR', 'MarginV', 'Encoding'
+class SubtitlesWriter(ResultWriter):
+    always_include_hours: bool
+    decimal_marker: str
 
-    """
+    def iterate_result(self, result: dict, options: dict):
+        raw_max_line_width: Optional[int] = options["max_line_width"]
+        max_line_count: Optional[int] = options["max_line_count"]
+        highlight_words: bool = options["highlight_words"]
+        max_line_width = 1000 if raw_max_line_width is None else raw_max_line_width
+        preserve_segments = max_line_count is None or raw_max_line_width is None
 
-    fmt_style_dict = {'Name': 'Default', 'Fontname': 'Arial', 'Fontsize': '48', 'PrimaryColour': '&Hffffff',
-                    'SecondaryColour': '&Hffffff', 'OutlineColour': '&H0', 'BackColour': '&H0', 'Bold': '0',
-                    'Italic': '0', 'Underline': '0', 'StrikeOut': '0', 'ScaleX': '100', 'ScaleY': '100',
-                    'Spacing': '0', 'Angle': '0', 'BorderStyle': '1', 'Outline': '1', 'Shadow': '0',
-                    'Alignment': '2', 'MarginL': '10', 'MarginR': '10', 'MarginV': '10', 'Encoding': '0'}
+        def iterate_subtitles():
+            line_len = 0
+            line_count = 1
+            # the next subtitle to yield (a list of word timings with whitespace)
+            subtitle: list[dict] = []
+            last = result["segments"][0]["words"][0]["start"]
+            for segment in result["segments"]:
+                for i, original_timing in enumerate(segment["words"]):
+                    timing = original_timing.copy()
+                    long_pause = not preserve_segments and timing["start"] - last > 3.0
+                    has_room = line_len + len(timing["word"]) <= max_line_width
+                    seg_break = i == 0 and len(subtitle) > 0 and preserve_segments
+                    if line_len > 0 and has_room and not long_pause and not seg_break:
+                        # line continuation
+                        line_len += len(timing["word"])
+                    else:
+                        # new line
+                        timing["word"] = timing["word"].strip()
+                        if (
+                            len(subtitle) > 0
+                            and max_line_count is not None
+                            and (long_pause or line_count >= max_line_count)
+                            or seg_break
+                        ):
+                            # subtitle break
+                            yield subtitle
+                            subtitle = []
+                            line_count = 1
+                        elif line_len > 0:
+                            # line break
+                            line_count += 1
+                            timing["word"] = "\n" + timing["word"]
+                        line_len = len(timing["word"].strip())
+                    subtitle.append(timing)
+                    last = timing["start"]
+            if len(subtitle) > 0:
+                yield subtitle
 
-    for k, v in filter(lambda x: 'colour' in x[0].lower() and not str(x[1]).startswith('&H'), kwargs.items()):
-        kwargs[k] = f'&H{kwargs[k]}'
+        if "words" in result["segments"][0]:
+            for subtitle in iterate_subtitles():
+                subtitle_start = self.format_timestamp(subtitle[0]["start"])
+                subtitle_end = self.format_timestamp(subtitle[-1]["end"])
+                subtitle_text = "".join([word["word"] for word in subtitle])
+                if highlight_words:
+                    last = subtitle_start
+                    all_words = [timing["word"] for timing in subtitle]
+                    for i, this_word in enumerate(subtitle):
+                        start = self.format_timestamp(this_word["start"])
+                        end = self.format_timestamp(this_word["end"])
+                        if last != start:
+                            yield last, start, subtitle_text
 
-    fmt_style_dict.update((k, v) for k, v in kwargs.items() if k in fmt_style_dict)
-
-    if font:
-        fmt_style_dict.update(Fontname=font)
-    if font_size:
-        fmt_style_dict.update(Fontsize=font_size)
-
-    fmts = f'Format: {", ".join(map(str, fmt_style_dict.keys()))}'
-
-    styles = f'Style: {",".join(map(str, fmt_style_dict.values()))}'
-
-    ass_str = f'[Script Info]\nScriptType: v4.00+\nPlayResX: 384\nPlayResY: 288\nScaledBorderAndShadow: yes\n\n' \
-            f'[V4+ Styles]\n{fmts}\n{styles}\n\n' \
-            f'[Events]\nFormat: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text\n\n'
-
-    if prefmt or suffmt:
-        if suffmt:
-            assert prefmt, 'prefmt must be used along with suffmt'
+                        yield start, end, "".join(
+                            [
+                                re.sub(r"^(\s*)(.*)$", r"\1<u>\2</u>", word)
+                                if j == i
+                                else word
+                                for j, word in enumerate(all_words)
+                            ]
+                        )
+                        last = end
+                else:
+                    yield subtitle_start, subtitle_end, subtitle_text
         else:
-            suffmt = r'\r'
-    else:
-        if not color:
-            color = 'HFF00'
-        underline_code = r'\u1' if underline else ''
-
-        prefmt = r'{\1c&' + f'{color.upper()}&{underline_code}' + '}'
-        suffmt = r'{\r}'
-    
-    def secs_to_hhmmss(secs: Tuple[float, int]):
-        mm, ss = divmod(secs, 60)
-        hh, mm = divmod(mm, 60)
-        return f'{hh:0>1.0f}:{mm:0>2.0f}:{ss:0>2.2f}'
-
-
-    def dialogue(chars: str, start: float, end: float, idx_0: int, idx_1: int) -> str:
-        if idx_0 == -1:
-            text = chars
-        else:
-            text = f'{chars[:idx_0]}{prefmt}{chars[idx_0:idx_1]}{suffmt}{chars[idx_1:]}'
-        return f"Dialogue: 0,{secs_to_hhmmss(start)},{secs_to_hhmmss(end)}," \
-               f"Default,,0,0,0,,{text.strip() if strip else text}"
-
-    if resolution == "word":
-        resolution_key = "word-segments"
-    elif resolution == "char":
-        resolution_key = "char-segments"
-    else:
-        raise ValueError(".ass resolution should be 'word' or 'char', not ", resolution)
-    
-    ass_arr = []
-
-    for segment in transcript:
-        # if "12" in segment['text']:
-            # import pdb; pdb.set_trace()
-        if resolution_key in segment:
-            res_segs = pd.DataFrame(segment[resolution_key])
-            prev = segment['start']
-            if "speaker" in segment:
-                speaker_str = f"[{segment['speaker']}]: "
-            else:
-                speaker_str = ""
-            for cdx, crow in res_segs.iterrows():
-                if not np.isnan(crow['start']):
-                    if resolution == "char":
-                        idx_0 = cdx
-                        idx_1 = cdx + 1
-                    elif resolution == "word":
-                        idx_0 = int(crow["segment-text-start"])
-                        idx_1 = int(crow["segment-text-end"])
-                    # fill gap
-                    if crow['start'] > prev:
-                        filler_ts = {
-                            "chars": speaker_str + segment['text'],
-                            "start": prev,
-                            "end": crow['start'],
-                            "idx_0": -1,
-                            "idx_1": -1
-                        }
-
-                        ass_arr.append(filler_ts)
-                    # highlight current word
-                    f_word_ts = {
-                        "chars": speaker_str + segment['text'],
-                        "start": crow['start'],
-                        "end": crow['end'],
-                        "idx_0": idx_0 + len(speaker_str),
-                        "idx_1": idx_1 + len(speaker_str)
-                    }
-                    ass_arr.append(f_word_ts)
-                    prev = crow['end']
-
-    ass_str += '\n'.join(map(lambda x: dialogue(**x), ass_arr))
-
-    file.write(ass_str)
-
-
-from whisper.utils import SubtitlesWriter, ResultWriter, WriteTXT, WriteVTT, WriteSRT, WriteTSV, WriteJSON, format_timestamp
-
-class WriteASS(ResultWriter):
-    extension: str = "ass"
-
-    def write_result(self, result: dict, file: TextIO):
-        write_ass(result["segments"], file, resolution="word")
-
-class WriteASSchar(ResultWriter):
-    extension: str = "ass"
-
-    def write_result(self, result: dict, file: TextIO):
-        write_ass(result["segments"], file, resolution="char")
-
-class WritePickle(ResultWriter):
-    extension: str = "ass"
-
-    def write_result(self, result: dict, file: TextIO):
-        pd.DataFrame(result["segments"]).to_pickle(file)
-
-class WriteSRTWord(ResultWriter):
-    extension: str = "word.srt"
-    always_include_hours: bool = True
-    decimal_marker: str = ","
-
-    def iterate_result(self, result: dict):
-        for segment in result["word_segments"]:
-            segment_start = self.format_timestamp(segment["start"])
-            segment_end = self.format_timestamp(segment["end"])
-            segment_text = segment["text"].strip().replace("-->", "->")
-
-            if word_timings := segment.get("words", None):
-                all_words = [timing["word"] for timing in word_timings]
-                all_words[0] = all_words[0].strip()  # remove the leading space, if any
-                last = segment_start
-                for i, this_word in enumerate(word_timings):
-                    start = self.format_timestamp(this_word["start"])
-                    end = self.format_timestamp(this_word["end"])
-                    if last != start:
-                        yield last, start, segment_text
-
-                    yield start, end, "".join(
-                        [
-                            f"<u>{word}</u>" if j == i else word
-                            for j, word in enumerate(all_words)
-                        ]
-                    )
-                    last = end
-
-                if last != segment_end:
-                    yield last, segment_end, segment_text
-            else:
+            for segment in result["segments"]:
+                segment_start = self.format_timestamp(segment["start"])
+                segment_end = self.format_timestamp(segment["end"])
+                segment_text = segment["text"].strip().replace("-->", "->")
                 yield segment_start, segment_end, segment_text
 
-    def write_result(self, result: dict, file: TextIO):
-        if "word_segments" not in result:
-            return
-        for i, (start, end, text) in enumerate(self.iterate_result(result), start=1):
-            print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True)
-
     def format_timestamp(self, seconds: float):
         return format_timestamp(
             seconds=seconds,
@@ -282,36 +303,81 @@ class WriteSRTWord(ResultWriter):
             decimal_marker=self.decimal_marker,
         )
 
-def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]:
+
+class WriteVTT(SubtitlesWriter):
+    extension: str = "vtt"
+    always_include_hours: bool = False
+    decimal_marker: str = "."
+
+    def write_result(self, result: dict, file: TextIO, options: dict):
+        print("WEBVTT\n", file=file)
+        for start, end, text in self.iterate_result(result, options):
+            print(f"{start} --> {end}\n{text}\n", file=file, flush=True)
+
+
+class WriteSRT(SubtitlesWriter):
+    extension: str = "srt"
+    always_include_hours: bool = True
+    decimal_marker: str = ","
+
+    def write_result(self, result: dict, file: TextIO, options: dict):
+        for i, (start, end, text) in enumerate(
+            self.iterate_result(result, options), start=1
+        ):
+            print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True)
+
+
+class WriteTSV(ResultWriter):
+    """
+    Write a transcript to a file in TSV (tab-separated values) format containing lines like:
+    <start time in integer milliseconds>\t<end time in integer milliseconds>\t<transcript text>
+
+    Using integer milliseconds as start and end times means there's no chance of interference from
+    an environment setting a language encoding that causes the decimal in a floating point number
+    to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++.
+    """
+
+    extension: str = "tsv"
+
+    def write_result(self, result: dict, file: TextIO, options: dict):
+        print("start", "end", "text", sep="\t", file=file)
+        for segment in result["segments"]:
+            print(round(1000 * segment["start"]), file=file, end="\t")
+            print(round(1000 * segment["end"]), file=file, end="\t")
+            print(segment["text"].strip().replace("\t", " "), file=file, flush=True)
+
+
+class WriteJSON(ResultWriter):
+    extension: str = "json"
+
+    def write_result(self, result: dict, file: TextIO, options: dict):
+        json.dump(result, file)
+
+
+def get_writer(
+    output_format: str, output_dir: str
+) -> Callable[[dict, TextIO, dict], None]:
     writers = {
         "txt": WriteTXT,
         "vtt": WriteVTT,
         "srt": WriteSRT,
         "tsv": WriteTSV,
-        "ass": WriteASS,
-        "srt-word": WriteSRTWord,
-        # "ass-char": WriteASSchar,
-        # "pickle": WritePickle,
-        # "json": WriteJSON,
-    }
-
-    writers_other = {
-        "pkl": WritePickle,
-        "ass-char": WriteASSchar
+        "json": WriteJSON,
     }
 
     if output_format == "all":
         all_writers = [writer(output_dir) for writer in writers.values()]
 
-        def write_all(result: dict, file: TextIO):
+        def write_all(result: dict, file: TextIO, options: dict):
             for writer in all_writers:
-                writer(result, file)
+                writer(result, file, options)
 
         return write_all
 
-    if output_format in writers:
-        return writers[output_format](output_dir)
-    elif output_format in writers_other:
-        return writers_other[output_format](output_dir)
+    return writers[output_format](output_dir)
+
+def interpolate_nans(x, method='nearest'):
+    if x.notnull().sum() > 1:
+        return x.interpolate(method=method).ffill().bfill()
     else:
-        raise ValueError(f"Output format '{output_format}' not supported, choose from {writers.keys()} and {writers_other.keys()}")
+        return x.ffill().bfill()
\ No newline at end of file
diff --git a/whisperx/vad.py b/whisperx/vad.py
index 933d270..42b0bfb 100644
--- a/whisperx/vad.py
+++ b/whisperx/vad.py
@@ -1,22 +1,23 @@
+import hashlib
 import os
 import urllib
-import pandas as pd
+from typing import Callable, Optional, Text, Union
+
 import numpy as np
+import pandas as pd
 import torch
-import hashlib
-from tqdm import tqdm
-from typing import Optional, Callable, Union, Text
-from pyannote.audio.core.io import AudioFile
-from pyannote.core import Annotation, Segment, SlidingWindowFeature
-from pyannote.audio.pipelines.utils import PipelineModel
 from pyannote.audio import Model
+from pyannote.audio.core.io import AudioFile
 from pyannote.audio.pipelines import VoiceActivityDetection
+from pyannote.audio.pipelines.utils import PipelineModel
+from pyannote.core import Annotation, Segment, SlidingWindowFeature
+from tqdm import tqdm
+
 from .diarize import Segment as SegmentX
-from typing import List, Tuple, Optional
 
 VAD_SEGMENTATION_URL = "https://whisperx.s3.eu-west-2.amazonaws.com/model_weights/segmentation/0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea/pytorch_model.bin"
 
-def load_vad_model(device, vad_onset, vad_offset, use_auth_token=None, model_fp=None):
+def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, use_auth_token=None, model_fp=None):
     model_dir = torch.hub._get_torch_home()
     os.makedirs(model_dir, exist_ok = True)
     if model_fp is None:

From 2a29f0ec6a8df93152949053db2fe471ea6e16cc Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Mon, 24 Apr 2023 21:24:22 +0100
Subject: [PATCH 22/48] add compute types

---
 whisperx/transcribe.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index c3719ba..0c6b803 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -22,6 +22,8 @@ def cli():
     parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
     parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
     parser.add_argument("--batch_size", default=8, type=int, help="device to use for PyTorch inference")
+    parser.add_argument("--compute_type", default="float16", type=str, choices=["float16", "float32", "int8"], help="compute type for computation")
+
 
