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1
.github/FUNDING.yml
vendored
Normal file
1
.github/FUNDING.yml
vendored
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@ -0,0 +1 @@
|
||||
custom: https://www.buymeacoffee.com/maxhbain
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
## Other Languages
|
||||
|
||||
For non-english ASR, it is best to use the `large` whisper model. Alignment models are automatically picked by the chosen language from the default [lists](https://github.com/m-bain/whisperX/blob/e909f2f766b23b2000f2d95df41f9b844ac53e49/whisperx/transcribe.py#L22).
|
||||
For non-english ASR, it is best to use the `large` whisper model. Alignment models are automatically picked by the chosen language from the default [lists](https://github.com/m-bain/whisperX/blob/main/whisperx/alignment.py#L18).
|
||||
|
||||
Currently support default models tested for {en, fr, de, es, it, ja, zh, nl}
|
||||
|
||||
|
||||
153
README.md
153
README.md
@ -24,22 +24,22 @@
|
||||
<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="EXAMPLES.md">More examples</a> •
|
||||
<a href="https://arxiv.org/abs/2303.00747">Paper</a>
|
||||
</p>
|
||||
|
||||
<h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com">https://www.maxbain.com</a></h6>
|
||||
|
||||
<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.
|
||||
<p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy + quality via forced phoneme alignment and speech-activity batching.
|
||||
|
||||
</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), 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.
|
||||
@ -48,32 +48,60 @@ 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>
|
||||
|
||||
- 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)
|
||||
- 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 `--diarization`)
|
||||
- Diarization (still in beta, add `--diarize`)
|
||||
|
||||
|
||||
<h2 align="left" id="setup">Setup ⚙️</h2>
|
||||
Install this package using
|
||||
Tested for PyTorch 2.0, Python 3.10 (use other versions at your own risk!)
|
||||
|
||||
`pip install git+https://github.com/m-bain/whisperx.git`
|
||||
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 Python3.10 environment
|
||||
|
||||
`conda create --name whisperx python=3.10`
|
||||
|
||||
`conda activate whisperx`
|
||||
|
||||
|
||||
### 2. Install PyTorch2.0, e.g. for Linux and Windows CUDA11.7:
|
||||
|
||||
`pip3 install torch torchvision torchaudio`
|
||||
|
||||
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`
|
||||
|
||||
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 .
|
||||
```
|
||||
|
||||
|
||||
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)
|
||||
|
||||
|
||||
<h2 align="left" id="example">Usage 💬 (command line)</h2>
|
||||
|
||||
### English
|
||||
@ -83,9 +111,9 @@ 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.en --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:
|
||||
|
||||
@ -120,8 +148,10 @@ device = "cuda"
|
||||
audio_file = "audio.mp3"
|
||||
|
||||
# transcribe with original whisper
|
||||
model = whisperx.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
|
||||
|
||||
@ -129,7 +159,7 @@ 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
|
||||
@ -142,15 +172,12 @@ 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>
|
||||
|
||||
- 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.
|
||||
- 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.
|
||||
- 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.
|
||||
|
||||
|
||||
<h2 align="left" id="contribute">Contribute 🧑🏫</h2>
|
||||
@ -161,65 +188,59 @@ The next major upgrade we are working on is whisper with speaker diarization, so
|
||||
|
||||
<h2 align="left" id="coming-soon">Coming Soon 🗓</h2>
|
||||
|
||||
[x] ~~Multilingual init~~ done
|
||||
* [x] Multilingual init
|
||||
|
||||
[x] ~~Subtitle .ass output~~ done
|
||||
* [x] Subtitle .ass output
|
||||
|
||||
[x] ~~Automatic align model selection based on language detection~~ done
|
||||
* [x] Automatic align model selection based on language detection
|
||||
|
||||
[x] ~~Python usage~~ done
|
||||
* [x] Python usage
|
||||
|
||||
[x] ~~Character level timestamps~~
|
||||
* [x] Character level timestamps
|
||||
|
||||
[x] ~~Incorporating speaker diarization~~
|
||||
* [x] Incorporating speaker diarization
|
||||
|
||||
[ ] Improve diarization (word level)
|
||||
* [x] Model flush, for low gpu mem resources
|
||||
|
||||
[ ] Inference speedup with batch processing
|
||||
* [x] Faster-whisper backend
|
||||
|
||||
<h2 align="left" id="contact">Contact 📇</h2>
|
||||
* [ ] 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).
|
||||
|
||||
<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>
|
||||
|
||||
Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk for business things.
