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README.md
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@ -1,14 +1,42 @@
<h1 align="center">WhisperX</h1> <h1 align="center">WhisperX</h1>
<h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com/">https://www.maxbain.com/</a></h6> <p align="center">
<a href="https://github.com/m-bain/whisperX/stargazers">
<img src="https://img.shields.io/github/stars/m-bain/whisperX.svg?colorA=orange&colorB=orange&logo=github"
alt="GitHub stars">
</a>
<a href="https://github.com/m-bain/whisperX/issues">
<img src="https://img.shields.io/github/issues/m-bain/whisperx.svg"
alt="GitHub issues">
</a>
<a href="https://github.com/m-bain/whisperX/blob/master/LICENSE">
<img src="https://img.shields.io/github/license/m-bain/whisperX.svg"
alt="GitHub license">
</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">Example usage</a>
</p>
<img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/208313881-903ab3ea-4932-45fd-b3dc-70876cddaaa2.png">
<h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com">https://www.maxbain.com</a></h6>
<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 using forced alignment.
</p> </p>
<h2 align="left">What is it 🔎</h2> <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) 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.
**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. **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.
@ -17,40 +45,40 @@ 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. **Forced Alignment** refers to the process by which orthographic transcriptions are aligned to audio recordings to automatically generate phone level segmentation.
<img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/208313881-903ab3ea-4932-45fd-b3dc-70876cddaaa2.png"> <h2 align="left" id="setup">Setup ⚙️</h2>
<h2 align="left">Setup ⚙️</h2>
Install this package using Install this package using
`pip install git+https://github.com/m-bain/whisperx.git` `pip install git+https://github.com/m-bain/whisperx.git`
You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup. You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
<h2 align="left">Examples💬</h2> <h2 align="left" id="example">Example usage💬</h2>
### English ### English
Run whisper on example segment (using default params) Run whisper on example segment (using default params)
`whisperx examples/sample01.wav --model medium.en --output examples/whisperx --align_model WAV2VEC2_ASR_LARGE_LV60K_960H --align_extend 2` whisperx examples/sample01.wav
If low gpu memory is required, use a smaller align model e.g. `WAV2VEC2_ASR_BASE_LV60K_960H`
Using normal whisper out of the box, many transcriptions are out of sync: For increased timestamp accuracy, at the cost of higher gpu mem, use a bigger alignment model e.g.
https://user-images.githubusercontent.com/36994049/207743923-b4f0d537-29ae-4be2-b404-bb941db73652.mov whisperx examples/sample01.wav --model medium.en --align_model WAV2VEC2_ASR_LARGE_LV60K_960H --output_dir examples/whisperx
Now, using *WhisperX* with forced alignment to wav2vec2.0: Result using *WhisperX* with forced alignment to wav2vec2.0 large:
https://user-images.githubusercontent.com/36994049/208253969-7e35fe2a-7541-434a-ae91-8e919540555d.mp4 https://user-images.githubusercontent.com/36994049/208253969-7e35fe2a-7541-434a-ae91-8e919540555d.mp4
Compare this to original whisper out the box, where many transcriptions are out of sync:
https://user-images.githubusercontent.com/36994049/207743923-b4f0d537-29ae-4be2-b404-bb941db73652.mov
## Other Languages ## Other Languages
For non-english ASR, it is best to use the `large` whisper model. For non-english ASR, it is best to use the `large` whisper model.