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
     parser.add_argument("--output_format", "-f", type=str, default="all", choices=["all", "srt", "vtt", "txt", "tsv", "json"], help="format of the output file; if not specified, all available formats will be produced")

From 0efad26066f2db7be1f23ebbcad990210027e0a2 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Mon, 24 Apr 2023 21:26:44 +0100
Subject: [PATCH 23/48] pass compute_type

---
 whisperx/transcribe.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 0c6b803..fd6cf52 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -24,7 +24,6 @@ def cli():
     parser.add_argument("--batch_size", default=8, type=int, help="device to use for PyTorch inference")
     parser.add_argument("--compute_type", default="float16", type=str, choices=["float16", "float32", "int8"], help="compute type for computation")
 
-
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
     parser.add_argument("--output_format", "-f", type=str, default="all", choices=["all", "srt", "vtt", "txt", "tsv", "json"], help="format of the output file; if not specified, all available formats will be produced")
     parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
@@ -82,6 +81,8 @@ def cli():
     output_dir: str = args.pop("output_dir")
     output_format: str = args.pop("output_format")
     device: str = args.pop("device")
+    compute_type: str = args.pop("compute_type")
+
     # model_flush: bool = args.pop("model_flush")
     os.makedirs(output_dir, exist_ok=True)
 
@@ -145,7 +146,7 @@ def cli():
     results = []
     tmp_results = []
     # model = load_model(model_name, device=device, download_root=model_dir)
-    model = load_model(model_name, device=device, language=args['language'], asr_options=asr_options, vad_options={"vad_onset": vad_onset, "vad_offset": vad_offset},)
+    model = load_model(model_name, device=device, compute_type=compute_type, language=args['language'], asr_options=asr_options, vad_options={"vad_onset": vad_onset, "vad_offset": vad_offset},)
 
     for audio_path in args.pop("audio"):
         audio = load_audio(audio_path)

From 25be8210e59e7fdceeedbda0b6c18c3b7c8cbe01 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Tue, 25 Apr 2023 10:07:34 +0100
Subject: [PATCH 24/48] add v3 tag for install

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index b5b95c5..e34e533 100644
--- a/README.md
+++ b/README.md
@@ -81,7 +81,7 @@ See other methods [here.](https://pytorch.org/get-started/previous-versions/#whe
 
 ### 3. Install this repo
 
-`pip install git+https://github.com/m-bain/whisperx.git`
+`pip install git+https://github.com/m-bain/whisperx.git@v3`
 
 If already installed, update package to most recent commit
 
@@ -242,4 +242,4 @@ If you use this in your research, please cite the paper:
   journal={arXiv preprint, arXiv:2303.00747},
   year={2023}
 }
-```
\ No newline at end of file
+```

From db97f29678f4344496136283a08e75d5d8aba643 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Tue, 25 Apr 2023 11:19:23 +0100
Subject: [PATCH 25/48] update pip install

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e34e533..16f1e1c 100644
--- a/README.md
+++ b/README.md
@@ -75,7 +75,7 @@ GPU execution requires the NVIDIA libraries cuBLAS 11.x and cuDNN 8.x to be inst
 
 ### 2. Install PyTorch 0.11.0, e.g. for Linux and Windows:
 
-`conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch`
+`pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113`
 
 See other methods [here.](https://pytorch.org/get-started/previous-versions/#wheel-4)
 

From cc7e168d2b520b4a5e46d50b7c64cafac739db03 Mon Sep 17 00:00:00 2001
From: m-bain <36994049+m-bain@users.noreply.github.com>
Date: Tue, 25 Apr 2023 12:14:23 +0100
Subject: [PATCH 26/48] add checkout command

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 16f1e1c..c9951ce 100644
--- a/README.md
+++ b/README.md
@@ -85,12 +85,13 @@ See other methods [here.](https://pytorch.org/get-started/previous-versions/#whe
 
 If already installed, update package to most recent commit
 
-`pip install git+https://github.com/m-bain/whisperx.git --upgrade`
+`pip install git+https://github.com/m-bain/whisperx.git@v3 --upgrade`
 
 If wishing to modify this package, clone and install in editable mode:
 ```
-$ git clone https://github.com/m-bain/whisperX.git
+$ git clone https://github.com/m-bain/whisperX.git@v3
 $ cd whisperX
+$ git checkout v3
 $ pip install -e .
 ```
 

From 5b85c5433fef363d71a147a050d8089d206ce944 Mon Sep 17 00:00:00 2001
From: Max Bain <36994049+m-bain@users.noreply.github.com>
Date: Fri, 28 Apr 2023 16:47:04 +0100
Subject: [PATCH 27/48] Update setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index d6472e1..33db0e1 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ from setuptools import setup, find_packages
 setup(
     name="whisperx",
     py_modules=["whisperx"],
-    version="2.0",
+    version="2.0.1",
     description="Time-Accurate Automatic Speech Recognition using Whisper.",
     readme="README.md",
     python_requires=">=3.8",

From cb176a186ee6dab55f55c55562664bef583e3fd6 Mon Sep 17 00:00:00 2001
From: Thomas Mol <email@thomasmol.com>
Date: Sat, 29 Apr 2023 19:51:05 +0200
Subject: [PATCH 28/48] added num_workers to fix pickling error

---
 whisperx/asr.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/whisperx/asr.py b/whisperx/asr.py
index d9cbff0..b23a54d 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -207,7 +207,7 @@ class FasterWhisperPipeline(Pipeline):
         return final_iterator
 
     def transcribe(
-        self, audio: Union[str, np.ndarray], batch_size=None
+        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0
     ):
         if isinstance(audio, str):
             audio = load_audio(audio)
@@ -232,7 +232,7 @@ class FasterWhisperPipeline(Pipeline):
 
         segments = []
         batch_size = batch_size or self._batch_size
-        for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size)):
+        for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size, num_workers=num_workers)):
             text = out['text']
             if batch_size in [0, 1, None]:
                 text = text[0]

From b9c8c5072b57b3ed9ec1c121a39f56930e4b4bb9 Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Sun, 30 Apr 2023 18:34:18 +0200
Subject: [PATCH 29/48] Pad language detection if audio is too short

---
 whisperx/asr.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/whisperx/asr.py b/whisperx/asr.py
index b23a54d..ba6220b 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -251,7 +251,10 @@ class FasterWhisperPipeline(Pipeline):
 
 
     def detect_language(self, audio: np.ndarray):
-        segment = log_mel_spectrogram(audio[: N_SAMPLES], padding=0)
+        if audio.shape[0] < N_SAMPLES:
+            print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
+        segment = log_mel_spectrogram(audio[: N_SAMPLES],
+                                      padding=0 if audio.shape[0] >= N_SAMPLES else N_SAMPLES - audio.shape[0])
         encoder_output = self.model.encode(segment)
         results = self.model.model.detect_language(encoder_output)
         language_token, language_probability = results[0][0]

From 601c91140fe9056b8a40a10c923e0d09c51cd948 Mon Sep 17 00:00:00 2001
From: Prashanth Ellina <prashanthellina@gmail.com>
Date: Sun, 30 Apr 2023 17:33:24 +0000
Subject: [PATCH 30/48] references #202, attempt to fix speaker diarization
 failing in v3

---
 whisperx/alignment.py  | 4 ++--
 whisperx/transcribe.py | 6 ++++++
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index 09f044f..e2c86f7 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -450,8 +450,8 @@ def align(
                 "end": srow["end"],
                 "text": text,
                 "words": word_list,
-                # "word-segments": wseg,
-                # "char-segments": cseg
+                "word-segments": wseg,
+                "char-segments": cseg
             }
         )
     
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index fd6cf52..dab9e12 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -203,6 +203,12 @@ def cli():
 
     # >> Write
     for result, audio_path in results:
+        # Remove pandas dataframes from result so that
+        # we can serialize the result with json
+        for seg in result["segments"]:
+            seg.pop("word-segments", None)
+            seg.pop("char-segments", None)
+
         writer(result, audio_path, writer_args)
 
 if __name__ == "__main__":

From 5becc99e56e184f2103df2aaf71798626ebdaf46 Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Mon, 1 May 2023 13:47:41 +0200
Subject: [PATCH 31/48] Version bump pyannote, pytorch

---
 requirements.txt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 2747e2d..d569c15 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
-torch==1.11.0
-torchaudio==0.11.0
-pyannote.audio
+torch==1.13.1
+torchaudio==0.13.1
+pyannote.audio==2.1.1
 faster-whisper
 transformers
 ffmpeg-python==0.2.0

From 64ca208cc88d801d1c6295215b4f4f535dd806e3 Mon Sep 17 00:00:00 2001
From: Arnav Mehta <65492948+arnavmehta7@users.noreply.github.com>
Date: Tue, 2 May 2023 13:13:02 +0530
Subject: [PATCH 32/48] Fixed the word_start variable not initialized bug.

---
 whisperx/alignment.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index e2c86f7..38c2f00 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -439,8 +439,8 @@ def align(
         word_list.append(
             {
                 "word": curr_text.rstrip(),
-                "start": word_start,
-                "end": word_end,
+                "start": wseg.iloc[wdx]['start'],
+                "end": wseg.iloc[wdx]['end'],
             }
         )
 

From a693a779facae27a4867ca360d1c32d1f2714c25 Mon Sep 17 00:00:00 2001
From: sohaibanwaar <sohaibanwaar36@gmail.com>
Date: Tue, 2 May 2023 13:28:20 +0500
Subject: [PATCH 33/48] feat: adding the docker file

---
 .gitignore               |  3 +-
 Dockerfile               | 19 +++++++++
 README.md                | 11 ++++-
 notebooks/whisperx.ipynb | 91 ++++++++++++++++++++++++++++++++++++++++
 4 files changed, 122 insertions(+), 2 deletions(-)
 create mode 100644 Dockerfile
 create mode 100644 notebooks/whisperx.ipynb

diff --git a/.gitignore b/.gitignore
index f137a5b..540c132 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 whisperx.egg-info/
-**/__pycache__/
\ No newline at end of file
+**/__pycache__/
+.ipynb_checkpoints
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..94df6de
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,19 @@
+FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-devel
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub && \
+    apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+RUN apt-get update && \
+    apt-get install -y wget && \
+    wget -qO - https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub | apt-key add - && \
+    apt-get update && \
+    apt-get install -y git && \
+    apt-get install libsndfile1 -y && \
+    apt-get clean
+    
+RUN pip install --upgrade pip
+RUN pip install --upgrade setuptools
+RUN pip install git+https://github.com/m-bain/whisperx.git
+RUN pip install jupyter ipykernel
+EXPOSE 8888
+# Use external volume for data
+ENV NVIDIA_VISIBLE_DEVICES 1
+CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--NotebookApp.token=''","--NotebookApp.password=''", "--allow-root"]
diff --git a/README.md b/README.md
index 2bffa43..502bd6f 100644
--- a/README.md
+++ b/README.md
@@ -77,7 +77,16 @@ $ pip install -e .
 
 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
 
-
+### Docker 
+Alternatively, you can use the docker image provided in this repo. To build the image, run the following command from the root of this repo:
+1. In this image you can find jupyter notebook where you can easily run and debug the code.
+```bash
+docker build -t whisperx .
+```
+2. To run the image, run the following command:
+```bash
+docker run --gpus=all  -it -v <local code dir>:/workspace -p 8888:8888 whisperx
+```
 ### Setup not working???
 Safest to use install pytorch as follows (for gpu)
 
diff --git a/notebooks/whisperx.ipynb b/notebooks/whisperx.ipynb
new file mode 100644
index 0000000..34f4b6b
--- /dev/null
+++ b/notebooks/whisperx.ipynb
@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "11fc5246",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /opt/conda/lib/python3.8/site-packages/torchvision/image.so: undefined symbol: _ZNK3c1010TensorImpl36is_contiguous_nondefault_policy_implENS_12MemoryFormatE\n",
+      "  warn(f\"Failed to load image Python extension: {e}\")\n"
+     ]
+    },
+    {
+     "ename": "OutOfMemoryError",
+     "evalue": "CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 7.79 GiB total capacity; 5.76 GiB already allocated; 59.19 MiB free; 6.06 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_66/1447832577.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# transcribe with original whisper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwhisper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"large\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranscribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/whisper/__init__.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(name, device, download_root, in_memory)\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alignment_heads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malignment_heads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    987\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 989\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m     def register_backward_hook(\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    662\u001b[0m             \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 664\u001b[0;31m                 \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    665\u001b[0m             \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mshould_use_set_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    985\u001b[0m                 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,\n\u001b[1;32m    986\u001b[0m                             non_blocking, memory_format=convert_to_format)\n\u001b[0;32m--> 987\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 7.79 GiB total capacity; 5.76 GiB already allocated; 59.19 MiB free; 6.06 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
+     ]
+    }
+   ],
+   "source": [
+    "import whisperx\n",
+    "import whisper\n",
+    "\n",
+    "device = \"cuda\" \n",
+    "audio_file = \"audio.mp3\"\n",
+    "\n",
+    "# transcribe with original whisper\n",
+    "model = whisper.load_model(\"large\", device)\n",
+    "result = model.transcribe(audio_file)\n",
+    "\n",
+    "print(result[\"segments\"]) # before alignment\n",
+    "\n",
+    "# load alignment model and metadata\n",
+    "model_a, metadata = whisperx.load_align_model(language_code=result[\"language\"], device=device)\n",
+    "\n",
+    "# align whisper output\n",
+    "result_aligned = whisperx.align(result[\"segments\"], model_a, metadata, audio_file, device)\n",
+    "\n",
+    "print(result_aligned[\"segments\"]) # after alignment\n",
+    "print(result_aligned[\"word_segments\"]) # after alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b63e6170",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From b6665230042406c7c76d91b8a7c46dc88c2a2bd0 Mon Sep 17 00:00:00 2001
From: Max Bain <36994049+m-bain@users.noreply.github.com>
Date: Tue, 2 May 2023 15:10:40 +0100
Subject: [PATCH 34/48] add v3 pre-release comment, and v4 progress update

---
 README.md | 50 +++++++++-----------------------------------------
 1 file changed, 9 insertions(+), 41 deletions(-)

diff --git a/README.md b/README.md
index 502bd6f..25a3f9c 100644
--- a/README.md
+++ b/README.md
@@ -52,7 +52,8 @@ This repository refines the timestamps of openAI's Whisper model via forced alig
 
 <h2 align="left", id="highlights">New🚨</h2>
 
-- v2 released, code cleanup, imports whisper library, batched inference from paper not included (contact for licensing / batched model API). VAD filtering is now turned on by default, as in the paper.
+- v3 pre-release [this branch](https://github.com/m-bain/whisperX/tree/v3) *70x speed-up open-sourced. Using batched whisper with faster-whisper backend*!
+- v2 released, code cleanup, imports whisper library. VAD filtering is now turned on by default, as in the paper.
 - Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed (not provided in this repo).
 - VAD filtering: Voice Activity Detection (VAD) from [Pyannote.audio](https://huggingface.co/pyannote/voice-activity-detection) is used as a preprocessing step to remove reliance on whisper timestamps and only transcribe audio segments containing speech. add `--vad_filter True` flag, increases timestamp accuracy and robustness (requires more GPU mem due to 30s inputs in wav2vec2)
 - Character level timestamps (see `*.char.ass` file output)
@@ -179,7 +180,7 @@ In addition to forced alignment, the following two modifications have been made
 
 If you are multilingual, a major way you can contribute to this project is to find phoneme models on huggingface (or train your own) and test them on speech for the target language. If the results look good send a merge request and some examples showing its success.
 