|
||||
|
||||
<h2 align="left" id="acks">Acknowledgements 🙏</h2>
|
||||
|
||||
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)
|
||||
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, just cite the repo,
|
||||
If you use this in your research, please cite the paper:
|
||||
|
||||
```bibtex
|
||||
@misc{bain2022whisperx,
|
||||
author = {Bain, Max},
|
||||
title = {WhisperX},
|
||||
year = {2022},
|
||||
publisher = {GitHub},
|
||||
journal = {GitHub repository},
|
||||
howpublished = {\url{https://github.com/m-bain/whisperX}},
|
||||
}
|
||||
```
|
||||
|
||||
as well as the whisper paper,
|
||||
|
||||
```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}
|
||||
}
|
||||
```
|
||||
and any alignment model used, e.g. wav2vec2.0.
|
||||
|
||||
```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}
|
||||
@article{bain2022whisperx,
|
||||
title={WhisperX: Time-Accurate Speech Transcription of Long-Form Audio},
|
||||
author={Bain, Max and Huh, Jaesung and Han, Tengda and Zisserman, Andrew},
|
||||
journal={arXiv preprint, arXiv:2303.00747},
|
||||
year={2023}
|
||||
}
|
||||
```
|
||||
|
||||
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@ -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.
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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.
|
||||
@ -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.
|
||||
|
||||
Binary file not shown.
@ -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}
|
||||
@ -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.
|
||||
|
||||
@ -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
|
||||
00:00:49,029 --> 00:00:49,249
|
||||
sheet.
|
||||
|
||||
132
|
||||
00:00:50,291 --> 00:00:50,371
|
||||
He
|
||||
|
||||
133
|
||||
00:00:50,412 --> 00:00:50,772
|
||||
closes
|
||||
|
||||
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
|
||||
00:00:52,255 --> 00:00:52,315
|
||||
the
|
||||
|
||||
140
|
||||
00:00:52,355 --> 00:00:52,856
|
||||
pajamas.
|
||||
|
||||
141
|
||||
00:00:54,186 --> 00:00:54,488
|
||||
There
|
||||
|
||||
142
|
||||
00:00:54,549 --> 00:00:54,771
|
||||
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
|
||||
00:00:59,276 --> 00:00:59,578
|
||||
of
|
||||
|
||||
156
|
||||
00:00:59,678 --> 00:00:59,960
|
||||
course.
|
||||
|
||||
@ -1,184 +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: 0,0:00:0.56,0:00:0.96,Default,,0,0,0,,{\1c&HFF00&\u1}Weinlein{\r} von Hammersmann
|
||||
Dialogue: 0,0:00:0.96,0:00:0.98,Default,,0,0,0,,Weinlein von Hammersmann
|
||||
Dialogue: 0,0:00:0.98,0:00:1.21,Default,,0,0,0,,Weinlein {\1c&HFF00&\u1}von{\r} Hammersmann
|
||||
Dialogue: 0,0:00:1.21,0:00:1.23,Default,,0,0,0,,Weinlein von Hammersmann
|
||||
Dialogue: 0,0:00:1.23,0:00:1.87,Default,,0,0,0,,Weinlein von {\1c&HFF00&\u1}Hammersmann{\r}
|
||||
Dialogue: 0,0:00:6.19,0:00:6.61,Default,,0,0,0,,{\1c&HFF00&\u1}Oberst{\r} Lande, es ist lange her
|
||||
Dialogue: 0,0:00:6.61,0:00:6.63,Default,,0,0,0,,Oberst Lande, es ist lange her
|
||||
Dialogue: 0,0:00:6.63,0:00:6.97,Default,,0,0,0,,Oberst {\1c&HFF00&\u1}Lande,{\r} es ist lange her
|
||||
Dialogue: 0,0:00:6.97,0:00:7.01,Default,,0,0,0,,Oberst Lande, es ist lange her
|
||||
Dialogue: 0,0:00:7.01,0:00:7.17,Default,,0,0,0,,Oberst Lande, {\1c&HFF00&\u1}es{\r} ist lange her
|
||||
Dialogue: 0,0:00:7.17,0:00:7.21,Default,,0,0,0,,Oberst Lande, es ist lange her
|
||||
Dialogue: 0,0:00:7.21,0:00:7.47,Default,,0,0,0,,Oberst Lande, es {\1c&HFF00&\u1}ist{\r} lange her
|
||||
Dialogue: 0,0:00:7.47,0:00:7.49,Default,,0,0,0,,Oberst Lande, es ist lange her
|
||||
Dialogue: 0,0:00:7.49,0:00:7.77,Default,,0,0,0,,Oberst Lande, es ist {\1c&HFF00&\u1}lange{\r} her
|
||||
Dialogue: 0,0:00:7.77,0:00:7.83,Default,,0,0,0,,Oberst Lande, es ist lange her
|
||||
Dialogue: 0,0:00:7.83,0:00:8.01,Default,,0,0,0,,Oberst Lande, es ist lange {\1c&HFF00&\u1}her{\r}
|
||||
Dialogue: 0,0:00:8.01,0:00:8.17,Default,,0,0,0,,{\1c&HFF00&\u1}Schneide{\r} ich wie eh und je
|
||||
Dialogue: 0,0:00:8.17,0:00:8.19,Default,,0,0,0,,Schneide ich wie eh und je
|
||||
Dialogue: 0,0:00:8.19,0:00:8.25,Default,,0,0,0,,Schneide {\1c&HFF00&\u1}ich{\r} wie eh und je
|
||||
Dialogue: 0,0:00:8.25,0:00:8.27,Default,,0,0,0,,Schneide ich wie eh und je
