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<h1 align="center">WhisperX</h1>
<h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com/">https://www.maxbain.com/</a></h6>
<p align="center">
<a href="https://github.com/m-bain/whisperX/stargazers">
<img src="https://img.shields.io/github/stars/m-bain/whisperX.svg?colorA=orange&colorB=orange&logo=github"
alt="GitHub stars">
</a>
<a href="https://github.com/m-bain/whisperX/issues">
<img src="https://img.shields.io/github/issues/m-bain/whisperx.svg"
alt="GitHub issues">
</a>
<a href="https://github.com/m-bain/whisperX/blob/master/LICENSE">
<img src="https://img.shields.io/github/license/m-bain/whisperX.svg"
alt="GitHub license">
</a>
<a href="https://twitter.com/intent/tweet?text=&url=https%3A%2F%2Fgithub.com%2Fm-bain%2FwhisperX">
<img src="https://img.shields.io/twitter/url/https/github.com/m-bain/whisperX.svg?style=social" alt="Twitter">
</a>
</p>
<p align="center">
<a href="#what-is-it">What is it</a> •
<a href="#setup">Setup</a> •
<a href="#example">Example usage</a>
</p>
<img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/208313881-903ab3ea-4932-45fd-b3dc-70876cddaaa2.png">
<h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com">https://www.maxbain.com</a></h6>
<p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy using forced alignment.
</p>
<h2 align="left">What is it 🔎</h2>
<h2 align="left", id="what-is-it">What is it 🔎</h2>
This repository refines the timestamps of openAI's Whisper model via forced aligment with phoneme-based ASR models (e.g. wav2vec2.0)
This repository refines the timestamps of openAI's Whisper model via forced aligment with phoneme-based ASR models (e.g. wav2vec2.0), multilingual use-case.
**Whisper** is an ASR model [developed by OpenAI](https://github.com/openai/whisper), trained on a large dataset of diverse audio. Whilst it does produces highly accurate transcriptions, the corresponding timestamps are at the utterance-level, not per word, and can be inaccurate by several seconds.
@ -17,40 +45,40 @@ This repository refines the timestamps of openAI's Whisper model via forced alig
**Forced Alignment** refers to the process by which orthographic transcriptions are aligned to audio recordings to automatically generate phone level segmentation.
<img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/208313881-903ab3ea-4932-45fd-b3dc-70876cddaaa2.png">
<h2 align="left">Setup ⚙️</h2>
<h2 align="left" id="setup">Setup ⚙️</h2>
Install this package using
`pip install git+https://github.com/m-bain/whisperx.git`
You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
<h2 align="left">Examples💬</h2>
<h2 align="left" id="example">Example usage💬</h2>
### English
Run whisper on example segment (using default params)
`whisperx examples/sample01.wav --model medium.en --output examples/whisperx --align_model WAV2VEC2_ASR_LARGE_LV60K_960H --align_extend 2`
whisperx examples/sample01.wav
If low gpu memory is required, use a smaller align model e.g. `WAV2VEC2_ASR_BASE_LV60K_960H`
Using normal whisper out of the box, many transcriptions are out of sync:
For increased timestamp accuracy, at the cost of higher gpu mem, use a bigger alignment model e.g.
https://user-images.githubusercontent.com/36994049/207743923-b4f0d537-29ae-4be2-b404-bb941db73652.mov
whisperx examples/sample01.wav --model medium.en --align_model WAV2VEC2_ASR_LARGE_LV60K_960H --output_dir examples/whisperx
Now, using *WhisperX* with forced alignment to wav2vec2.0:
Result using *WhisperX* with forced alignment to wav2vec2.0 large:
https://user-images.githubusercontent.com/36994049/208253969-7e35fe2a-7541-434a-ae91-8e919540555d.mp4
Compare this to original whisper out the box, where many transcriptions are out of sync:
https://user-images.githubusercontent.com/36994049/207743923-b4f0d537-29ae-4be2-b404-bb941db73652.mov
## Other Languages
For non-english ASR, it is best to use the `large` whisper model.
### French
`whisperx --model large --language fr examples/sample_fr_01.wav --align_model VOXPOPULI_ASR_BASE_10K_FR --output_dir examples/whisperx/ --align_extend 2`
whisperx examples/sample_fr_01.wav --model large --language fr --align_model VOXPOPULI_ASR_BASE_10K_FR --output_dir examples/whisperx
https://user-images.githubusercontent.com/36994049/208298804-31c49d6f-6787-444e-a53f-e93c52706752.mov
@ -58,8 +86,7 @@ https://user-images.githubusercontent.com/36994049/208298804-31c49d6f-6787-444e-
### German
`whisperx --model large --language de examples/sample_de_01.wav --align_model VOXPOPULI_ASR_BASE_10K_DE --output_dir examples/whisperx/ --align_extend 2`
whisperx examples/sample_de_01.wav --model large --language de --align_model VOXPOPULI_ASR_BASE_10K_DE --output_dir examples/whisperx
https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-97d4-0aebd07e0eb3.mov
@ -68,7 +95,7 @@ https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-
### Italian
`whisperx --model large --language it examples/sample_it_01.wav --align_model VOXPOPULI_ASR_BASE_10K_IT --output_dir examples/whisperx/ --align_extend 2`
whisperx examples/sample_it_01.wav --model large --language it --align_model VOXPOPULI_ASR_BASE_10K_IT --output_dir examples/whisperx
@ -78,17 +105,16 @@ https://user-images.githubusercontent.com/36994049/208298819-6f462b2c-8cae-4c54-
`whisperx --model large --language ja examples/sample_ja_01.wav --align_model jonatasgrosman/wav2vec2-large-xlsr-53-japanese --output_dir examples/whisperx --align_extend 2`
https://user-images.githubusercontent.com/19920981/208448405-60f80c0e-2715-42d8-9437-e19e6362b638.mov
<h2 align="left">Limitations ⚠️</h2>
<h2 align="left" id="limitations">Limitations ⚠️</h2>
- Not thoroughly tested, especially for non-english, results may vary -- please post issue to let me know its results on your data
- Not thoroughly tested, especially for non-english, results may vary -- please post issue to let me know the results on your data
- Whisper normalises spoken numbers e.g. "fifty seven" to arabic numerals "57". Need to perform this normalization after alignment, so the phonemes can be aligned. Currently just ignores numbers.
- 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.
<h2 align="left">Coming Soon 🗓</h2>
<h2 align="left" id="coming-soon">Coming Soon 🗓</h2>
[x] Multilingual init
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[ ] Automatic align model selection based on language detection
[ ] Reduce GPU (clear cache etc.)
[ ] Incorporating word-level speaker diarization
[ ] Inference speedup with batch processing
<h2 align="left">Contact</h2>
<h2 align="left" id="contact">Contact</h2>
Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk if using this for commerical purposes.
Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk if using this commerically.
<h2 align="left">Acknowledgements 🙏</h2>
<h2 align="left" id="acks">Acknowledgements 🙏</h2>
Of course, this is mostly just a modification to [openAI's whisper](https://github.com/openai/whisper).
As well as accreditation to this [PyTorch tutorial on forced alignment](https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html)
<h2 align="left">Citation</h2>
<h2 align="left" id="cite">Citation</h2>
If you use this in your research, just cite the repo,
```bibtex