-The next major upgrade we are working on is whisper with speaker diarization, so if you have any experience on this please share.
+Bug finding and pull requests are also highly appreciated to keep this project going, since it's already diverging from the original research scope.
 
 <h2 align="left" id="coming-soon">Coming Soon 🗓</h2>
 
@@ -195,16 +196,14 @@ The next major upgrade we are working on is whisper with speaker diarization, so
 
 * [x] Incorporating  speaker diarization
 
-* [ ] Automatic .wav conversion to make VAD compatible
+* [x] Model flush, for low gpu mem resources
 
-* [ ] Model flush, for low gpu mem resources
-
-* [ ] Improve diarization (word level). *Harder than first thought...*
+* [ ] Improve diarization (word level). *Harder than first thought... see #below*
 
 
 <h2 align="left" id="contact">Contact/Support 📇</h2>
 
-Contact maxhbain@gmail.com for queries and licensing / early access to a model API with batched inference (transcribe 1hr audio in under 1min).
+Contact maxhbain@gmail.com for queries. WhisperX v4 development is underway with *with siginificantly improved diarization*. To support v4 and get early access, get in touch.
 
 <a href="https://www.buymeacoffee.com/maxhbain" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
 
@@ -216,7 +215,9 @@ This work, and my PhD, is supported by the [VGG (Visual Geometry Group)](https:/
 
 
 Of course, this is builds on [openAI's whisper](https://github.com/openai/whisper).
-And borrows important alignment code from [PyTorch tutorial on forced alignment](https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html)
+Borrows important alignment code from [PyTorch tutorial on forced alignment](https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html)
+And uses the wonderful pyannote VAD / Diarization https://github.com/pyannote/pyannote-audio
+
 