|
||||
Dialogue: 0,0:00:8.27,0:00:8.33,Default,,0,0,0,,Schneide ich {\1c&HFF00&\u1}wie{\r} eh und je
|
||||
Dialogue: 0,0:00:8.33,0:00:8.35,Default,,0,0,0,,Schneide ich wie eh und je
|
||||
Dialogue: 0,0:00:8.35,0:00:8.39,Default,,0,0,0,,Schneide ich wie {\1c&HFF00&\u1}eh{\r} und je
|
||||
Dialogue: 0,0:00:8.39,0:00:8.41,Default,,0,0,0,,Schneide ich wie eh und je
|
||||
Dialogue: 0,0:00:8.41,0:00:8.47,Default,,0,0,0,,Schneide ich wie eh {\1c&HFF00&\u1}und{\r} je
|
||||
Dialogue: 0,0:00:8.47,0:00:8.49,Default,,0,0,0,,Schneide ich wie eh und je
|
||||
Dialogue: 0,0:00:8.49,0:00:8.53,Default,,0,0,0,,Schneide ich wie eh und {\1c&HFF00&\u1}je{\r}
|
||||
Dialogue: 0,0:00:13.99,0:00:14.17,Default,,0,0,0,,{\1c&HFF00&\u1}Also{\r} was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.17,0:00:14.21,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.21,0:00:14.45,Default,,0,0,0,,Also {\1c&HFF00&\u1}was{\r} ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.45,0:00:14.47,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.47,0:00:14.59,Default,,0,0,0,,Also was {\1c&HFF00&\u1}ist{\r} mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.59,0:00:14.61,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.61,0:00:14.71,Default,,0,0,0,,Also was ist {\1c&HFF00&\u1}mit{\r} Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.71,0:00:14.75,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.75,0:00:14.91,Default,,0,0,0,,Also was ist mit {\1c&HFF00&\u1}Ihrem{\r} wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.91,0:00:14.93,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:14.93,0:00:15.55,Default,,0,0,0,,Also was ist mit Ihrem {\1c&HFF00&\u1}wunderschönen{\r} Bein geschehen?
|
||||
Dialogue: 0,0:00:15.55,0:00:15.59,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:15.59,0:00:15.81,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen {\1c&HFF00&\u1}Bein{\r} geschehen?
|
||||
Dialogue: 0,0:00:15.81,0:00:15.85,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein geschehen?
|
||||
Dialogue: 0,0:00:15.85,0:00:16.23,Default,,0,0,0,,Also was ist mit Ihrem wunderschönen Bein {\1c&HFF00&\u1}geschehen?{\r}
|
||||
Dialogue: 0,0:00:17.03,0:00:17.15,Default,,0,0,0,,{\1c&HFF00&\u1}Ein{\r} Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:17.15,0:00:17.17,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:17.17,0:00:17.74,Default,,0,0,0,,Ein {\1c&HFF00&\u1}Nebenprodukt{\r} der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:17.74,0:00:17.78,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:17.78,0:00:17.92,Default,,0,0,0,,Ein Nebenprodukt {\1c&HFF00&\u1}der{\r} Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:17.92,0:00:17.96,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:17.96,0:00:18.60,Default,,0,0,0,,Ein Nebenprodukt der {\1c&HFF00&\u1}Arschtritte,{\r} die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.60,0:00:18.64,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.64,0:00:18.70,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, {\1c&HFF00&\u1}die{\r} Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.70,0:00:18.74,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.74,0:00:18.82,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die {\1c&HFF00&\u1}Sie{\r} in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.82,0:00:18.86,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.86,0:00:18.92,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie {\1c&HFF00&\u1}in{\r} der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.92,0:00:18.94,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:18.94,0:00:19.02,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in {\1c&HFF00&\u1}der{\r} deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:19.02,0:00:19.04,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:19.04,0:00:19.32,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der {\1c&HFF00&\u1}deutschen{\r} Filmwelt ausleihen, zweifelsohne
|
||||
Dialogue: 0,0:00:19.32,0:00:19.36,Default,,0,0,0,,Ein Nebenprodukt der Arschtritte, die Sie in der deutschen Filmwelt ausleihen, zweifelsohne
|
||||
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}
|
||||
@ -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.