### French ### French
`whisperx --model large --language fr examples/sample_fr_01.wav --align_model VOXPOPULI_ASR_BASE_10K_FR --output_dir examples/whisperx/ --align_extend 2` whisperx examples/sample_fr_01.wav --model large --language fr --align_model VOXPOPULI_ASR_BASE_10K_FR --output_dir examples/whisperx
https://user-images.githubusercontent.com/36994049/208298804-31c49d6f-6787-444e-a53f-e93c52706752.mov https://user-images.githubusercontent.com/36994049/208298804-31c49d6f-6787-444e-a53f-e93c52706752.mov
@ -58,8 +86,7 @@ https://user-images.githubusercontent.com/36994049/208298804-31c49d6f-6787-444e-
### German ### German
`whisperx --model large --language de examples/sample_de_01.wav --align_model VOXPOPULI_ASR_BASE_10K_DE --output_dir examples/whisperx/ --align_extend 2` whisperx examples/sample_de_01.wav --model large --language de --align_model VOXPOPULI_ASR_BASE_10K_DE --output_dir examples/whisperx
https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-97d4-0aebd07e0eb3.mov https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-97d4-0aebd07e0eb3.mov
@ -68,7 +95,7 @@ https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-
### Italian ### Italian
`whisperx --model large --language it examples/sample_it_01.wav --align_model VOXPOPULI_ASR_BASE_10K_IT --output_dir examples/whisperx/ --align_extend 2` whisperx examples/sample_it_01.wav --model large --language it --align_model VOXPOPULI_ASR_BASE_10K_IT --output_dir examples/whisperx
@ -78,17 +105,16 @@ https://user-images.githubusercontent.com/36994049/208298819-6f462b2c-8cae-4c54-
`whisperx --model large --language ja examples/sample_ja_01.wav --align_model jonatasgrosman/wav2vec2-large-xlsr-53-japanese --output_dir examples/whisperx --align_extend 2` `whisperx --model large --language ja examples/sample_ja_01.wav --align_model jonatasgrosman/wav2vec2-large-xlsr-53-japanese --output_dir examples/whisperx --align_extend 2`
https://user-images.githubusercontent.com/19920981/208448405-60f80c0e-2715-42d8-9437-e19e6362b638.mov https://user-images.githubusercontent.com/19920981/208448405-60f80c0e-2715-42d8-9437-e19e6362b638.mov
<h2 align="left">Limitations ⚠️</h2> <h2 align="left" id="limitations">Limitations ⚠️</h2>
- Not thoroughly tested, especially for non-english, results may vary -- please post issue to let me know its results on your data - 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. - 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.
- 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) - 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)
- Hacked this up quite quickly, there might be some errors, please raise an issue if you encounter any. - Hacked this up quite quickly, there might be some errors, please raise an issue if you encounter any.
<h2 align="left">Coming Soon 🗓</h2> <h2 align="left" id="coming-soon">Coming Soon 🗓</h2>
[x] Multilingual init [x] Multilingual init
@ -96,24 +122,23 @@ https://user-images.githubusercontent.com/19920981/208448405-60f80c0e-2715-42d8-
[ ] Automatic align model selection based on language detection [ ] Automatic align model selection based on language detection
[ ] Reduce GPU (clear cache etc.)
[ ] Incorporating word-level speaker diarization [ ] Incorporating word-level speaker diarization
[ ] Inference speedup with batch processing [ ] Inference speedup with batch processing
<h2 align="left">Contact</h2> <h2 align="left" id="contact">Contact</h2>
Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk if using this for commerical purposes. Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk if using this commerically.
<h2 align="left">Acknowledgements 🙏</h2> <h2 align="left" id="acks">Acknowledgements 🙏</h2>
Of course, this is mostly just a modification to [openAI's whisper](https://github.com/openai/whisper). Of course, this is mostly just a modification to [openAI's whisper](https://github.com/openai/whisper).
As well as accreditation to this [PyTorch tutorial on forced alignment](https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html) As well as accreditation to this [PyTorch tutorial on forced alignment](https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html)
<h2 align="left">Citation</h2> <h2 align="left" id="cite">Citation</h2>
If you use this in your research, just cite the repo, If you use this in your research, just cite the repo,
```bibtex ```bibtex

176
examples/whisperx/sample01.wav.word.srt
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@ -431,194 +431,194 @@ green
case. case.
109 109
00:00:38,095 --> 00:00:38,256 00:00:38,135 --> 00:00:38,255
Do Do
110 110
00:00:38,276 --> 00:00:38,356 00:00:38,275 --> 00:00:38,355
you you
111 111
00:00:38,376 --> 00:00:38,516 00:00:38,375 --> 00:00:38,535
want want
112 112
00:00:38,556 --> 00:00:38,736 00:00:38,555 --> 00:00:38,736
your your
113 113
00:00:38,877 --> 00:00:39,297 00:00:38,876 --> 00:00:39,296
PJs? PJs?