 
 <h2 align="left" id="cite">Citation</h2>
@@ -230,36 +231,3 @@ If you use this in your research, please cite the paper:
   year={2023}
 }
 ```
-
-as well the following works, used in each stage of the pipeline:
-
-```bibtex
-@article{radford2022robust,
-  title={Robust speech recognition via large-scale weak supervision},
-  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
-  journal={arXiv preprint arXiv:2212.04356},
-  year={2022}
-}
-```
-
-```bibtex
-@article{baevski2020wav2vec,
-  title={wav2vec 2.0: A framework for self-supervised learning of speech representations},
-  author={Baevski, Alexei and Zhou, Yuhao and Mohamed, Abdelrahman and Auli, Michael},
-  journal={Advances in neural information processing systems},
-  volume={33},
-  pages={12449--12460},
-  year={2020}
-}
-```
-
-```bibtex
-@inproceedings{bredin2020pyannote,
-  title={Pyannote. audio: neural building blocks for speaker diarization},
-  author={Bredin, Herv{\'e} and Yin, Ruiqing and Coria, Juan Manuel and Gelly, Gregory and Korshunov, Pavel and Lavechin, Marvin and Fustes, Diego and Titeux, Hadrien and Bouaziz, Wassim and Gill, Marie-Philippe},
-  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
-  pages={7124--7128},
-  year={2020},
-  organization={IEEE}
-}
-```

From 067189248f17b15d8af2cad3f38bcfe44911a8f2 Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Tue, 2 May 2023 18:44:43 +0200
Subject: [PATCH 35/48] Use pyannote develop branch and torch version 2

---
 requirements.txt | 5 ++---
 setup.py         | 2 +-
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index d569c15..f4f9c21 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,5 @@
-torch==1.13.1
-torchaudio==0.13.1
-pyannote.audio==2.1.1
+torch==2.0.0
+torchaudio==2.0.1
 faster-whisper
 transformers
 ffmpeg-python==0.2.0
diff --git a/setup.py b/setup.py
index 2060d42..1eaaeb9 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ setup(
         for r in pkg_resources.parse_requirements(
             open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
         )
-    ],
+    ] + ["pyannote.audio @ https://github.com/pyannote/pyannote-audio/archive/refs/heads/develop.zip"],
     entry_points = {
         'console_scripts': ['whisperx=whisperx.transcribe:cli'],
     },

From 2a6830492c7e9499a20e719ec5806b99b25af8ed Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Tue, 2 May 2023 20:25:56 +0200
Subject: [PATCH 36/48] Fix pyannote to specific commit

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 1eaaeb9..859d171 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ setup(
         for r in pkg_resources.parse_requirements(
             open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
         )
-    ] + ["pyannote.audio @ https://github.com/pyannote/pyannote-audio/archive/refs/heads/develop.zip"],
+    ] + ["pyannote.audio @ git+https://github.com/pyannote/pyannote-audio@11b56a137a578db9335efc00298f6ec1932e6317"],
     entry_points = {
         'console_scripts': ['whisperx=whisperx.transcribe:cli'],
     },

From cb53661070cab184ff2182e6498e715709e1a61e Mon Sep 17 00:00:00 2001
From: aramlang <100400031+aramlang@users.noreply.github.com>
Date: Wed, 3 May 2023 11:26:12 -0500
Subject: [PATCH 37/48] Enable Hebrew support

---
 whisperx/alignment.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index 38c2f00..2ae77f3 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -38,6 +38,7 @@ DEFAULT_ALIGN_MODELS_HF = {
     "fa": "jonatasgrosman/wav2vec2-large-xlsr-53-persian",
     "el": "jonatasgrosman/wav2vec2-large-xlsr-53-greek",
     "tr": "mpoyraz/wav2vec2-xls-r-300m-cv7-turkish",
+    "he": "imvladikon/wav2vec2-xls-r-300m-hebrew",
 }
 
 
@@ -231,8 +232,13 @@ def align(
 
             emission = emissions[0].cpu().detach()
 
-            trellis = get_trellis(emission, tokens)
-            path = backtrack(trellis, emission, tokens)
+            blank_id = 0
+            for char, code in model_dictionary.items():
+                if char == '[pad]' or char == '<pad>':
+                    blank_id = code
+
+            trellis = get_trellis(emission, tokens, blank_id)
+            path = backtrack(trellis, emission, tokens, blank_id)
             if path is None:
                 print(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original...')
                 break

From 2d59eb97260037113d85edcfe2713117987bd218 Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Wed, 3 May 2023 23:17:44 +0200
Subject: [PATCH 38/48] Add torch compile to log mel spectrogram

---
 whisperx/asr.py   |  5 ++++-
 whisperx/audio.py | 44 +++++++++++++++-----------------------------
 2 files changed, 19 insertions(+), 30 deletions(-)

diff --git a/whisperx/asr.py b/whisperx/asr.py
index ba6220b..1ca12ce 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -181,6 +181,9 @@ class FasterWhisperPipeline(Pipeline):
 
     def preprocess(self, audio):
         audio = audio['inputs']
+        if isinstance(audio, np.ndarray):
+            audio = torch.from_numpy(audio)
+
         features = log_mel_spectrogram(audio, padding=N_SAMPLES - audio.shape[0])
         return {'inputs': features}
 
@@ -253,7 +256,7 @@ class FasterWhisperPipeline(Pipeline):
     def detect_language(self, audio: np.ndarray):
         if audio.shape[0] < N_SAMPLES:
             print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
-        segment = log_mel_spectrogram(audio[: N_SAMPLES],
+        segment = log_mel_spectrogram(torch.from_numpy(audio[:N_SAMPLES]),
                                       padding=0 if audio.shape[0] >= N_SAMPLES else N_SAMPLES - audio.shape[0])
         encoder_output = self.model.encode(segment)
         results = self.model.model.detect_language(encoder_output)
diff --git a/whisperx/audio.py b/whisperx/audio.py
index 513ab7c..8ac0674 100644
--- a/whisperx/audio.py
+++ b/whisperx/audio.py
@@ -22,6 +22,12 @@ N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2  # the initial convolutions has stride 2
 FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH)  # 10ms per audio frame
 TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audio token
 
+with np.load(
+    os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
+) as f:
+    MEL_FILTERS = torch.from_numpy(f[f"mel_{80}"])
+
+
 
 def load_audio(file: str, sr: int = SAMPLE_RATE):
     """
@@ -79,27 +85,9 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
     return array
 
 
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
-    """
-    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
-    Allows decoupling librosa dependency; saved using:
-
-        np.savez_compressed(
-            "mel_filters.npz",
-            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
-        )
-    """
-    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
-    with np.load(
-        os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
-    ) as f:
-        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
-
-
+@torch.compile(fullgraph=True)
 def log_mel_spectrogram(
-    audio: Union[str, np.ndarray, torch.Tensor],
-    n_mels: int = N_MELS,
+    audio: torch.Tensor,
     padding: int = 0,
     device: Optional[Union[str, torch.device]] = None,
 ):
@@ -108,7 +96,7 @@ def log_mel_spectrogram(
 
     Parameters
     ----------
-    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+    audio: torch.Tensor, shape = (*)
         The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
 
     n_mels: int
@@ -125,21 +113,19 @@ def log_mel_spectrogram(
     torch.Tensor, shape = (80, n_frames)
         A Tensor that contains the Mel spectrogram
     """
-    if not torch.is_tensor(audio):
-        if isinstance(audio, str):
-            audio = load_audio(audio)
-        audio = torch.from_numpy(audio)
+    global MEL_FILTERS
 
     if device is not None:
         audio = audio.to(device)
     if padding > 0:
         audio = F.pad(audio, (0, padding))
     window = torch.hann_window(N_FFT).to(audio.device)
-    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
-    magnitudes = stft[..., :-1].abs() ** 2
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=False)
+    # Square the real and imaginary components and sum them together, similar to torch.abs() on complex tensors
+    magnitudes = (stft[:, :-1, :] ** 2).sum(dim=-1)
 
-    filters = mel_filters(audio.device, n_mels)
-    mel_spec = filters @ magnitudes
+    MEL_FILTERS = MEL_FILTERS.to(audio.device)
+    mel_spec = MEL_FILTERS @ magnitudes
 
     log_spec = torch.clamp(mel_spec, min=1e-10).log10()
     log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)

From d8f0ef4a19a7cd06bc6afb8ce97f575e51655b68 Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Thu, 4 May 2023 16:25:34 +0200
Subject: [PATCH 39/48] Set diarization device manually

---
 whisperx/diarize.py    | 7 ++++++-
 whisperx/transcribe.py | 3 ++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/whisperx/diarize.py b/whisperx/diarize.py
index 34dfc63..6f8c257 100644
--- a/whisperx/diarize.py
+++ b/whisperx/diarize.py
@@ -1,14 +1,19 @@
 import numpy as np
 import pandas as pd
 from pyannote.audio import Pipeline
+from typing import Optional, Union
+import torch
 
 class DiarizationPipeline:
     def __init__(
         self,
         model_name="pyannote/speaker-diarization@2.1",
         use_auth_token=None,
+        device: Optional[Union[str, torch.device]] = "cpu",
     ):
-        self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token)
+        if isinstance(device, str):
+            device = torch.device(device)
+        self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token).to(device)
 
     def __call__(self, audio, min_speakers=None, max_speakers=None):
         segments = self.model(audio, min_speakers=min_speakers, max_speakers=max_speakers)
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index dab9e12..e284e83 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -193,8 +193,9 @@ def cli():
         if hf_token is None:
             print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
         tmp_results = results
+        print(">>Performing diarization...")
         results = []
-        diarize_model = DiarizationPipeline(use_auth_token=hf_token)
+        diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device)
         for result, input_audio_path in tmp_results:
             diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
             results_segments, word_segments = assign_word_speakers(diarize_segments, result["segments"])

From 4e2ac4e4e9da9b2b392c13f68f0718e86d293fb0 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Thu, 4 May 2023 20:38:13 +0100
Subject: [PATCH 40/48] torch2.0, remove compile for now, round to times to 3
 decimal

---
 README.md              | 12 ++++++------
 setup.py               |  2 +-
 whisperx/alignment.py  |  4 ++--
 whisperx/asr.py        |  5 +----
 whisperx/audio.py      | 44 ++++++++++++++++++++++++++++--------------
 whisperx/transcribe.py |  7 +------
 6 files changed, 40 insertions(+), 34 deletions(-)

diff --git a/README.md b/README.md
index c9951ce..1f41bb9 100644
--- a/README.md
+++ b/README.md
@@ -61,23 +61,23 @@ This repository refines the timestamps of openAI's Whisper model via forced alig
 
 
 <h2 align="left" id="setup">Setup ⚙️</h2>
-Tested for PyTorch 0.11, Python 3.8 (use other versions at your own risk!)
+Tested for PyTorch 2.0, Python 3.10 (use other versions at your own risk!)
 
 GPU execution requires the NVIDIA libraries cuBLAS 11.x and cuDNN 8.x to be installed on the system. Please refer to the [CTranslate2 documentation](https://opennmt.net/CTranslate2/installation.html).
 
 
-### 1. Create Python3.8 environment
+### 1. Create Python3.10 environment
 
-`conda create --name whisperx python=3.8`
+`conda create --name whisperx python=3.10`
 
 `conda activate whisperx`
 
 
-### 2. Install PyTorch 0.11.0, e.g. for Linux and Windows:
+### 2. Install PyTorch2.0, e.g. for Linux and Windows CUDA11.7:
 
-`pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113`
+`pip3 install torch torchvision torchaudio`
 
-See other methods [here.](https://pytorch.org/get-started/previous-versions/#wheel-4)
+See other methods [here.](https://pytorch.org/get-started/locally/)
 
 ### 3. Install this repo
 
diff --git a/setup.py b/setup.py
index 859d171..66f22cd 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ from setuptools import setup, find_packages
 setup(
     name="whisperx",
     py_modules=["whisperx"],
-    version="3.0.0",
+    version="3.0.2",
     description="Time-Accurate Automatic Speech Recognition using Whisper.",
     readme="README.md",
     python_requires=">=3.8",
diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index 2ae77f3..e63e6e5 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -268,8 +268,8 @@ def align(
                 start, end, score = None, None, None
                 if cdx in clean_cdx:
                     char_seg = char_segments[clean_cdx.index(cdx)]
-                    start = char_seg.start * ratio + t1
-                    end = char_seg.end * ratio + t1
+                    start = round(char_seg.start * ratio + t1, 3)
+                    end = round(char_seg.end * ratio + t1, 3)
                     score = char_seg.score        
 
                 char_segments_arr["char"].append(char)
diff --git a/whisperx/asr.py b/whisperx/asr.py
index 1ca12ce..ba6220b 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -181,9 +181,6 @@ class FasterWhisperPipeline(Pipeline):
 
     def preprocess(self, audio):
         audio = audio['inputs']
-        if isinstance(audio, np.ndarray):
-            audio = torch.from_numpy(audio)
-
         features = log_mel_spectrogram(audio, padding=N_SAMPLES - audio.shape[0])
         return {'inputs': features}
 
@@ -256,7 +253,7 @@ class FasterWhisperPipeline(Pipeline):
     def detect_language(self, audio: np.ndarray):
         if audio.shape[0] < N_SAMPLES:
             print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
-        segment = log_mel_spectrogram(torch.from_numpy(audio[:N_SAMPLES]),
+        segment = log_mel_spectrogram(audio[: N_SAMPLES],
                                       padding=0 if audio.shape[0] >= N_SAMPLES else N_SAMPLES - audio.shape[0])
         encoder_output = self.model.encode(segment)
         results = self.model.model.detect_language(encoder_output)
diff --git a/whisperx/audio.py b/whisperx/audio.py
index 8ac0674..513ab7c 100644
--- a/whisperx/audio.py
+++ b/whisperx/audio.py
@@ -22,12 +22,6 @@ N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2  # the initial convolutions has stride 2
 FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH)  # 10ms per audio frame
 TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audio token
 
-with np.load(
-    os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
-) as f:
-    MEL_FILTERS = torch.from_numpy(f[f"mel_{80}"])
-
-
 
 def load_audio(file: str, sr: int = SAMPLE_RATE):
     """
@@ -85,9 +79,27 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
     return array
 
 
-@torch.compile(fullgraph=True)
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+    """
+    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+    Allows decoupling librosa dependency; saved using:
+
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+        )
+    """
+    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+    with np.load(
+        os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
+    ) as f:
+        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
 def log_mel_spectrogram(
-    audio: torch.Tensor,
+    audio: Union[str, np.ndarray, torch.Tensor],
+    n_mels: int = N_MELS,
     padding: int = 0,
     device: Optional[Union[str, torch.device]] = None,
 ):
@@ -96,7 +108,7 @@ def log_mel_spectrogram(
 
     Parameters
     ----------
-    audio: torch.Tensor, shape = (*)
+    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
         The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
 
     n_mels: int
@@ -113,19 +125,21 @@ def log_mel_spectrogram(
     torch.Tensor, shape = (80, n_frames)
         A Tensor that contains the Mel spectrogram
     """
-    global MEL_FILTERS
+    if not torch.is_tensor(audio):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio = torch.from_numpy(audio)
 
     if device is not None:
         audio = audio.to(device)
     if padding > 0:
         audio = F.pad(audio, (0, padding))
     window = torch.hann_window(N_FFT).to(audio.device)
-    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=False)
-    # Square the real and imaginary components and sum them together, similar to torch.abs() on complex tensors
-    magnitudes = (stft[:, :-1, :] ** 2).sum(dim=-1)
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+    magnitudes = stft[..., :-1].abs() ** 2
 
-    MEL_FILTERS = MEL_FILTERS.to(audio.device)
-    mel_spec = MEL_FILTERS @ magnitudes
+    filters = mel_filters(audio.device, n_mels)
+    mel_spec = filters @ magnitudes
 
     log_spec = torch.clamp(mel_spec, min=1e-10).log10()
     log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index e284e83..4b5a664 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -72,7 +72,6 @@ def cli():
 
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
     # parser.add_argument("--model_flush", action="store_true", help="Flush memory from each model after use, reduces GPU requirement but slower processing >1 audio file.")
-    parser.add_argument("--tmp_dir", default=None, help="Temporary directory to write audio file if input if not .wav format (only for VAD).")
     # fmt: on
 
     args = parser.parse_args().__dict__
@@ -86,10 +85,6 @@ def cli():
     # model_flush: bool = args.pop("model_flush")
     os.makedirs(output_dir, exist_ok=True)
 
-    tmp_dir: str = args.pop("tmp_dir")
-    if tmp_dir is not None:
-        os.makedirs(tmp_dir, exist_ok=True)
-
     align_model: str = args.pop("align_model")
     interpolate_method: str = args.pop("interpolate_method")
     no_align: bool = args.pop("no_align")
@@ -195,7 +190,7 @@ def cli():
         tmp_results = results
         print(">>Performing diarization...")
         results = []
-        diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device)
+        diarize_model = DiarizationPipeline(use_auth_token=hf_token)
         for result, input_audio_path in tmp_results:
             diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
             results_segments, word_segments = assign_word_speakers(diarize_segments, result["segments"])

From 07361ba1d7e10c218ef30dd465b92e89ddebb5c5 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Fri, 5 May 2023 11:53:51 +0100
Subject: [PATCH 41/48] add device to dia pipeline @sorgfresser

---
 whisperx/diarize.py    | 1 +
 whisperx/transcribe.py | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/whisperx/diarize.py b/whisperx/diarize.py