|
||||
|
||||
@ -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}
|
||||
@ -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.
|
||||
|
||||
@ -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.
|
||||
Dialogue: 0,0:00:5.41,0:00:5.61,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, {\1c&HFF00&\u1}questa{\r} gemma propria della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
|
||||
Dialogue: 0,0:00:5.61,0:00:5.79,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: 0,0:00:5.79,0:00:6.07,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.
|
||||
Dialogue: 0,0:00:6.07,0:00:6.13,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: 0,0:00:6.13,0:00:6.51,Default,,0,0,0,,Signore, è un piacere, gli amici della vedetta ammirata da tutti noi, questa gemma {\1c&HFF00&\u1}propria{\r} della nostra cultura, saranno naturalmente accolti sotto la mia protezione per la durata del loro soggiorno.
|
||||
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}
|
||||
@ -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
|
||||
|
||||
BIN
figures/pipeline.png
Normal file
BIN
figures/pipeline.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 120 KiB |
@ -1,9 +1,7 @@
|
||||
numpy
|
||||
torch
|
||||
torchaudio
|
||||
tqdm
|
||||
soundfile
|
||||
more-itertools
|
||||
transformers>=4.19.0
|
||||
torch==2.0.0
|
||||
torchaudio==2.0.1
|
||||
faster-whisper
|
||||
transformers
|
||||
ffmpeg-python==0.2.0
|
||||
pyannote.audio
|
||||
pandas
|
||||
setuptools==65.6.3
|
||||
6
setup.py
6
setup.py
@ -6,10 +6,10 @@ from setuptools import setup, find_packages
|
||||
setup(
|
||||
name="whisperx",
|
||||
py_modules=["whisperx"],
|
||||
version="1.0",
|
||||
version="3.0.2",
|
||||
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",
|
||||
@ -19,7 +19,7 @@ setup(
|
||||
for r in pkg_resources.parse_requirements(
|
||||
open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
|
||||
)
|
||||
],
|
||||
] + ["pyannote.audio @ git+https://github.com/pyannote/pyannote-audio@11b56a137a578db9335efc00298f6ec1932e6317"],
|
||||
entry_points = {
|
||||
'console_scripts': ['whisperx=whisperx.transcribe:cli'],
|
||||
},
|
||||
|
||||
BIN
tests/jfk.flac
BIN
tests/jfk.flac
Binary file not shown.