114 114
00:00:39,862 --> 00:00:40,185 00:00:39,879 --> 00:00:40,181
Yeah. Yeah.
115 115
00:00:42,394 --> 00:00:42,474 00:00:42,388 --> 00:00:42,689
Yeah.
116
00:00:42,474 --> 00:00:42,694
Lifting Lifting
117 116
00:00:42,714 --> 00:00:42,754 00:00:42,729 --> 00:00:42,749
a a
118 117
00:00:42,794 --> 00:00:43,095 00:00:42,809 --> 00:00:43,110
bundle bundle
119 118
00:00:43,135 --> 00:00:43,195 00:00:43,131 --> 00:00:43,191
of of
120 119
00:00:43,235 --> 00:00:43,776 00:00:43,251 --> 00:00:43,773
pajamas, pajamas,
121 120
00:00:44,076 --> 00:00:44,316 00:00:44,073 --> 00:00:44,314
Peter Peter
122 121
00:00:44,376 --> 00:00:44,637 00:00:44,374 --> 00:00:44,634
finds finds
123 122
00:00:44,677 --> 00:00:44,697 00:00:44,674 --> 00:00:44,694
a a
124 123
00:00:44,757 --> 00:00:44,957 00:00:44,754 --> 00:00:44,955
sheet sheet
125 124
00:00:44,997 --> 00:00:45,057 00:00:44,995 --> 00:00:45,055
of of
126 125
00:00:45,117 --> 00:00:45,418 00:00:45,115 --> 00:00:45,456
paper paper
127 126
00:00:45,538 --> 00:00:45,899 00:00:45,536 --> 00:00:45,876
labeled labeled
128 127
00:00:46,341 --> 00:00:47,043 00:00:46,338 --> 00:00:47,041
Lancaster Lancaster
129 128
00:00:47,124 --> 00:00:47,384 00:00:47,121 --> 00:00:47,382
North North
130 129
00:00:47,445 --> 00:00:47,946 00:00:47,442 --> 00:00:47,944
Hospital Hospital
131 130
00:00:48,267 --> 00:00:48,930 00:00:48,266 --> 00:00:48,928
discharge discharge
132 131
00:00:49,030 --> 00:00:49,251 00:00:49,029 --> 00:00:49,249
sheet. sheet.
133 132
00:00:50,293 --> 00:00:50,373 00:00:50,291 --> 00:00:50,371
He He
134 133
00:00:50,413 --> 00:00:50,774 00:00:50,412 --> 00:00:50,772
closes closes
135 134
00:00:50,814 --> 00:00:50,914 00:00:50,812 --> 00:00:50,912
the the
136 135
00:00:50,954 --> 00:00:51,395 00:00:50,953 --> 00:00:51,393
suitcase suitcase
137 136
00:00:51,435 --> 00:00:51,515 00:00:51,433 --> 00:00:51,514
and and
138 137
00:00:51,535 --> 00:00:51,796 00:00:51,534 --> 00:00:51,794
brings brings
139 138
00:00:51,836 --> 00:00:52,217 00:00:51,834 --> 00:00:52,235
Gloria Gloria
140 139
00:00:52,257 --> 00:00:52,317 00:00:52,255 --> 00:00:52,315
the the
141 140
00:00:52,357 --> 00:00:52,858 00:00:52,355 --> 00:00:52,856
pajamas. pajamas.
142 141
00:00:54,187 --> 00:00:54,489 00:00:54,186 --> 00:00:54,488
There There
143 142
00:00:54,550 --> 00:00:54,771 00:00:54,549 --> 00:00:54,771
you you
144 143
00:00:54,791 --> 00:00:54,832 00:00:54,791 --> 00:00:54,831
go. go.
145 144
00:00:55,655 --> 00:00:55,755 00:00:55,654 --> 00:00:55,775
Thank Thank
146 145
00:00:55,775 --> 00:00:55,896 00:00:55,795 --> 00:00:55,895
you. you.