index 6f8c257..93ff41d 100644
--- a/whisperx/diarize.py
+++ b/whisperx/diarize.py
@@ -11,6 +11,7 @@ class DiarizationPipeline:
         use_auth_token=None,
         device: Optional[Union[str, torch.device]] = "cpu",
     ):
+        self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token)
         if isinstance(device, str):
             device = torch.device(device)
         self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token).to(device)
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index 4b5a664..f3f63fe 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -190,7 +190,7 @@ def cli():
         tmp_results = results
         print(">>Performing diarization...")
         results = []
-        diarize_model = DiarizationPipeline(use_auth_token=hf_token)
+        diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device)
         for result, input_audio_path in tmp_results:
             diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
             results_segments, word_segments = assign_word_speakers(diarize_segments, result["segments"])

From 24008aa1ed67c4f75c90107b4937178a1452519d Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Sun, 7 May 2023 15:32:58 +0100
Subject: [PATCH 42/48] fix long segments, break into sentences using nltk,
 improve align logic, improve diarize (sentence-based)

---
 requirements.txt       |   3 +-
 whisperx/alignment.py  | 519 ++++++++++++++---------------------------
 whisperx/asr.py        | 155 +-----------
 whisperx/diarize.py    |  82 +++----
 whisperx/transcribe.py |  17 +-
 whisperx/utils.py      |  67 ++++--
 6 files changed, 269 insertions(+), 574 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index f4f9c21..ec90a07 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,4 +4,5 @@ faster-whisper
 transformers
 ffmpeg-python==0.2.0
 pandas
-setuptools==65.6.3
\ No newline at end of file
+setuptools==65.6.3
+nltk
\ No newline at end of file
diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index e63e6e5..2812c10 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -13,6 +13,7 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
 from .audio import SAMPLE_RATE, load_audio
 from .utils import interpolate_nans
+import nltk
 
 LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]
 
@@ -84,386 +85,226 @@ def align(
     align_model_metadata: dict,
     audio: Union[str, np.ndarray, torch.Tensor],
     device: str,
-    extend_duration: float = 0.0,
-    start_from_previous: bool = True,
     interpolate_method: str = "nearest",
+    return_char_alignments: bool = False,
 ):
     """
-    Force align phoneme recognition predictions to known transcription
-
-    Parameters
-    ----------
-    transcript: Iterator[dict]
-        The Whisper model instance
-    
-    model: torch.nn.Module
-        Alignment model (wav2vec2)
-
-    audio: Union[str, np.ndarray, torch.Tensor]
-        The path to the audio file to open, or the audio waveform
-
-    device: str
-        cuda device
-
-    diarization: pd.DataFrame {'start': List[float], 'end': List[float], 'speaker': List[float]}
-        diarization segments with speaker labels.
-
-    extend_duration: float
-        Amount to pad input segments by. If not using vad--filter then recommended to use 2 seconds
-
-        If the gzip compression ratio is above this value, treat as failed
-
-    interpolate_method: str ["nearest", "linear", "ignore"]
-        Method to assign timestamps to non-aligned words. Words are not able to be aligned when none of the characters occur in the align model dictionary.
-        "nearest" copies timestamp of nearest word within the segment. "linear" is linear interpolation. "drop" removes that word from output.
-
-    Returns
-    -------
-    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
-    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
+    Align phoneme recognition predictions to known transcription.
     """
+    
     if not torch.is_tensor(audio):
         if isinstance(audio, str):
             audio = load_audio(audio)
         audio = torch.from_numpy(audio)
     if len(audio.shape) == 1:
         audio = audio.unsqueeze(0)
-
+    
     MAX_DURATION = audio.shape[1] / SAMPLE_RATE
 
     model_dictionary = align_model_metadata["dictionary"]
     model_lang = align_model_metadata["language"]
     model_type = align_model_metadata["type"]
 
-    aligned_segments = []
-
-    prev_t2 = 0
-
-    char_segments_arr = {
-        "segment-idx": [],
-        "subsegment-idx": [],
-        "word-idx": [],
-        "char": [],
-        "start": [],
-        "end": [],
-        "score": [],
-    }
-
+    # 1. Preprocess to keep only characters in dictionary
     for sdx, segment in enumerate(transcript):
-        while True:
-            segment_align_success = False
+        # strip spaces at beginning / end, but keep track of the amount.
+        num_leading = len(segment["text"]) - len(segment["text"].lstrip())
+        num_trailing = len(segment["text"]) - len(segment["text"].rstrip())
+        text = segment["text"]
 
-            # strip spaces at beginning / end, but keep track of the amount.
-            num_leading = len(segment["text"]) - len(segment["text"].lstrip())
-            num_trailing = len(segment["text"]) - len(segment["text"].rstrip())
-            transcription = segment["text"]
+        # split into words
+        if model_lang not in LANGUAGES_WITHOUT_SPACES:
+            per_word = text.split(" ")
+        else:
+            per_word = text
 
-            # TODO: convert number tokenizer / symbols to phonetic words for alignment.
-            # e.g. "$300" -> "three hundred dollars"
-            # currently "$300" is ignored since no characters present in the phonetic dictionary
-
-            # split into words
+        clean_char, clean_cdx = [], []
+        for cdx, char in enumerate(text):
+            char_ = char.lower()
+            # wav2vec2 models use "|" character to represent spaces
             if model_lang not in LANGUAGES_WITHOUT_SPACES:
-                per_word = transcription.split(" ")
-            else:
-                per_word = transcription
-
-            # first check that characters in transcription can be aligned (they are contained in align model"s dictionary)
-            clean_char, clean_cdx = [], []
-            for cdx, char in enumerate(transcription):
-                char_ = char.lower()
-                # wav2vec2 models use "|" character to represent spaces
-                if model_lang not in LANGUAGES_WITHOUT_SPACES:
-                    char_ = char_.replace(" ", "|")
-                
-                # ignore whitespace at beginning and end of transcript
-                if cdx < num_leading:
-                    pass
-                elif cdx > len(transcription) - num_trailing - 1:
-                    pass
-                elif char_ in model_dictionary.keys():
-                    clean_char.append(char_)
-                    clean_cdx.append(cdx)
-
-            clean_wdx = []
-            for wdx, wrd in enumerate(per_word):
-                if any([c in model_dictionary.keys() for c in wrd]):
-                    clean_wdx.append(wdx)
-
-            # if no characters are in the dictionary, then we skip this segment...
-            if len(clean_char) == 0:
-                print(f'Failed to align segment ("{segment["text"]}"): no characters in this segment found in model dictionary, resorting to original...')
-                break          
-           
-            transcription_cleaned = "".join(clean_char)
-            tokens = [model_dictionary[c] for c in transcription_cleaned]
-
-            # we only pad if not using VAD filtering
-            if "seg_text" not in segment:
-                # pad according original timestamps
-                t1 = max(segment["start"] - extend_duration, 0)
-                t2 = min(segment["end"] + extend_duration, MAX_DURATION)
-
-            # use prev_t2 as current t1 if it"s later
-            if start_from_previous and t1 < prev_t2:
-                t1 = prev_t2
-
-            # check if timestamp range is still valid
-            if t1 >= MAX_DURATION:
-                print("Failed to align segment: original start time longer than audio duration, skipping...")
-                break
-            if t2 - t1 < 0.02:
-                print("Failed to align segment: duration smaller than 0.02s time precision")
-                break
-
-            f1 = int(t1 * SAMPLE_RATE)
-            f2 = int(t2 * SAMPLE_RATE)
-
-            waveform_segment = audio[:, f1:f2]
-
-            with torch.inference_mode():
-                if model_type == "torchaudio":
-                    emissions, _ = model(waveform_segment.to(device))
-                elif model_type == "huggingface":
-                    emissions = model(waveform_segment.to(device)).logits
-                else:
-                    raise NotImplementedError(f"Align model of type {model_type} not supported.")
-                emissions = torch.log_softmax(emissions, dim=-1)
-
-            emission = emissions[0].cpu().detach()
-
-            blank_id = 0
-            for char, code in model_dictionary.items():
-                if char == '[pad]' or char == '<pad>':
-                    blank_id = code
-
-            trellis = get_trellis(emission, tokens, blank_id)
-            path = backtrack(trellis, emission, tokens, blank_id)
-            if path is None:
-                print(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original...')
-                break
-            char_segments = merge_repeats(path, transcription_cleaned)
-            # word_segments = merge_words(char_segments)
+                char_ = char_.replace(" ", "|")
             
+            # ignore whitespace at beginning and end of transcript
+            if cdx < num_leading:
+                pass
+            elif cdx > len(text) - num_trailing - 1:
+                pass
+            elif char_ in model_dictionary.keys():
+                clean_char.append(char_)
+                clean_cdx.append(cdx)
 
-            # sub-segments
-            if "seg-text" not in segment:
-                segment["seg-text"] = [transcription]
-                
-            seg_lens = [0] + [len(x) for x in segment["seg-text"]]
-            seg_lens_cumsum = list(np.cumsum(seg_lens))
-            sub_seg_idx = 0
-
-            wdx = 0
-            duration = t2 - t1
-            ratio = duration * waveform_segment.size(0) / (trellis.size(0) - 1)
-            for cdx, char in enumerate(transcription + " "):
-                is_last = False
-                if cdx == len(transcription):
-                    break
-                elif cdx+1 == len(transcription):
-                    is_last = True
-
-                
-                start, end, score = None, None, None
-                if cdx in clean_cdx:
-                    char_seg = char_segments[clean_cdx.index(cdx)]
-                    start = round(char_seg.start * ratio + t1, 3)
-                    end = round(char_seg.end * ratio + t1, 3)
-                    score = char_seg.score        
-
-                char_segments_arr["char"].append(char)
-                char_segments_arr["start"].append(start)
-                char_segments_arr["end"].append(end)
-                char_segments_arr["score"].append(score)
-                char_segments_arr["word-idx"].append(wdx)
-                char_segments_arr["segment-idx"].append(sdx)
-                char_segments_arr["subsegment-idx"].append(sub_seg_idx)
-
-                # word-level info
-                if model_lang in LANGUAGES_WITHOUT_SPACES:
-                    # character == word
-                    wdx += 1
-                elif is_last or transcription[cdx+1] == " " or cdx == seg_lens_cumsum[sub_seg_idx+1] - 1:
-                    wdx += 1
-
-                if is_last or cdx == seg_lens_cumsum[sub_seg_idx+1] - 1:
-                    wdx = 0
-                    sub_seg_idx += 1
-
-            prev_t2 = segment["end"]
-
-            segment_align_success = True
-            # end while True loop
-            break
-
-        # reset prev_t2 due to drifting issues
-        if not segment_align_success:
-            prev_t2 = 0
-        
-    char_segments_arr = pd.DataFrame(char_segments_arr)
-    not_space = char_segments_arr["char"] != " "
-
-    per_seg_grp = char_segments_arr.groupby(["segment-idx", "subsegment-idx"], as_index = False)
-    char_segments_arr = per_seg_grp.apply(lambda x: x.reset_index(drop = True)).reset_index()
-    per_word_grp = char_segments_arr[not_space].groupby(["segment-idx", "subsegment-idx", "word-idx"])
-    per_subseg_grp = char_segments_arr[not_space].groupby(["segment-idx", "subsegment-idx"])
-    per_seg_grp = char_segments_arr[not_space].groupby(["segment-idx"])
-    char_segments_arr["local-char-idx"] = char_segments_arr.groupby(["segment-idx", "subsegment-idx"]).cumcount()
-    per_word_grp = char_segments_arr[not_space].groupby(["segment-idx", "subsegment-idx", "word-idx"]) # regroup
-
-    word_segments_arr = {}
-
-    # start of word is first char with a timestamp
-    word_segments_arr["start"] = per_word_grp["start"].min().values
-    # end of word is last char with a timestamp
-    word_segments_arr["end"] = per_word_grp["end"].max().values
-    # score of word is mean (excluding nan)
-    word_segments_arr["score"] = per_word_grp["score"].mean().values
-
-    word_segments_arr["segment-text-start"] = per_word_grp["local-char-idx"].min().astype(int).values
-    word_segments_arr["segment-text-end"] = per_word_grp["local-char-idx"].max().astype(int).values+1
-    word_segments_arr = pd.DataFrame(word_segments_arr)
-
-    word_segments_arr[["segment-idx", "subsegment-idx", "word-idx"]] = per_word_grp["local-char-idx"].min().reset_index()[["segment-idx", "subsegment-idx", "word-idx"]].astype(int)
-    segments_arr = {}
-    segments_arr["start"] = per_subseg_grp["start"].min().reset_index()["start"]
-    segments_arr["end"] = per_subseg_grp["end"].max().reset_index()["end"]
-    segments_arr = pd.DataFrame(segments_arr)
-    segments_arr[["segment-idx", "subsegment-idx-start"]] = per_subseg_grp["start"].min().reset_index()[["segment-idx", "subsegment-idx"]]
-    segments_arr["subsegment-idx-end"] = segments_arr["subsegment-idx-start"] + 1
-
-    # interpolate missing words / sub-segments
-    if interpolate_method != "ignore":
-        wrd_subseg_grp = word_segments_arr.groupby(["segment-idx", "subsegment-idx"], group_keys=False)
-        wrd_seg_grp = word_segments_arr.groupby(["segment-idx"], group_keys=False)
-        # we still know which word timestamps are interpolated because their score == nan
-        word_segments_arr["start"] = wrd_subseg_grp['start'].apply(lambda group: interpolate_nans(group, method=interpolate_method))
-        word_segments_arr["end"] = wrd_subseg_grp['end'].apply(lambda group: interpolate_nans(group, method=interpolate_method))
-
-        word_segments_arr["start"] = wrd_seg_grp['start'].apply(lambda group: interpolate_nans(group, method=interpolate_method))
-        word_segments_arr["end"] = wrd_seg_grp['end'].apply(lambda group: interpolate_nans(group, method=interpolate_method))
-
-        sub_seg_grp =  segments_arr.groupby(["segment-idx"], group_keys=False)
-        segments_arr['start'] = sub_seg_grp['start'].apply(lambda group: interpolate_nans(group, method=interpolate_method))
-        segments_arr['end'] = sub_seg_grp['end'].apply(lambda group: interpolate_nans(group, method=interpolate_method))
-
-        # merge words & subsegments which are missing times
-        word_grp = word_segments_arr.groupby(["segment-idx", "subsegment-idx", "end"])
-
-        word_segments_arr["segment-text-start"] = word_grp["segment-text-start"].transform(min)
-        word_segments_arr["segment-text-end"] = word_grp["segment-text-end"].transform(max)
-        word_segments_arr.drop_duplicates(subset=["segment-idx", "subsegment-idx", "end"], inplace=True)
-
-        seg_grp_dup = segments_arr.groupby(["segment-idx", "start", "end"])
-        segments_arr["subsegment-idx-start"] = seg_grp_dup["subsegment-idx-start"].transform(min)
-        segments_arr["subsegment-idx-end"] = seg_grp_dup["subsegment-idx-end"].transform(max)
-        segments_arr.drop_duplicates(subset=["segment-idx", "subsegment-idx-start", "subsegment-idx-end"], inplace=True)
-    else:
-        word_segments_arr.dropna(inplace=True)
-        segments_arr.dropna(inplace=True)
-
-    # if some segments still have missing timestamps (usually because all numerals / symbols), then use original timestamps...
-    segments_arr['start'].fillna(pd.Series([x['start'] for x in transcript]), inplace=True)
-    segments_arr['end'].fillna(pd.Series([x['end'] for x in transcript]), inplace=True)
-    segments_arr['subsegment-idx-start'].fillna(0, inplace=True)
-    segments_arr['subsegment-idx-end'].fillna(1, inplace=True)
+        clean_wdx = []
+        for wdx, wrd in enumerate(per_word):
+            if any([c in model_dictionary.keys() for c in wrd]):
+                clean_wdx.append(wdx)
 
+        sentence_spans = list(nltk.tokenize.punkt.PunktSentenceTokenizer().span_tokenize(text))
 
+        segment["clean_char"] = clean_char
+        segment["clean_cdx"] = clean_cdx
+        segment["clean_wdx"] = clean_wdx
+        segment["sentence_spans"] = sentence_spans
+    
     aligned_segments = []
-    aligned_segments_word = []
 
-    word_segments_arr.set_index(["segment-idx", "subsegment-idx"], inplace=True)
-    char_segments_arr.set_index(["segment-idx", "subsegment-idx", "word-idx"], inplace=True)
+    # 2. Get prediction matrix from alignment model & align
+    for sdx, segment in enumerate(transcript):
+        t1 = segment["start"]
+        t2 = segment["end"]
+        text = segment["text"]
 
-    for sdx, srow in segments_arr.iterrows():
+        aligned_seg = {
+            "start": t1,
+            "end": t2,
+            "text": text,
+            "words": [],
+        }
 
-        seg_idx = int(srow["segment-idx"])
-        sub_start = int(srow["subsegment-idx-start"])
-        sub_end = int(srow["subsegment-idx-end"])
+        if return_char_alignments:
+            aligned_seg["chars"] = []
 
-        seg = transcript[seg_idx]
-        text = "".join(seg["seg-text"][sub_start:sub_end])
+        # check we can align
+        if len(segment["clean_char"]) == 0:
+            print(f'Failed to align segment ("{segment["text"]}"): no characters in this segment found in model dictionary, resorting to original...')
+            aligned_segments.append(aligned_seg)
+            continue
 
-        wseg = word_segments_arr.loc[seg_idx].loc[sub_start:sub_end-1]
-        wseg["start"].fillna(srow["start"], inplace=True)