@ -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
|
||||
@ -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"
|
||||
)
|
||||
@ -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)
|
||||
@ -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
|
||||
@ -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, load_align_model, align, transcribe_with_vad
|
||||
|
||||
_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 .transcribe import load_model
|
||||
from .alignment import load_align_model, align
|
||||
from .audio import load_audio
|
||||
@ -1,9 +1,473 @@
|
||||
""""
|
||||
Forced Alignment with Whisper
|
||||
C. Max Bain
|
||||
"""
|
||||
from dataclasses import dataclass
|
||||
from typing import Iterator, Union
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
import torchaudio
|
||||
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
|
||||
|
||||
from .audio import SAMPLE_RATE, load_audio
|
||||
from .utils import interpolate_nans
|
||||
|
||||
LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]
|
||||
|
||||
DEFAULT_ALIGN_MODELS_TORCH = {
|
||||
"en": "WAV2VEC2_ASR_BASE_960H",
|
||||
"fr": "VOXPOPULI_ASR_BASE_10K_FR",
|
||||
"de": "VOXPOPULI_ASR_BASE_10K_DE",
|
||||
"es": "VOXPOPULI_ASR_BASE_10K_ES",
|
||||
"it": "VOXPOPULI_ASR_BASE_10K_IT",
|
||||
}
|
||||
|
||||
DEFAULT_ALIGN_MODELS_HF = {
|
||||
"ja": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
|
||||
"zh": "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn",
|
||||
"nl": "jonatasgrosman/wav2vec2-large-xlsr-53-dutch",
|
||||
"uk": "Yehor/wav2vec2-xls-r-300m-uk-with-small-lm",
|
||||
"pt": "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese",
|
||||
"ar": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic",
|
||||
"ru": "jonatasgrosman/wav2vec2-large-xlsr-53-russian",
|
||||
"pl": "jonatasgrosman/wav2vec2-large-xlsr-53-polish",
|
||||
"hu": "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian",
|
||||
"fi": "jonatasgrosman/wav2vec2-large-xlsr-53-finnish",
|
||||
"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",
|
||||
}
|
||||
|
||||
|
||||
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:
|
||||
model_name = DEFAULT_ALIGN_MODELS_TORCH[language_code]
|
||||
elif language_code in DEFAULT_ALIGN_MODELS_HF:
|
||||
model_name = DEFAULT_ALIGN_MODELS_HF[language_code]
|
||||
else:
|
||||
print(f"There is no default alignment model set for this language ({language_code}).\
|
||||
Please find a wav2vec2.0 model finetuned on this language in https://huggingface.co/models, then pass the model name in --align_model [MODEL_NAME]")
|
||||
raise ValueError(f"No default align-model for language: {language_code}")
|
||||
|
||||
if model_name in torchaudio.pipelines.__all__:
|
||||
pipeline_type = "torchaudio"
|
||||
bundle = torchaudio.pipelines.__dict__[model_name]
|
||||
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:
|
||||
try:
|
||||
processor = Wav2Vec2Processor.from_pretrained(model_name)
|
||||
align_model = Wav2Vec2ForCTC.from_pretrained(model_name)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print(f"Error loading model from huggingface, check https://huggingface.co/models for finetuned wav2vec2.0 models")
|
||||
raise ValueError(f'The chosen align_model "{model_name}" could not be found in huggingface (https://huggingface.co/models) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14)')
|
||||
pipeline_type = "huggingface"
|
||||
align_model = align_model.to(device)
|
||||
labels = processor.tokenizer.get_vocab()
|
||||
align_dictionary = {char.lower(): code for char,code in processor.tokenizer.get_vocab().items()}
|
||||
|
||||
align_metadata = {"language": language_code, "dictionary": align_dictionary, "type": pipeline_type}
|
||||
|
||||
return align_model, align_metadata
|
||||
|
||||
|
||||
def align(
|
||||
transcript: Iterator[dict],
|
||||
model: torch.nn.Module,
|
||||
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",
|
||||
):
|
||||
"""
|
||||
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.
|
||||
"""
|
||||
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": [],
|
||||
}
|
||||
|
||||
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())
|
||||
transcription = segment["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
|
||||
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)
|
||||
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
for sdx, srow in segments_arr.iterrows():
|
||||
|
||||
seg_idx = int(srow["segment-idx"])
|
||||
sub_start = int(srow["subsegment-idx-start"])
|
||||
sub_end = int(srow["subsegment-idx-end"])
|
||||
|
||||
seg = transcript[seg_idx]
|
||||
text = "".join(seg["seg-text"][sub_start:sub_end])
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
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": word_start,
|
||||
"end": word_end
|
||||
}
|
||||
)
|
||||
|
||||
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(),
|
||||
"start": wseg.iloc[wdx]["start"],
|
||||
"end": wseg.iloc[wdx]["end"]
|
||||
}
|
||||
)
|
||||
|
||||
word_list.append(
|
||||
{
|
||||
"word": curr_text.rstrip(),
|
||||
"start": wseg.iloc[wdx]['start'],
|
||||
"end": wseg.iloc[wdx]['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}
|
||||
|
||||
|
||||
"""
|
||||
source: https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html
|
||||
"""
|
||||
import torch
|
||||
from dataclasses import dataclass
|
||||
|
||||
def get_trellis(emission, tokens, blank_id=0):
|
||||
num_frame = emission.size(0)
|
||||
num_tokens = len(tokens)
|
||||
|
||||
409
whisperx/asr.py
Normal file
409
whisperx/asr.py
Normal file
@ -0,0 +1,409 @@
|
||||
import os
|
||||
import warnings
|
||||
from typing import List, Union
|
||||
|
||||
import ctranslate2
|
||||
import faster_whisper
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import Pipeline
|
||||
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
|
||||
|
||||
|
||||
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.