147 146
00:00:55,916 --> 00:00:55,956 00:00:55,895 --> 00:00:55,936
He He
148 147
00:00:55,976 --> 00:00:56,077 00:00:55,956 --> 00:00:56,097
picks picks
149 148
00:00:56,097 --> 00:00:56,198 00:00:56,117 --> 00:00:56,198
up up
150 149
00:00:56,218 --> 00:00:56,319 00:00:56,218 --> 00:00:56,319
the the
151 150
00:00:56,359 --> 00:00:56,742 00:00:56,359 --> 00:00:56,742
locket. locket.
152 151
00:00:57,124 --> 00:00:57,225 00:00:57,124 --> 00:00:57,225
He You
153 152
00:00:57,265 --> 00:00:57,466 00:00:57,265 --> 00:00:57,466
kept kept
154 153
00:00:57,547 --> 00:00:57,627 00:00:57,547 --> 00:00:57,627
it. it.
155 154
00:00:58,874 --> 00:00:58,995 00:00:58,874 --> 00:00:58,994
Oh, Oh,
156 155
00:00:59,678 --> 00:00:59,899 00:00:59,276 --> 00:00:59,578
cool. of
156
00:00:59,678 --> 00:00:59,960
course.

37
whisperx/transcribe.py
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@ -28,7 +28,7 @@ def transcribe(
compression_ratio_threshold: Optional[float] = 2.4, compression_ratio_threshold: Optional[float] = 2.4,
logprob_threshold: Optional[float] = -1.0, logprob_threshold: Optional[float] = -1.0,
no_speech_threshold: Optional[float] = 0.6, no_speech_threshold: Optional[float] = 0.6,
condition_on_previous_text: bool = True, condition_on_previous_text: bool = False, # turn off by default due to errors it causes
**decode_options, **decode_options,
): ):
""" """
@ -258,6 +258,7 @@ def align(
device: str, device: str,
extend_duration: float = 0.0, extend_duration: float = 0.0,
start_from_previous: bool = True, start_from_previous: bool = True,
drop_non_aligned_words: bool = False,
): ):
print("Performing alignment...") print("Performing alignment...")
if not torch.is_tensor(audio): if not torch.is_tensor(audio):
@ -270,6 +271,7 @@ def align(
MAX_DURATION = audio.shape[1] / SAMPLE_RATE MAX_DURATION = audio.shape[1] / SAMPLE_RATE
prev_t2 = 0 prev_t2 = 0
word_segments_list = []
for idx, segment in enumerate(transcript): for idx, segment in enumerate(transcript):
t1 = max(segment['start'] - extend_duration, 0) t1 = max(segment['start'] - extend_duration, 0)
t2 = min(segment['end'] + extend_duration, MAX_DURATION) t2 = min(segment['end'] + extend_duration, MAX_DURATION)
@ -319,8 +321,7 @@ def align(
segment['end'] = t2_actual segment['end'] = t2_actual
prev_t2 = segment['end'] prev_t2 = segment['end']
# for the .ass output
# merge missing words to previous, or merge with next word ahead if idx == 0
for x in range(len(t_local)): for x in range(len(t_local)):
curr_word = t_words[x] curr_word = t_words[x]
curr_timestamp = t_local[x] curr_timestamp = t_local[x]
@ -329,15 +330,29 @@ def align(
else: else:
segment['word-level'].append({"text": curr_word, "start": None, "end": None}) segment['word-level'].append({"text": curr_word, "start": None, "end": None})
# for per-word .srt ouput
# merge missing words to previous, or merge with next word ahead if idx == 0
for x in range(len(t_local)):
curr_word = t_words[x]
curr_timestamp = t_local[x]
if curr_timestamp is not None:
word_segments_list.append({"text": curr_word, "start": curr_timestamp[0], "end": curr_timestamp[1]})
elif not drop_non_aligned_words:
# then we merge
if x == 0:
t_words[x+1] = " ".join([curr_word, t_words[x+1]])
else:
word_segments_list[-1]['text'] += ' ' + curr_word
else: else:
# then we resort back to original whisper timestamps # then we resort back to original whisper timestamps
# segment['start] and segment['end'] are unchanged # segment['start] and segment['end'] are unchanged
prev_t2 = 0 prev_t2 = 0
segment['word-level'].append({"text": segment['text'], "start": segment['start'], "end":segment['end']}) segment['word-level'].append({"text": segment['text'], "start": segment['start'], "end":segment['end']})
word_segments_list.append({"text": segment['text'], "start": segment['start'], "end":segment['end']})