-        wseg["end"].fillna(srow["end"], inplace=True)
-        wseg["segment-text-start"].fillna(0, inplace=True)
-        wseg["segment-text-end"].fillna(len(text)-1, inplace=True)
+        if t1 >= MAX_DURATION or t2 - t1 < 0.02:
+            print("Failed to align segment: original start time longer than audio duration, skipping...")
+            aligned_segments.append(aligned_seg)
+            continue
 
-        cseg = char_segments_arr.loc[seg_idx].loc[sub_start:sub_end-1]
-        # fixes bug for single segment in transcript
-        cseg['segment-text-start'] = cseg['level_1'] if 'level_1' in cseg else 0
-        cseg['segment-text-end'] = cseg['level_1'] + 1 if 'level_1' in cseg else 1
-        if 'level_1' in cseg: del cseg['level_1']
-        if 'level_0' in cseg: del cseg['level_0']
-        cseg.reset_index(inplace=True)
+        text_clean = "".join(segment["clean_char"])
+        tokens = [model_dictionary[c] for c in text_clean]
 
-        def get_raw_text(word_row):
-            return seg["seg-text"][word_row.name][int(word_row["segment-text-start"]):int(word_row["segment-text-end"])+1]
+        f1 = int(t1 * SAMPLE_RATE)
+        f2 = int(t2 * SAMPLE_RATE)
 
-        word_list = []
-        wdx = 0
-        curr_text = get_raw_text(wseg.iloc[wdx])
-        if not curr_text.startswith(" "):
-            curr_text = " " + curr_text
+        # TODO: Probably can get some speedup gain with batched inference here
+        waveform_segment = audio[:, f1:f2]
+
+        with torch.inference_mode():
+            if model_type == "torchaudio":
+                emissions, _ = model(waveform_segment.to(device))
+            elif model_type == "huggingface":
+                emissions = model(waveform_segment.to(device)).logits
+            else:
+                raise NotImplementedError(f"Align model of type {model_type} not supported.")
+            emissions = torch.log_softmax(emissions, dim=-1)
+
+        emission = emissions[0].cpu().detach()
+
+        blank_id = 0
+        for char, code in model_dictionary.items():
+            if char == '[pad]' or char == '<pad>':
+                blank_id = code
+
+        trellis = get_trellis(emission, tokens, blank_id)
+        path = backtrack(trellis, emission, tokens, blank_id)
+
+        if path is None:
+            print(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original...')
+            aligned_segments.append(aligned_seg)
+            continue
+
+        char_segments = merge_repeats(path, text_clean)
+
+        duration = t2 -t1
+        ratio = duration * waveform_segment.size(0) / (trellis.size(0) - 1)
+
+        # assign timestamps to aligned characters
+        char_segments_arr = []
+        word_idx = 0
+        for cdx, char in enumerate(text):
+            start, end, score = None, None, None
+            if cdx in segment["clean_cdx"]:
+                char_seg = char_segments[segment["clean_cdx"].index(cdx)]
+                start = round(char_seg.start * ratio + t1, 3)
+                end = round(char_seg.end * ratio + t1, 3)
+                score = round(char_seg.score, 3)
+
+            char_segments_arr.append(
+                {
+                    "char": char,
+                    "start": start,
+                    "end": end,
+                    "score": score,
+                    "word-idx": word_idx,
+                }
+            )
+
+            # increment word_idx, nltk word tokenization would probably be more robust here, but us space for now...
+            if model_lang in LANGUAGES_WITHOUT_SPACES:
+                word_idx += 1
+            elif cdx == len(text) - 1 or text[cdx+1] == " ":
+                word_idx += 1
             
-        if len(wseg) > 1:
-            for _, wrow in wseg.iloc[1:].iterrows():
-                if wrow['start'] != wseg.iloc[wdx]['start']:
-                    word_start = wseg.iloc[wdx]['start']
-                    word_end = wseg.iloc[wdx]['end']
+        char_segments_arr = pd.DataFrame(char_segments_arr)
 
-                    aligned_segments_word.append(
-                        {
-                            "text": curr_text.strip(),
-                            "start": word_start,
-                            "end": word_end
-                        }
-                    )
+        aligned_subsegments = []
+        # assign sentence_idx to each character index
+        char_segments_arr["sentence-idx"] = None
+        for sdx, (sstart, send) in enumerate(segment["sentence_spans"]):
+            curr_chars = char_segments_arr.loc[(char_segments_arr.index >= sstart) & (char_segments_arr.index <= send)]
+            char_segments_arr.loc[(char_segments_arr.index >= sstart) & (char_segments_arr.index <= send), "sentence-idx"] = sdx
+        
+            sentence_text = text[sstart:send]
+            sentence_start = curr_chars["start"].min()
+            sentence_end = curr_chars["end"].max()
+            sentence_words = []
 
-                    word_list.append(
-                        {
-                            "word": curr_text.rstrip(),
-                            "start": word_start,
-                            "end": word_end,
-                        }
-                    )
+            for word_idx in curr_chars["word-idx"].unique():
+                word_chars = curr_chars.loc[curr_chars["word-idx"] == word_idx]
+                word_text = "".join(word_chars["char"].tolist()).strip()
+                if len(word_text) == 0:
+                    continue
+                word_start = word_chars["start"].min()
+                word_end = word_chars["end"].max()
+                word_score = round(word_chars["score"].mean(), 3)
 
-                    curr_text = " "
-                curr_text += get_raw_text(wrow) + " "
-                wdx += 1
+                # -1 indicates unalignable 
+                word_segment = {"word": word_text}
 
-        aligned_segments_word.append(
-            {
-                "text": curr_text.strip(),
-                "start": wseg.iloc[wdx]["start"],
-                "end": wseg.iloc[wdx]["end"]
-            }
-        )
+                if not np.isnan(word_start):
+                    word_segment["start"] = word_start
+                if not np.isnan(word_end):
+                    word_segment["end"] = word_end
+                if not np.isnan(word_score):
+                    word_segment["score"] = word_score
 
-        word_list.append(
-            {
-                "word": curr_text.rstrip(),
-                "start": wseg.iloc[wdx]['start'],
-                "end": wseg.iloc[wdx]['end'],
-            }
-        )
+                sentence_words.append(word_segment)
+            
+            aligned_subsegments.append({
+                "text": sentence_text,
+                "start": sentence_start,
+                "end": sentence_end,
+                "words": sentence_words,
+            })
 
-        aligned_segments.append(
-            {
-                "start": srow["start"],
-                "end": srow["end"],
-                "text": text,
-                "words": word_list,
-                "word-segments": wseg,
-                "char-segments": cseg
-            }
-        )
-    
-    
-    return {"segments": aligned_segments, "word_segments": aligned_segments_word}
+            if return_char_alignments:
+                curr_chars = curr_chars[["char", "start", "end", "score"]]
+                curr_chars.fillna(-1, inplace=True)
+                curr_chars = curr_chars.to_dict("records")
+                curr_chars = [{key: val for key, val in char.items() if val != -1} for char in curr_chars]
 
+        aligned_subsegments = pd.DataFrame(aligned_subsegments)
+        aligned_subsegments["start"] = interpolate_nans(aligned_subsegments["start"], method=interpolate_method)
+        aligned_subsegments["end"] = interpolate_nans(aligned_subsegments["end"], method=interpolate_method)
+        # concatenate sentences with same timestamps
+        agg_dict = {"text": " ".join, "words": "sum"}
+        if return_char_alignments:
+            agg_dict["chars"] = "sum"
+        aligned_subsegments= aligned_subsegments.groupby(["start", "end"], as_index=False).agg(agg_dict)
+        aligned_subsegments = aligned_subsegments.to_dict('records')
+        aligned_segments += aligned_subsegments
+
+    # create word_segments list
+    word_segments = []
+    for segment in aligned_segments:
+        word_segments += segment["words"]
+
+    return {"segments": aligned_segments, "word_segments": word_segments}
 
 """
 source: https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html
diff --git a/whisperx/asr.py b/whisperx/asr.py
index ba6220b..f2c54f6 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -78,7 +78,7 @@ def load_model(whisper_arch, device, compute_type="float16", asr_options=None, l
 class WhisperModel(faster_whisper.WhisperModel):
     '''
     FasterWhisperModel provides batched inference for faster-whisper.
-    Currently only works in non-timestamp mode.
+    Currently only works in non-timestamp mode and fixed prompt for all samples in batch.
     '''
 
     def generate_segment_batched(self, features: np.ndarray, tokenizer: faster_whisper.tokenizer.Tokenizer, options: faster_whisper.transcribe.TranscriptionOptions, encoder_output = None):
@@ -140,6 +140,13 @@ class WhisperModel(faster_whisper.WhisperModel):
         return self.model.encode(features, to_cpu=to_cpu)
     
 class FasterWhisperPipeline(Pipeline):
+    """
+    Huggingface Pipeline wrapper for FasterWhisperModel.
+    """
+    # TODO:
+    # - add support for timestamp mode
+    # - add support for custom inference kwargs
+
     def __init__(
             self,
             model,
@@ -261,149 +268,3 @@ class FasterWhisperPipeline(Pipeline):
         language = language_token[2:-2]
         print(f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio...")
         return language
-
-if __name__ == "__main__":
-    main_type = "simple"
-    import time
-
-    import jiwer
-    from tqdm import tqdm
-    from whisper.normalizers import EnglishTextNormalizer
-
-    from benchmark.tedlium import parse_tedlium_annos
-
-    if main_type == "complex":
-        from faster_whisper.tokenizer import Tokenizer
-        from faster_whisper.transcribe import TranscriptionOptions
-        from faster_whisper.vad import (SpeechTimestampsMap,
-                                        get_speech_timestamps)
-
-        from whisperx.vad import load_vad_model, merge_chunks
-
-        from .audio import SAMPLE_RATE, load_audio, log_mel_spectrogram
-        faster_t_options = TranscriptionOptions(
-        beam_size=5,
-        best_of=5,
-        patience=1,
-        length_penalty=1,
-        temperatures=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0],
-        compression_ratio_threshold=2.4,
-        log_prob_threshold=-1.0,
-        no_speech_threshold=0.6,
-        condition_on_previous_text=False,
-        initial_prompt=None,
-        prefix=None,
-        suppress_blank=True,
-        suppress_tokens=[-1],
-        without_timestamps=True,
-        max_initial_timestamp=0.0,
-        word_timestamps=False,
-        prepend_punctuations="\"'“¿([{-",
-        append_punctuations="\"'.。,，!！?？:：”)]}、"
-    )
-        whisper_arch = "large-v2"
-        device = "cuda"
-        batch_size = 16
-        model = WhisperModel(whisper_arch, device="cuda", compute_type="float16",)
-        tokenizer = Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task="transcribe", language="en")
-        model = FasterWhisperPipeline(model, tokenizer, faster_t_options, device=-1)
-        fn = "DanielKahneman_2010.wav"
-        wav_dir = f"/tmp/test/wav/"
-        vad_model = load_vad_model("cuda", 0.6, 0.3)
-        audio = load_audio(os.path.join(wav_dir, fn))
-        vad_segments = vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
-        vad_segments = merge_chunks(vad_segments, 30)
-
-        def data(audio, segments):
-            for seg in segments:
-                f1 = int(seg['start'] * SAMPLE_RATE)
-                f2 = int(seg['end'] * SAMPLE_RATE)
-                # print(f2-f1)
-                yield {'inputs': audio[f1:f2]}
-        vad_method="pyannote"
-
-        wav_dir = f"/tmp/test/wav/"
-        wer_li = []
-        time_li = []
-        for fn in os.listdir(wav_dir):
-            if fn == "RobertGupta_2010U.wav":
-                continue
-            base_fn = fn.split('.')[0]
-            audio_fp = os.path.join(wav_dir, fn)
-
-            audio = load_audio(audio_fp)
-            t1 = time.time()
-            if vad_method == "pyannote":
-                vad_segments = vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
-                vad_segments = merge_chunks(vad_segments, 30)
-            elif vad_method == "silero":
-                vad_segments = get_speech_timestamps(audio, threshold=0.5, max_speech_duration_s=30)
-                vad_segments = [{"start": x["start"] / SAMPLE_RATE, "end": x["end"] / SAMPLE_RATE} for x in vad_segments]
-                new_segs = []
-                curr_start = vad_segments[0]['start']
-                curr_end = vad_segments[0]['end']
-                for seg in vad_segments[1:]:
-                    if seg['end'] - curr_start > 30:
-                        new_segs.append({"start": curr_start, "end": curr_end})
-                        curr_start = seg['start']
-                        curr_end = seg['end']
-                    else:
-                        curr_end = seg['end']
-                new_segs.append({"start": curr_start, "end": curr_end})
-                vad_segments = new_segs
-            text = []
-            # for idx, out in tqdm(enumerate(model(data(audio_fp, vad_segments), batch_size=batch_size)), total=len(vad_segments)):
-            for idx, out in enumerate(model(data(audio, vad_segments), batch_size=batch_size)):
-                text.append(out['text'])
-            t2 = time.time()
-            if batch_size == 1:
-                text = [x[0] for x in text]
-            text = " ".join(text)
-
-            normalizer = EnglishTextNormalizer()
-            text = normalizer(text)
-            gt_corpus = normalizer(parse_tedlium_annos(base_fn, "/tmp/test/"))
-
-            wer_result = jiwer.wer(gt_corpus, text)
-            print("WER: %.2f \t time: %.2f \t [%s]" % (wer_result * 100, t2-t1, fn))
-
-            wer_li.append(wer_result)
-            time_li.append(t2-t1)
-        print("# Avg Mean...")
-        print("WER: %.2f" % (sum(wer_li) * 100/len(wer_li)))
-        print("Time: %.2f" % (sum(time_li)/len(time_li)))
-    elif main_type == "simple":
-        model = load_model(
-            "large-v2",
-            device="cuda",
-            language="en",
-        )
-
-        wav_dir = f"/tmp/test/wav/"
-        wer_li = []
-        time_li = []
-        for fn in os.listdir(wav_dir):
-            if fn == "RobertGupta_2010U.wav":
-                continue
-            # fn = "DanielKahneman_2010.wav"
-            base_fn = fn.split('.')[0]
-            audio_fp = os.path.join(wav_dir, fn)
-
-            audio = load_audio(audio_fp)
-            t1 = time.time()
-            out = model.transcribe(audio_fp, batch_size=8)["segments"]
-            t2 = time.time()
-
-            text = " ".join([x['text'] for x in out])
-            normalizer = EnglishTextNormalizer()
-            text = normalizer(text)
-            gt_corpus = normalizer(parse_tedlium_annos(base_fn, "/tmp/test/"))
-
-            wer_result = jiwer.wer(gt_corpus, text)
-            print("WER: %.2f \t time: %.2f \t [%s]" % (wer_result * 100, t2-t1, fn))
-
-            wer_li.append(wer_result)
-            time_li.append(t2-t1)
-        print("# Avg Mean...")
-        print("WER: %.2f" % (sum(wer_li) * 100/len(wer_li)))
-        print("Time: %.2f" % (sum(time_li)/len(time_li)))
diff --git a/whisperx/diarize.py b/whisperx/diarize.py
index 93ff41d..320d2a4 100644
--- a/whisperx/diarize.py
+++ b/whisperx/diarize.py
@@ -11,7 +11,6 @@ class DiarizationPipeline:
         use_auth_token=None,
         device: Optional[Union[str, torch.device]] = "cpu",
     ):
-        self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token)
         if isinstance(device, str):
             device = torch.device(device)
         self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token).to(device)
@@ -21,59 +20,44 @@ class DiarizationPipeline:
         diarize_df = pd.DataFrame(segments.itertracks(yield_label=True))
         diarize_df['start'] = diarize_df[0].apply(lambda x: x.start)
         diarize_df['end'] = diarize_df[0].apply(lambda x: x.end)
+        diarize_df.rename(columns={2: "speaker"}, inplace=True)
         return diarize_df
 
-def assign_word_speakers(diarize_df, result_segments, fill_nearest=False):
-    for seg in result_segments:
-        wdf = seg['word-segments']
-        if len(wdf['start'].dropna()) == 0:
-            wdf['start'] = seg['start']
-            wdf['end'] = seg['end']
-        speakers = []
-        for wdx, wrow in wdf.iterrows():
-            if not np.isnan(wrow['start']):
-                diarize_df['intersection'] = np.minimum(diarize_df['end'], wrow['end']) - np.maximum(diarize_df['start'], wrow['start'])
-                diarize_df['union'] = np.maximum(diarize_df['end'], wrow['end']) - np.minimum(diarize_df['start'], wrow['start'])
-                # remove no hit
-                if not fill_nearest:
-                    dia_tmp = diarize_df[diarize_df['intersection'] > 0]
-                else:
-                    dia_tmp = diarize_df
-                if len(dia_tmp) == 0:
-                    speaker = None
-                else:
-                    speaker = dia_tmp.sort_values("intersection", ascending=False).iloc[0][2]
-            else:
-                speaker = None
-            speakers.append(speaker)
-        seg['word-segments']['speaker'] = speakers
 
-        speaker_count = pd.Series(speakers).value_counts()
-        if len(speaker_count) == 0:
-            seg["speaker"]= "UNKNOWN"
+def assign_word_speakers(diarize_df, transcript_result, fill_nearest=False):
+    transcript_segments = transcript_result["segments"]
+    for seg in transcript_segments:
+        # assign speaker to segment (if any)
+        diarize_df['intersection'] = np.minimum(diarize_df['end'], seg['end']) - np.maximum(diarize_df['start'], seg['start'])
+        diarize_df['union'] = np.maximum(diarize_df['end'], seg['end']) - np.minimum(diarize_df['start'], seg['start'])
+        # remove no hit, otherwise we look for closest (even negative intersection...)
+        if not fill_nearest:
+            dia_tmp = diarize_df[diarize_df['intersection'] > 0]
         else:
-            seg["speaker"] = speaker_count.index[0]
+            dia_tmp = diarize_df