|
||||
'''
|
||||
|
||||
if whisper_arch.endswith(".en"):
|
||||
language = "en"
|
||||
|
||||
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:
|
||||
print("No language specified, language will be first be detected for each audio file (increases inference time).")
|
||||
tokenizer = None
|
||||
|
||||
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
|
||||
):
|
||||
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:
|
||||
self.device = torch.device(f"cuda:{device}")
|
||||
else:
|
||||
self.device = device
|
||||
|
||||
super(Pipeline, self).__init__()
|
||||
self.vad_model = vad
|
||||
|
||||
def _sanitize_parameters(self, **kwargs):
|
||||
preprocess_kwargs = {}
|
||||
if "tokenizer" in kwargs:
|
||||
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
|
||||
return preprocess_kwargs, {}, {}
|
||||
|
||||
def preprocess(self, audio):
|
||||
audio = audio['inputs']
|
||||
features = log_mel_spectrogram(audio, padding=N_SAMPLES - audio.shape[0])
|
||||
return {'inputs': features}
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
def transcribe(
|
||||
self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0
|
||||
):
|
||||
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]}
|
||||
|
||||
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
|
||||
|
||||
segments = []
|
||||
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']
|
||||
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
|
||||
|
||||
return {"segments": segments, "language": language}
|
||||
|
||||
|
||||
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],
|
||||
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]
|
||||
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)))
|
||||
File diff suppressed because it is too large
Load Diff
@ -1 +0,0 @@
|
||||
{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
|
||||
@ -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"}
|
||||
File diff suppressed because one or more lines are too long
0
whisperx/assets/mel_filters.npz
Executable file → Normal file
0
whisperx/assets/mel_filters.npz
Executable file → Normal file
@ -1 +0,0 @@
|
||||
{"<|endoftext|>": 50257}
|
||||
File diff suppressed because it is too large
Load Diff
@ -1 +0,0 @@
|
||||
{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
|
||||
@ -1 +0,0 @@
|
||||
{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
|
||||
File diff suppressed because one or more lines are too long
@ -1,6 +1,6 @@
|
||||
import os
|
||||
from functools import lru_cache
|
||||
from typing import Union
|
||||
from typing import Optional, Union
|
||||
|
||||
import ffmpeg
|
||||
import numpy as np
|
||||
@ -15,8 +15,12 @@ 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
|
||||
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):
|
||||
@ -55,7 +59,9 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
|
||||
"""
|
||||
if torch.is_tensor(array):
|
||||
if array.shape[axis] > length:
|
||||
array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
|
||||
array = array.index_select(
|
||||
dim=axis, index=torch.arange(length, device=array.device)
|
||||
)
|
||||
|
||||
if array.shape[axis] < length:
|
||||
pad_widths = [(0, 0)] * array.ndim
|
||||
@ -85,11 +91,18 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
|
||||
)
|
||||
"""
|
||||
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:
|
||||
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):
|
||||
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
|
||||
|
||||
@ -101,6 +114,12 @@ def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int
|
||||
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)
|
||||
@ -111,6 +130,10 @@ def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int
|
||||
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
|
||||
@ -121,4 +144,4 @@ def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int
|
||||
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
|
||||
return log_spec
|
||||
|
||||
@ -1,710 +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]):
|
||||
seq = [t for t in tokens[k, self.sample_begin :].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
|
||||
|
||||
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
|
||||
81
whisperx/diarize.py
Normal file
81
whisperx/diarize.py
Normal file
@ -0,0 +1,81 @@
|
||||
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",
|
||||
):
|
||||
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)
|
||||
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:
|
||||
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"
|
||||
else:
|
||||
seg["speaker"] = speaker_count.index[0]
|
||||
|
||||
# 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):
|
||||
self.start = start
|
||||
self.end = end
|
||||
self.speaker = speaker
|
||||
@ -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
|
||||
@ -1,2 +0,0 @@
|
||||
from .basic import BasicTextNormalizer
|
||||
from .english import EnglishTextNormalizer
|
||||
@ -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
|
||||
File diff suppressed because it is too large
Load Diff
@ -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
|
||||
@ -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))
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,7 +1,144 @@
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
import zlib
|
||||
from typing import Callable, TextIO, Iterator, Tuple
|
||||
import pandas as pd
|
||||
from typing import Callable, Optional, TextIO
|
||||
|
||||
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
|
||||
@ -29,7 +166,9 @@ def compression_ratio(text) -> float:
|
||||
return len(text_bytes) / len(zlib.compress(text_bytes))
|
||||
|
||||
|
||||
def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
|
||||
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)
|
||||
|
||||
@ -43,209 +182,199 @@ def format_timestamp(seconds: float, always_include_hours: bool = False, decimal
|
||||
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 (
|
||||
f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
|
||||
)
|
||||
|
||||
|
||||
def write_txt(transcript: Iterator[dict], file: TextIO):
|
||||
for segment in transcript:
|
||||
print(segment['text'].strip(), file=file, flush=True)
|
||||
class ResultWriter:
|
||||
extension: str
|
||||
|
||||
def __init__(self, output_dir: str):
|
||||
self.output_dir = output_dir
|
||||
|
||||
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 __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(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)
|
||||
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
|
||||
def write_result(self, result: dict, file: TextIO, options: dict):
|
||||
for segment in result["segments"]:
|
||||
print(segment["text"].strip(), file=file, flush=True)
|
||||
|
||||
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,
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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:
|
||||
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 format_timestamp(self, seconds: float):
|
||||
return format_timestamp(
|
||||
seconds=seconds,
|
||||
always_include_hours=self.always_include_hours,
|
||||
decimal_marker=self.decimal_marker,
|
||||
)
|
||||
|
||||
|
||||
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 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++.