print(f"[{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}] {segment['text']}") print(f"[{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}] {segment['text']}")
return {"segments": transcript} return {"segments": transcript, "word_segments": word_segments_list}
def cli(): def cli():
from . import available_models from . import available_models
@ -348,9 +363,10 @@ 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("--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("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
# alignment params # alignment params
parser.add_argument("--align_model", default="WAV2VEC2_ASR_LARGE_LV60K_960H", help="Name of phoneme-level ASR model to do alignment") parser.add_argument("--align_model", default="WAV2VEC2_ASR_BASE_960H", 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_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("--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("--drop_non_aligned", action="store_true", help="For word .srt, whether to drop non aliged words, or merge them into neighbouring.")
parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
parser.add_argument("--output_type", default="srt", choices=['all', 'srt', 'vtt', 'txt'], help="directory to save the outputs") parser.add_argument("--output_type", default="srt", choices=['all', 'srt', 'vtt', 'txt'], help="directory to save the outputs")
@ -387,7 +403,7 @@ def cli():
align_model: str = args.pop("align_model") align_model: str = args.pop("align_model")
align_extend: float = args.pop("align_extend") align_extend: float = args.pop("align_extend")
align_from_prev: bool = args.pop("align_from_prev") align_from_prev: bool = args.pop("align_from_prev")
# align_interpolate_missing: bool = args.pop("align_interpolate_missing") drop_non_aligned: bool = args.pop("drop_non_aligned")
os.makedirs(output_dir, exist_ok=True) os.makedirs(output_dir, exist_ok=True)
@ -421,12 +437,13 @@ def cli():
labels = processor.tokenizer.get_vocab() labels = processor.tokenizer.get_vocab()
align_dictionary = processor.tokenizer.get_vocab() align_dictionary = processor.tokenizer.get_vocab()
else: else:
print(f'Align model "{align_model}" is not supported, choose from:\n {torchaudio.pipelines.__all__ + wa2vec2_models_on_hugginface}') print(f'Align model "{align_model}" is not supported, choose from:\n {torchaudio.pipelines.__all__ + wa2vec2_models_on_hugginface} \n\
See details here https://pytorch.org/audio/stable/pipelines.html#id14')
raise ValueError(f'Align model "{align_model}" not supported') raise ValueError(f'Align model "{align_model}" not supported')
for audio_path in args.pop("audio"): for audio_path in args.pop("audio"):
result = transcribe(model, audio_path, temperature=temperature, **args) result = transcribe(model, audio_path, temperature=temperature, **args)
result_aligned = align(result["segments"], result["language"], align_model, align_dictionary, audio_path, device, result_aligned = align(result["segments"], result["language"], align_model, align_dictionary, audio_path, device,
extend_duration=align_extend, start_from_previous=align_from_prev) extend_duration=align_extend, start_from_previous=align_from_prev, drop_non_aligned_words=drop_non_aligned)
audio_basename = os.path.basename(audio_path) audio_basename = os.path.basename(audio_path)
# save TXT # save TXT
@ -444,6 +461,10 @@ def cli():
with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt:
write_srt(result_aligned["segments"], file=srt) write_srt(result_aligned["segments"], file=srt)
# 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 # save ASS
with open(os.path.join(output_dir, audio_basename + ".ass"), "w", encoding="utf-8") as srt: with open(os.path.join(output_dir, audio_basename + ".ass"), "w", encoding="utf-8") as srt:
write_ass(result_aligned["segments"], file=srt) write_ass(result_aligned["segments"], file=srt)