+        if len(dia_tmp) > 0:
+            # sum over speakers
+            speaker = dia_tmp.groupby("speaker")["intersection"].sum().sort_values(ascending=False).index[0]
+            seg["speaker"] = speaker
+        
+        # assign speaker to words
+        if 'words' in seg:
+            for word in seg['words']:
+                if 'start' in word:
+                    diarize_df['intersection'] = np.minimum(diarize_df['end'], word['end']) - np.maximum(diarize_df['start'], word['start'])
+                    diarize_df['union'] = np.maximum(diarize_df['end'], word['end']) - np.minimum(diarize_df['start'], word['start'])
+                    # remove no hit
+                    if not fill_nearest:
+                        dia_tmp = diarize_df[diarize_df['intersection'] > 0]
+                    else:
+                        dia_tmp = diarize_df
+                    if len(dia_tmp) > 0:
+                        # sum over speakers
+                        speaker = dia_tmp.groupby("speaker")["intersection"].sum().sort_values(ascending=False).index[0]
+                        word["speaker"] = speaker
+        
+    return transcript_result            
 
-    # create word level segments for .srt
-    word_seg = []
-    for seg in result_segments:
-        wseg = pd.DataFrame(seg["word-segments"])
-        for wdx, wrow in wseg.iterrows():
-            if wrow["start"] is not None:
-                speaker = wrow['speaker']
-                if speaker is None or speaker == np.nan:
-                    speaker = "UNKNOWN"
-                word_seg.append(
-                    {
-                        "start": wrow["start"],
-                        "end": wrow["end"],
-                        "text": f"[{speaker}]: " + seg["text"][int(wrow["segment-text-start"]):int(wrow["segment-text-end"])]
-                    }
-                )
-
-    # TODO: create segments but split words on new speaker
-
-    return result_segments, word_seg
 
 class Segment:
     def __init__(self, start, end, speaker=None):
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index f3f63fe..b89a545 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -64,14 +64,11 @@ def cli():
     parser.add_argument("--max_line_width", type=optional_int, default=None, help="(not possible with --no_align) the maximum number of characters in a line before breaking the line")
     parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --no_align) the maximum number of lines in a segment")
     parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
+    parser.add_argument("--segment_resolution", type=str, default="sentence", choices=["sentence", "chunk"], help="(not possible with --no_align) the maximum number of characters in a line before breaking the line")
 
-    # parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
-    # parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
-    # parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
 
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
-    # parser.add_argument("--model_flush", action="store_true", help="Flush memory from each model after use, reduces GPU requirement but slower processing >1 audio file.")
     # fmt: on
 
     args = parser.parse_args().__dict__
@@ -97,7 +94,6 @@ def cli():
     min_speakers: int = args.pop("min_speakers")
     max_speakers: int = args.pop("max_speakers")
 
-    # TODO: check model loading works.
 
     if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
         if args["language"] is not None:
@@ -176,6 +172,7 @@ def cli():
                     align_model, align_metadata = load_align_model(result["language"], device)
                 print(">>Performing alignment...")
                 result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method)
+
             results.append((result, audio_path))
 
         # Unload align model
@@ -193,18 +190,10 @@ def cli():
         diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device)
         for result, input_audio_path in tmp_results:
             diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
-            results_segments, word_segments = assign_word_speakers(diarize_segments, result["segments"])
-            result = {"segments": results_segments, "word_segments": word_segments}
+            result = assign_word_speakers(diarize_segments, result)
             results.append((result, input_audio_path))
-
     # >> Write
     for result, audio_path in results:
-        # Remove pandas dataframes from result so that
-        # we can serialize the result with json
-        for seg in result["segments"]:
-            seg.pop("word-segments", None)
-            seg.pop("char-segments", None)
-
         writer(result, audio_path, writer_args)
 
 if __name__ == "__main__":
diff --git a/whisperx/utils.py b/whisperx/utils.py
index 3401a84..d042bb7 100644
--- a/whisperx/utils.py
+++ b/whisperx/utils.py
@@ -231,11 +231,16 @@ class SubtitlesWriter(ResultWriter):
             line_count = 1
             # the next subtitle to yield (a list of word timings with whitespace)
             subtitle: list[dict] = []
-            last = result["segments"][0]["words"][0]["start"]
+            times = []
+            last = result["segments"][0]["start"]
             for segment in result["segments"]:
                 for i, original_timing in enumerate(segment["words"]):
                     timing = original_timing.copy()
-                    long_pause = not preserve_segments and timing["start"] - last > 3.0
+                    long_pause = not preserve_segments
+                    if "start" in timing:
+                        long_pause = long_pause and timing["start"] - last > 3.0
+                    else:
+                        long_pause = False
                     has_room = line_len + len(timing["word"]) <= max_line_width
                     seg_break = i == 0 and len(subtitle) > 0 and preserve_segments
                     if line_len > 0 and has_room and not long_pause and not seg_break:
@@ -251,8 +256,9 @@ class SubtitlesWriter(ResultWriter):
                             or seg_break
                         ):
                             # subtitle break
-                            yield subtitle
+                            yield subtitle, times
                             subtitle = []
+                            times = []
                             line_count = 1
                         elif line_len > 0:
                             # line break
@@ -260,40 +266,53 @@ class SubtitlesWriter(ResultWriter):
                             timing["word"] = "\n" + timing["word"]
                         line_len = len(timing["word"].strip())
                     subtitle.append(timing)
-                    last = timing["start"]
+                    times.append((segment["start"], segment["end"], segment.get("speaker")))
+                    if "start" in timing:
+                        last = timing["start"]
             if len(subtitle) > 0:
-                yield subtitle
+                yield subtitle, times
 
         if "words" in result["segments"][0]:
-            for subtitle in iterate_subtitles():
-                subtitle_start = self.format_timestamp(subtitle[0]["start"])
-                subtitle_end = self.format_timestamp(subtitle[-1]["end"])
-                subtitle_text = "".join([word["word"] for word in subtitle])
-                if highlight_words:
+            for subtitle, _ in iterate_subtitles():
+                sstart, ssend, speaker = _[0]
+                subtitle_start = self.format_timestamp(sstart)
+                subtitle_end = self.format_timestamp(ssend)
+                subtitle_text = " ".join([word["word"] for word in subtitle])
+                has_timing = any(["start" in word for word in subtitle])
+
+                # add [$SPEAKER_ID]: to each subtitle if speaker is available
+                prefix = ""
+                if speaker is not None:
+                    prefix = f"[{speaker}]: "
+
+                if highlight_words and has_timing:
                     last = subtitle_start
                     all_words = [timing["word"] for timing in subtitle]
                     for i, this_word in enumerate(subtitle):
-                        start = self.format_timestamp(this_word["start"])
-                        end = self.format_timestamp(this_word["end"])
-                        if last != start:
-                            yield last, start, subtitle_text
+                        if "start" in this_word:
+                            start = self.format_timestamp(this_word["start"])
+                            end = self.format_timestamp(this_word["end"])
+                            if last != start:
+                                yield last, start, subtitle_text
 
-                        yield start, end, "".join(
-                            [
-                                re.sub(r"^(\s*)(.*)$", r"\1<u>\2</u>", word)
-                                if j == i
-                                else word
-                                for j, word in enumerate(all_words)
-                            ]
-                        )
-                        last = end
+                            yield start, end, prefix + " ".join(
+                                [
+                                    re.sub(r"^(\s*)(.*)$", r"\1<u>\2</u>", word)
+                                    if j == i
+                                    else word
+                                    for j, word in enumerate(all_words)
+                                ]
+                            )
+                            last = end
                 else:
-                    yield subtitle_start, subtitle_end, subtitle_text
+                    yield subtitle_start, subtitle_end, prefix + subtitle_text
         else:
             for segment in result["segments"]:
                 segment_start = self.format_timestamp(segment["start"])
                 segment_end = self.format_timestamp(segment["end"])
                 segment_text = segment["text"].strip().replace("-->", "->")
+                if "speaker" in segment:
+                    segment_text = f"[{segment['speaker']}]: {segment_text}"
                 yield segment_start, segment_end, segment_text
 
     def format_timestamp(self, seconds: float):

From 4603f010a5cdb93717e747ec092e0b7fa38877d2 Mon Sep 17 00:00:00 2001
From: Max Bain <maxbain@robots.ox.ac.uk>
Date: Sun, 7 May 2023 20:28:33 +0100
Subject: [PATCH 43/48] update readme, setup, add option to return
 char_timestamps

---
 README.md              | 154 +++++++++++++++++++++++++----------------
 setup.py               |   2 +-
 whisperx/__init__.py   |   3 +-
 whisperx/alignment.py  |   1 +
 whisperx/transcribe.py |   8 ++-
 5 files changed, 103 insertions(+), 65 deletions(-)

diff --git a/README.md b/README.md
index 1f41bb9..bccbca8 100644
--- a/README.md
+++ b/README.md
@@ -13,36 +13,36 @@
         <img src="https://img.shields.io/github/license/m-bain/whisperX.svg"
              alt="GitHub license">
   </a>
+  <a href="https://arxiv.org/abs/2303.00747">
+        <img src="http://img.shields.io/badge/Arxiv-2303.00747-B31B1B.svg"
+             alt="ArXiv paper">
+  </a>
   <a href="https://twitter.com/intent/tweet?text=&url=https%3A%2F%2Fgithub.com%2Fm-bain%2FwhisperX">
   <img src="https://img.shields.io/twitter/url/https/github.com/m-bain/whisperX.svg?style=social" alt="Twitter">
   </a>      
 </p>
 
-<p align="center">
-  <a href="#what-is-it">What is it</a> •
-  <a href="#setup">Setup</a> •
-  <a href="#example">Usage</a> •
-  <a href="#other-languages">Multilingual</a> •
-  <a href="#contribute">Contribute</a> •
-  <a href="EXAMPLES.md">More examples</a> •
-  <a href="https://arxiv.org/abs/2303.00747">Paper</a>
-</p>
-
 
 <img width="1216" align="center" alt="whisperx-arch" src="figures/pipeline.png">
 
 
-<p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy + quality via forced phoneme alignment and speech-activity batching.
-
-</p>
+<!-- <p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy + quality via forced phoneme alignment and voice-activity based batching for fast inference.</p> -->
 
 
-<h2 align="left", id="what-is-it">What is it 🔎</h2>
-
-This repository refines the timestamps of openAI's Whisper model via forced aligment with phoneme-based ASR models (e.g. wav2vec2.0) and VAD preprocesssing, multilingual use-case.
+<!-- <h2 align="left", id="what-is-it">What is it 🔎</h2> -->
 
 
-**Whisper** is an ASR model [developed by OpenAI](https://github.com/openai/whisper), trained on a large dataset of diverse audio. Whilst it does produces highly accurate transcriptions, the corresponding timestamps are at the utterance-level, not per word, and can be inaccurate by several seconds.
+This repository provides fast automatic speaker recognition (70x realtime with large-v2) with word-level timestamps and speaker diarization.
+
+- ⚡️ Batched inference for 70x realtime transcription using whisper large-v2
+- 🪶 [faster-whisper](https://github.com/guillaumekln/faster-whisper) backend, requires <8GB gpu memory for large-v2 with beam_size=5
+- 🎯 Accurate word-level timestamps using wav2vec2 alignment
+- 👯‍♂️ Multispeaker ASR using speaker diarization from [pyannote-audio](https://github.com/pyannote/pyannote-audio) (labels each segment/word with speaker ID) 
+- 🗣️ VAD preprocessing, reduces hallucination & batching with no WER degradation
+
+
+
+**Whisper** is an ASR model [developed by OpenAI](https://github.com/openai/whisper), trained on a large dataset of diverse audio. Whilst it does produces highly accurate transcriptions, the corresponding timestamps are at the utterance-level, not per word, and can be inaccurate by several seconds. OpenAI's whisper does not natively support batching.
 
 **Phoneme-Based ASR** A suite of models finetuned to recognise the smallest unit of speech distinguishing one word from another, e.g. the element p in "tap". A popular example model is [wav2vec2.0](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self).
 
@@ -50,15 +50,15 @@ This repository refines the timestamps of openAI's Whisper model via forced alig
 
 **Voice Activity Detection (VAD)** is the detection of the presence or absence of human speech.
 
+**Speaker Diarization** is the process of partitioning an audio stream containing human speech into homogeneous segments according to the identity of each speaker.
+
+
 <h2 align="left", id="highlights">New🚨</h2>
 
+- v3 transcript segment-per-sentence: using nltk sent_tokenize for better subtitlting & better diarization
 - v3 released, 70x speed-up open-sourced. Using batched whisper with [faster-whisper](https://github.com/guillaumekln/faster-whisper) backend!
-- v2 released, code cleanup, imports whisper library, batched inference from paper not included (contact for licensing / batched model API). VAD filtering is now turned on by default, as in the paper.
-- Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed (not provided in this repo).
-- VAD filtering: Voice Activity Detection (VAD) from [Pyannote.audio](https://huggingface.co/pyannote/voice-activity-detection) is used as a preprocessing step to remove reliance on whisper timestamps and only transcribe audio segments containing speech. add `--vad_filter True` flag, increases timestamp accuracy and robustness (requires more GPU mem due to 30s inputs in wav2vec2)
-- Character level timestamps (see `*.char.ass` file output)
-- Diarization (still in beta, add `--diarize`)
-
+- v2 released, code cleanup, imports whisper library VAD filtering is now turned on by default, as in the paper.
+- Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed.
 
 <h2 align="left" id="setup">Setup ⚙️</h2>
 Tested for PyTorch 2.0, Python 3.10 (use other versions at your own risk!)
@@ -89,15 +89,13 @@ If already installed, update package to most recent commit
 
 If wishing to modify this package, clone and install in editable mode:
 ```
-$ git clone https://github.com/m-bain/whisperX.git@v3
+$ git clone https://github.com/m-bain/whisperX.git
 $ cd whisperX
-$ git checkout v3
 $ pip install -e .
 ```
 
 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
 
-
 ### Speaker Diarization
 To **enable Speaker. Diarization**, include your Hugging Face access token that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the following models: [Segmentation](https://huggingface.co/pyannote/segmentation) , [Voice Activity Detection (VAD)](https://huggingface.co/pyannote/voice-activity-detection) , and [Speaker Diarization](https://huggingface.co/pyannote/speaker-diarization)
 
@@ -106,15 +104,11 @@ To **enable Speaker. Diarization**, include your Hugging Face access token that
 
 ### English
 
-Run whisper on example segment (using default params)
+Run whisper on example segment (using default params, whisper small) add `--highlight_words True` to visualise word timings in the .srt file.
 
     whisperx examples/sample01.wav
 
 
-For increased timestamp accuracy, at the cost of higher gpu mem, use bigger models (bigger alignment model not found to be that helpful, see paper) e.g.
-
-    whisperx examples/sample01.wav --model large-v2 --align_model WAV2VEC2_ASR_LARGE_LV60K_960H
-
 Result using *WhisperX* with forced alignment to wav2vec2.0 large:
 
 https://user-images.githubusercontent.com/36994049/208253969-7e35fe2a-7541-434a-ae91-8e919540555d.mp4
@@ -123,6 +117,16 @@ Compare this to original whisper out the box, where many transcriptions are out
 
 https://user-images.githubusercontent.com/36994049/207743923-b4f0d537-29ae-4be2-b404-bb941db73652.mov
 
+
+For increased timestamp accuracy, at the cost of higher gpu mem, use bigger models (bigger alignment model not found to be that helpful, see paper) e.g.
+
+    whisperx examples/sample01.wav --model large-v2 --align_model WAV2VEC2_ASR_LARGE_LV60K_960H --batch_size 4
+
+
+To label the transcript with speaker ID's (set number of speakers if known e.g. `--min_speakers 2` `--max_speakers 2`):
+
+    whisperx examples/sample01.wav --model large-v2 --diarize --highlight_words True
+
 ### Other languages
 
 The phoneme ASR alignment model is *language-specific*, for tested languages these models are [automatically picked from torchaudio pipelines or huggingface](https://github.com/m-bain/whisperX/blob/e909f2f766b23b2000f2d95df41f9b844ac53e49/whisperx/transcribe.py#L22).
@@ -132,7 +136,7 @@ Currently default models provided for `{en, fr, de, es, it, ja, zh, nl, uk, pt}`
 
 
 #### E.g. German
-    whisperx --model large --language de examples/sample_de_01.wav
+    whisperx --model large-v2 --language de examples/sample_de_01.wav
 
 https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-97d4-0aebd07e0eb3.mov
 
@@ -143,79 +147,107 @@ See more examples in other languages [here](EXAMPLES.md).
 