|
||||
"""
|
||||
|
||||
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'}
|
||||
extension: str = "tsv"
|
||||
|
||||
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]}'
|
||||
|
||||
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'
|
||||
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 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)
|
||||
|
||||
|
||||
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}"
|
||||
class WriteJSON(ResultWriter):
|
||||
extension: str = "json"
|
||||
|
||||
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 = []
|
||||
def write_result(self, result: dict, file: TextIO, options: dict):
|
||||
json.dump(result, file)
|
||||
|
||||
for segment in transcript:
|
||||
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 crow['start'] is not None:
|
||||
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']
|
||||
def get_writer(
|
||||
output_format: str, output_dir: str
|
||||
) -> Callable[[dict, TextIO, dict], None]:
|
||||
writers = {
|
||||
"txt": WriteTXT,
|
||||
"vtt": WriteVTT,
|
||||
"srt": WriteSRT,
|
||||
"tsv": WriteTSV,
|
||||
"json": WriteJSON,
|
||||
}
|
||||
|
||||
ass_str += '\n'.join(map(lambda x: dialogue(**x), ass_arr))
|
||||
if output_format == "all":
|
||||
all_writers = [writer(output_dir) for writer in writers.values()]
|
||||
|
||||
file.write(ass_str)
|
||||
def write_all(result: dict, file: TextIO, options: dict):
|
||||
for writer in all_writers:
|
||||
writer(result, file, options)
|
||||
|
||||
return write_all
|
||||
|
||||
return writers[output_format](output_dir)
|
||||
|
||||
def interpolate_nans(x, method='nearest'):
|
||||
if x.notnull().sum() > 1:
|
||||
|
||||
307
whisperx/vad.py
Normal file
307
whisperx/vad.py
Normal file
@ -0,0 +1,307 @@
|
||||
import hashlib
|
||||
import os
|
||||
import urllib
|
||||
from typing import Callable, Optional, Text, Union
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
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
|
||||
|
||||
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=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:
|
||||
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,
|
||||
"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, with min-cut operation
|
||||
to ensure not segments are longer than max_duration.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
onset : float, optional
|
||||
Onset threshold. Defaults to 0.5.
|
||||
offset : float, optional
|
||||
Offset threshold. Defaults to `onset`.
|
||||
min_duration_on : float, optional
|
||||
Remove active regions shorter than that many seconds. Defaults to 0s.
|
||||
min_duration_off : float, optional
|
||||
Fill inactive regions shorter than that many seconds. Defaults to 0s.
|
||||
pad_onset : float, optional
|
||||
Extend active regions by moving their start time by that many seconds.
|
||||
Defaults to 0s.
|
||||
pad_offset : float, optional
|
||||
Extend active regions by moving their end time by that many seconds.
|
||||
Defaults to 0s.
|
||||
max_duration: float
|
||||
The maximum length of an active segment, divides segment at timestamp with lowest score.