 ```python
 import whisperx
+import gc 
 
 device = "cuda" 
 audio_file = "audio.mp3"
+batch_size = 16 # reduce if low on GPU mem
+compute_type = "float16" # change to "int8" if low on GPU mem (may reduce accuracy)
 
-# transcribe with original whisper
-model = whisperx.load_model("large-v2", device)
+# 1. Transcribe with original whisper (batched)
+model = whisperx.load_model("large-v2", device, compute_type=compute_type)
 
 audio = whisperx.load_audio(audio_file)
-result = model.transcribe(audio, batch_size=8)
-
+result = model.transcribe(audio, batch_size=batch_size)
 print(result["segments"]) # before alignment
 
-# load alignment model and metadata
+# delete model if low on GPU resources
+# import gc; gc.collect(); torch.cuda.empty_cache(); del model
+
+# 2. Align whisper output
 model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
+result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)
 
-# align whisper output
-result_aligned = whisperx.align(result["segments"], model_a, metadata, audio, device)
+print(result["segments"]) # after alignment
 
-print(result_aligned["segments"]) # after alignment
-print(result_aligned["word_segments"]) # after alignment
+# delete model if low on GPU resources
+# import gc; gc.collect(); torch.cuda.empty_cache(); del model_a
+
+# 3. Assign speaker labels
+diarize_model = whisperx.DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device)
+
+# add min/max number of speakers if known
+diarize_segments = diarize_model(input_audio_path)
+# diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
+
+result = assign_word_speakers(diarize_segments, result)
+print(diarize_segments)
+print(result["segments"]) # segments are now assigned speaker IDs
 ```
 
 
-<h2 align="left" id="whisper-mod">Whisper Modifications</h2>
+<h2 align="left" id="whisper-mod">Technical Details 👷‍♂️</h2>
 
-In addition to forced alignment, the following two modifications have been made to the whisper transcription method:
+For specific details on the batching and alignment, the effect of VAD, as well as the chosen alignment model, see the preprint [paper](https://www.robots.ox.ac.uk/~vgg/publications/2023/Bain23/bain23.pdf).
 
-1. `--condition_on_prev_text` is set to `False` by default (reduces hallucination)
+To reduce GPU memory requirements, try any of the following (2. & 3. can affect quality):
+1.  reduce batch size, e.g. `--batch_size 4`
+2. use a smaller ASR model `--model base`
+3. Use lighter compute type `--compute_type int8`
+
+Transcription differences from openai's whisper:
+1. Transcription without timestamps. To enable single pass batching, whisper inference is performed `--without_timestamps True`, this ensures 1 forward pass per sample in the batch. However, this can cause discrepancies the default whisper output.
+2. VAD-based segment transcription, unlike the buffered transcription of openai's. In Wthe WhisperX paper we show this reduces WER, and enables accurate batched inference
+3.  `--condition_on_prev_text` is set to `False` by default (reduces hallucination)
 
 <h2 align="left" id="limitations">Limitations ⚠️</h2>
 
-- Whisper normalises spoken numbers e.g. "fifty seven" to arabic numerals "57". Need to perform this normalization after alignment, so the phonemes can be aligned. Currently just ignores numbers.
-- If setting `--vad_filter False`, then whisperx assumes the initial whisper timestamps are accurate to some degree (within margin of 2 seconds, adjust if needed -- bigger margins more prone to alignment errors)
+- Transcript words which do not contain characters in the alignment models dictionary e.g. "2014." or "£13.60" cannot be aligned and therefore are not given a timing.
 - Overlapping speech is not handled particularly well by whisper nor whisperx
-- Diariazation is far from perfect.
+- Diarization is far from perfect (working on this with custom model v4 -- see contact me).
+- Language specific wav2vec2 model is needed
 
 
 <h2 align="left" id="contribute">Contribute 🧑‍🏫</h2>
 
-If you are multilingual, a major way you can contribute to this project is to find phoneme models on huggingface (or train your own) and test them on speech for the target language. If the results look good send a merge request and some examples showing its success.
+If you are multilingual, a major way you can contribute to this project is to find phoneme models on huggingface (or train your own) and test them on speech for the target language. If the results look good send a pull request and some examples showing its success.
 
-The next major upgrade we are working on is whisper with speaker diarization, so if you have any experience on this please share.
+Bug finding and pull requests are also highly appreciated to keep this project going, since it's already diverging from the original research scope.
 
-<h2 align="left" id="coming-soon">Coming Soon 🗓</h2>
+<h2 align="left" id="coming-soon">TODO 🗓</h2>
 
 * [x] Multilingual init
 
-* [x] Subtitle .ass output
-
 * [x] Automatic align model selection based on language detection
 
 * [x] Python usage
 
-* [x] Character level timestamps
-
 * [x] Incorporating  speaker diarization
 
 * [x] Model flush, for low gpu mem resources
 
 * [x] Faster-whisper backend
 
+* [x] Add max-line etc. see (openai's whisper utils.py)
+
+* [x] Sentence-level segments (nltk toolbox)
+
+* [x] Improve alignment logic
+
+* [ ] update examples with diarization and word highlighting
+
+* [ ] Subtitle .ass output <- bring this back (removed in v3)
+
 * [ ] Add benchmarking code (TEDLIUM for spd/WER & word segmentation)
 
 * [ ] Allow silero-vad as alternative VAD option
 
-* [ ] Add max-line etc. see (openai's whisper utils.py)
-
 * [ ] Improve diarization (word level). *Harder than first thought...*
 
 
 <h2 align="left" id="contact">Contact/Support 📇</h2>
 
-Contact maxhbain@gmail.com for queries and licensing / early access to a model API with batched inference (transcribe 1hr audio in under 1min).
+Contact maxhbain@gmail.com for queries. WhisperX v4 development is underway with with siginificantly improved diarization. To support v4 and get early access, get in touch.
 
 <a href="https://www.buymeacoffee.com/maxhbain" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
 
@@ -224,14 +256,16 @@ Contact maxhbain@gmail.com for queries and licensing / early access to a model A
 
 This work, and my PhD, is supported by the [VGG (Visual Geometry Group)](https://www.robots.ox.ac.uk/~vgg/) and the University of Oxford.
 
-
 Of course, this is builds on [openAI's whisper](https://github.com/openai/whisper).
 And borrows important alignment code from [PyTorch tutorial on forced alignment](https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html)
 
-Valuable VAD & Diarization Models from (pyannote.audio)[https://github.com/pyannote/pyannote-audio]
+Valuable VAD & Diarization Models from [pyannote audio][https://github.com/pyannote/pyannote-audio]
 
-Great backend from (faster-whisper)[https://github.com/guillaumekln/faster-whisper] and (CTranslate2)[https://github.com/OpenNMT/CTranslate2]
+Great backend from [faster-whisper](https://github.com/guillaumekln/faster-whisper) and [CTranslate2](https://github.com/OpenNMT/CTranslate2)
 
+Those who have [supported this work financially](https://www.buymeacoffee.com/maxhbain) 🙏
+
+Finally, thanks to the OS [contributors](https://github.com/m-bain/whisperX/graphs/contributors) of this project, keeping it going and identifying bugs.
 
 <h2 align="left" id="cite">Citation</h2>
 If you use this in your research, please cite the paper:
diff --git a/setup.py b/setup.py
index 66f22cd..eea26ad 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ from setuptools import setup, find_packages
 setup(
     name="whisperx",
     py_modules=["whisperx"],
-    version="3.0.2",
+    version="3.1.0",
     description="Time-Accurate Automatic Speech Recognition using Whisper.",
     readme="README.md",
     python_requires=">=3.8",
diff --git a/whisperx/__init__.py b/whisperx/__init__.py
index d0294b9..20abaae 100644
--- a/whisperx/__init__.py
+++ b/whisperx/__init__.py
@@ -1,3 +1,4 @@
 from .transcribe import load_model
 from .alignment import load_align_model, align
-from .audio import load_audio
\ No newline at end of file
+from .audio import load_audio
+from .diarize import assign_word_speakers, DiarizationPipeline
\ No newline at end of file
diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index 2812c10..b873475 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -287,6 +287,7 @@ def align(
                 curr_chars.fillna(-1, inplace=True)
                 curr_chars = curr_chars.to_dict("records")
                 curr_chars = [{key: val for key, val in char.items() if val != -1} for char in curr_chars]
+                aligned_subsegments[-1]["chars"] = curr_chars
 
         aligned_subsegments = pd.DataFrame(aligned_subsegments)
         aligned_subsegments["start"] = interpolate_nans(aligned_subsegments["start"], method=interpolate_method)
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index b89a545..d09c5f6 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -35,6 +35,7 @@ def cli():
     parser.add_argument("--align_model", default=None, help="Name of phoneme-level ASR model to do alignment")
     parser.add_argument("--interpolate_method", default="nearest", choices=["nearest", "linear", "ignore"], help="For word .srt, method to assign timestamps to non-aligned words, or merge them into neighbouring.")
     parser.add_argument("--no_align", action='store_true', help="Do not perform phoneme alignment")
+    parser.add_argument("--return_char_alignments", action='store_true', help="Return character-level alignments in the output json file")
 
     # vad params
     parser.add_argument("--vad_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected")
@@ -42,8 +43,8 @@ def cli():
 
     # diarization params
     parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word")
-    parser.add_argument("--min_speakers", default=None, type=int)
-    parser.add_argument("--max_speakers", default=None, type=int)
+    parser.add_argument("--min_speakers", default=None, type=int, help="Minimum number of speakers to in audio file")
+    parser.add_argument("--max_speakers", default=None, type=int, help="Maximum number of speakers to in audio file")
 
     parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
     parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
@@ -85,6 +86,7 @@ def cli():
     align_model: str = args.pop("align_model")
     interpolate_method: str = args.pop("interpolate_method")
     no_align: bool = args.pop("no_align")
+    return_char_alignments: bool = args.pop("return_char_alignments")
 
     hf_token: str = args.pop("hf_token")
     vad_onset: float = args.pop("vad_onset")
@@ -171,7 +173,7 @@ def cli():
                     print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
                     align_model, align_metadata = load_align_model(result["language"], device)
                 print(">>Performing alignment...")
-                result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method)
+                result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method, return_char_alignments=return_char_alignments)
 
             results.append((result, audio_path))
 

From 0b839f3f019d6b71773c07fe2f1d671091f4420f Mon Sep 17 00:00:00 2001
From: Max Bain <36994049+m-bain@users.noreply.github.com>
Date: Sun, 7 May 2023 20:36:08 +0100
Subject: [PATCH 44/48] Update README.md

---
 README.md | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/README.md b/README.md
index ae3d5bd..a6d400c 100644
--- a/README.md
+++ b/README.md
@@ -52,13 +52,6 @@ This repository provides fast automatic speaker recognition (70x realtime with l
 
 **Speaker Diarization** is the process of partitioning an audio stream containing human speech into homogeneous segments according to the identity of each speaker.
 
-- v3 pre-release [this branch](https://github.com/m-bain/whisperX/tree/v3) *70x speed-up open-sourced. Using batched whisper with faster-whisper backend*!
-- v2 released, code cleanup, imports whisper library. VAD filtering is now turned on by default, as in the paper.
-- Paper drop🎓👨‍🏫! Please see our [ArxiV preprint](https://arxiv.org/abs/2303.00747) for benchmarking and details of WhisperX. We also introduce more efficient batch inference resulting in large-v2 with *60-70x REAL TIME speed (not provided in this repo).
-- VAD filtering: Voice Activity Detection (VAD) from [Pyannote.audio](https://huggingface.co/pyannote/voice-activity-detection) is used as a preprocessing step to remove reliance on whisper timestamps and only transcribe audio segments containing speech. add `--vad_filter True` flag, increases timestamp accuracy and robustness (requires more GPU mem due to 30s inputs in wav2vec2)
-- Character level timestamps (see `*.char.ass` file output)
-- Diarization (still in beta, add `--diarize`)
-
 <h2 align="left", id="highlights">New🚨</h2>
 
 - v3 transcript segment-per-sentence: using nltk sent_tokenize for better subtitlting & better diarization

From 2efa136114a3ad677a76c8e7b9e75008b3bb4f60 Mon Sep 17 00:00:00 2001
From: Max Bain <36994049+m-bain@users.noreply.github.com>
Date: Mon, 8 May 2023 17:20:38 +0100
Subject: [PATCH 45/48] update python usage example

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index a6d400c..9d61d9b 100644
--- a/README.md
+++ b/README.md
@@ -176,10 +176,10 @@ print(result["segments"]) # after alignment
 diarize_model = whisperx.DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device)
 
 # add min/max number of speakers if known
-diarize_segments = diarize_model(input_audio_path)
-# diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
+diarize_segments = diarize_model(audio_file)
+# diarize_model(audio_file, min_speakers=min_speakers, max_speakers=max_speakers)
 
-result = assign_word_speakers(diarize_segments, result)
+result = whisperx.assign_word_speakers(diarize_segments, result)
 print(diarize_segments)
 print(result["segments"]) # segments are now assigned speaker IDs
 ```

From b50aafb17b286a162e5bf08bd71eb820d0df396d Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Mon, 8 May 2023 20:03:42 +0200
Subject: [PATCH 46/48] Fix tuple unpacking

---
 whisperx/asr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/whisperx/asr.py b/whisperx/asr.py
index f2c54f6..21357ec 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -245,7 +245,7 @@ class FasterWhisperPipeline(Pipeline):
                 text = text[0]
             segments.append(
                 {
-                    "text": out['text'],
+                    "text": text,
                     "start": round(vad_segments[idx]['start'], 3),
                     "end": round(vad_segments[idx]['end'], 3)
                 }

From eabf35dff0d80ff3cabc946b65d2faf42797e671 Mon Sep 17 00:00:00 2001
From: Simon <simon.sorg@student.hpi.de>
Date: Mon, 8 May 2023 20:45:34 +0200
Subject: [PATCH 47/48] Custom result types

---
 whisperx/alignment.py | 13 +++++-----
 whisperx/asr.py       |  6 ++---
 whisperx/types.py     | 58 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 68 insertions(+), 9 deletions(-)
 create mode 100644 whisperx/types.py

diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index b873475..eb8d4b6 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -3,7 +3,7 @@ Forced Alignment with Whisper
 C. Max Bain
 """
 from dataclasses import dataclass
-from typing import Iterator, Union
+from typing import Iterator, Union, List
 
 import numpy as np
 import pandas as pd
@@ -13,6 +13,7 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
 from .audio import SAMPLE_RATE, load_audio
 from .utils import interpolate_nans
+from .types import AlignedTranscriptionResult, SingleSegment, SingleAlignedSegment, SingleWordSegment
 import nltk
 
 LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]
@@ -80,14 +81,14 @@ def load_align_model(language_code, device, model_name=None, model_dir=None):
 
 
 def align(
-    transcript: Iterator[dict],
+    transcript: Iterator[SingleSegment],
     model: torch.nn.Module,
     align_model_metadata: dict,
     audio: Union[str, np.ndarray, torch.Tensor],
     device: str,
     interpolate_method: str = "nearest",
     return_char_alignments: bool = False,
-):
+) -> AlignedTranscriptionResult:
     """
     Align phoneme recognition predictions to known transcription.
     """
@@ -146,7 +147,7 @@ def align(
         segment["clean_wdx"] = clean_wdx
         segment["sentence_spans"] = sentence_spans
     
-    aligned_segments = []
+    aligned_segments: List[SingleAlignedSegment] = []
 
     # 2. Get prediction matrix from alignment model & align
     for sdx, segment in enumerate(transcript):
@@ -154,7 +155,7 @@ def align(
         t2 = segment["end"]
         text = segment["text"]
 
-        aligned_seg = {
+        aligned_seg: SingleAlignedSegment = {
             "start": t1,
             "end": t2,
             "text": text,
@@ -301,7 +302,7 @@ def align(
         aligned_segments += aligned_subsegments
 
     # create word_segments list
-    word_segments = []
+    word_segments: List[SingleWordSegment] = []
     for segment in aligned_segments:
         word_segments += segment["words"]
 
diff --git a/whisperx/asr.py b/whisperx/asr.py
index 21357ec..e131ae1 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -11,7 +11,7 @@ from transformers.pipelines.pt_utils import PipelineIterator
 
 from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
 from .vad import load_vad_model, merge_chunks
-
+from .types import TranscriptionResult, SingleSegment
 
 def load_model(whisper_arch, device, compute_type="float16", asr_options=None, language=None,
                vad_options=None, model=None):
@@ -215,7 +215,7 @@ class FasterWhisperPipeline(Pipeline):
 
     def transcribe(
         self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0
-    ):
+    ) -> TranscriptionResult:
         if isinstance(audio, str):
             audio = load_audio(audio)
         
@@ -237,7 +237,7 @@ class FasterWhisperPipeline(Pipeline):
         else:
             language = self.tokenizer.language_code
 
-        segments = []
+        segments: List[SingleSegment] = []
         batch_size = batch_size or self._batch_size
         for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size, num_workers=num_workers)):
             text = out['text']
diff --git a/whisperx/types.py b/whisperx/types.py
new file mode 100644
index 0000000..75d4485
--- /dev/null
+++ b/whisperx/types.py
@@ -0,0 +1,58 @@
+from typing import TypedDict, Optional
+
+
+class SingleWordSegment(TypedDict):
+    """
+    A single word of a speech.
+    """
+    word: str
+    start: float
+    end: float
+    score: float
+
+class SingleCharSegment(TypedDict):
+    """
+    A single char of a speech.
+    """
+    char: str
+    start: float
+    end: float
+    score: float
+
+
+class SingleSegment(TypedDict):
+    """
+    A single segment (up to multiple sentences) of a speech.
+    """
+
+    start: float
+    end: float
+    text: str
+
+
+class SingleAlignedSegment(TypedDict):
+    """
+    A single segment (up to multiple sentences) of a speech with word alignment.
+    """
+
+    start: float
+    end: float
+    text: str
+    words: list[SingleWordSegment]
+    chars: Optional[list[SingleCharSegment]]
+
+
+class TranscriptionResult(TypedDict):
+    """
+    A list of segments and word segments of a speech.
+    """
+    segments: list[SingleSegment]
+    language: str
+
+
+class AlignedTranscriptionResult(TypedDict):
+    """
+    A list of segments and word segments of a speech.
+    """
+    segments: list[SingleAlignedSegment]
+    word_segments: list[SingleWordSegment]

From 5421f1d7ca5aef03fcb9a0a7c5bc415feffec6f8 Mon Sep 17 00:00:00 2001
From: Max Bain <36994049+m-bain@users.noreply.github.com>
Date: Tue, 9 May 2023 13:42:50 +0100
Subject: [PATCH 48/48] remove v3 tag on pip install

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 9d61d9b..943c482 100644
--- a/README.md
+++ b/README.md
@@ -80,11 +80,11 @@ See other methods [here.](https://pytorch.org/get-started/locally/)
 
 ### 3. Install this repo
 
-`pip install git+https://github.com/m-bain/whisperx.git@v3`
+`pip install git+https://github.com/m-bain/whisperx.git`
 
 If already installed, update package to most recent commit
 
-`pip install git+https://github.com/m-bain/whisperx.git@v3 --upgrade`
+`pip install git+https://github.com/m-bain/whisperx.git --upgrade`
 
 If wishing to modify this package, clone and install in editable mode:
 ```