|
||||
Reference
|
||||
---------
|
||||
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
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
onset: float = 0.5,
|
||||
offset: Optional[float] = None,
|
||||
min_duration_on: float = 0.0,
|
||||
min_duration_off: float = 0.0,
|
||||
pad_onset: float = 0.0,
|
||||
pad_offset: float = 0.0,
|
||||
max_duration: float = float('inf')
|
||||
):
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.onset = onset
|
||||
self.offset = offset or onset
|
||||
|
||||
self.pad_onset = pad_onset
|
||||
self.pad_offset = pad_offset
|
||||
|
||||
self.min_duration_on = min_duration_on
|
||||
self.min_duration_off = min_duration_off
|
||||
|
||||
self.max_duration = max_duration
|
||||
|
||||
def __call__(self, scores: SlidingWindowFeature) -> Annotation:
|
||||
"""Binarize detection scores
|
||||
Parameters
|
||||
----------
|
||||
scores : SlidingWindowFeature
|
||||
Detection scores.
|
||||
Returns
|
||||
-------
|
||||
active : Annotation
|
||||
Binarized scores.
|
||||
"""
|
||||
|
||||
num_frames, num_classes = scores.data.shape
|
||||
frames = scores.sliding_window
|
||||
timestamps = [frames[i].middle for i in range(num_frames)]
|
||||
|
||||
# annotation meant to store 'active' regions
|
||||
active = Annotation()
|
||||
for k, k_scores in enumerate(scores.data.T):
|
||||
|
||||
label = k if scores.labels is None else scores.labels[k]
|
||||
|
||||
# initial state
|
||||
start = timestamps[0]
|
||||
is_active = k_scores[0] > self.onset
|
||||
curr_scores = [k_scores[0]]
|
||||
curr_timestamps = [start]
|
||||
for t, y in zip(timestamps[1:], k_scores[1:]):
|
||||
# currently active
|
||||
if is_active:
|
||||
curr_duration = t - start
|
||||
if curr_duration > self.max_duration:
|
||||
# if curr_duration > 15:
|
||||
# import pdb; pdb.set_trace()
|
||||
search_after = len(curr_scores) // 2
|
||||
# divide segment
|
||||
min_score_div_idx = search_after + np.argmin(curr_scores[search_after:])
|
||||
min_score_t = curr_timestamps[min_score_div_idx]
|
||||
region = Segment(start - self.pad_onset, min_score_t + self.pad_offset)
|
||||
active[region, k] = label
|
||||
start = curr_timestamps[min_score_div_idx]
|
||||
curr_scores = curr_scores[min_score_div_idx+1:]
|
||||
curr_timestamps = curr_timestamps[min_score_div_idx+1:]
|
||||
# switching from active to inactive
|
||||
elif y < self.offset:
|
||||
region = Segment(start - self.pad_onset, t + self.pad_offset)
|
||||
active[region, k] = label
|
||||
start = t
|
||||
is_active = False
|
||||
curr_scores = []
|
||||
curr_timestamps = []
|
||||
# currently inactive
|
||||
else:
|
||||
# switching from inactive to active
|
||||
if y > self.onset:
|
||||
start = t
|
||||
is_active = True
|
||||
curr_scores.append(y)
|
||||
curr_timestamps.append(t)
|
||||
|
||||
# if active at the end, add final region
|
||||
if is_active:
|
||||
region = Segment(start - self.pad_onset, t + self.pad_offset)
|
||||
active[region, k] = label
|
||||
|
||||
# because of padding, some active regions might be overlapping: merge them.
|
||||
# also: fill same speaker gaps shorter than min_duration_off
|
||||
if self.pad_offset > 0.0 or self.pad_onset > 0.0 or self.min_duration_off > 0.0:
|
||||
if self.max_duration < float("inf"):
|
||||
raise NotImplementedError(f"This would break current max_duration param")
|
||||
active = active.support(collar=self.min_duration_off)
|
||||
|
||||
# remove tracks shorter than min_duration_on
|
||||
if self.min_duration_on > 0:
|
||||
for segment, track in list(active.itertracks()):
|
||||
if segment.duration < self.min_duration_on:
|
||||
del active[segment, track]
|
||||
|
||||
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()
|
||||
for k, vad_t in enumerate(vad_arr):
|
||||
region = Segment(vad_t[0] - pad_onset, vad_t[1] + pad_offset)
|
||||
active[region, k] = 1
|
||||
|
||||
|
||||
if pad_offset > 0.0 or pad_onset > 0.0 or min_duration_off > 0.0:
|
||||
active = active.support(collar=min_duration_off)
|
||||
|
||||
# remove tracks shorter than min_duration_on
|
||||
if min_duration_on > 0:
|
||||
for segment, track in list(active.itertracks()):
|
||||
if segment.duration < min_duration_on:
|
||||
del active[segment, track]
|
||||
|
||||
active = active.for_json()
|
||||
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"))
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
Reference in New Issue
Block a user