WhisperX
Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy using forced alignment.
What is it 🔎
This repository refines the timestamps of openAI's Whisper model via forced aligment with phoneme-based ASR models (e.g. wav2vec2.0)
**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.
**Phoneme-Based ASR** A suite of models finetuned to recognise the smallest unit of speech distinguishing one word from another, e.g. the element p in "tap". A popular example model is [wav2vec2.0](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self).
**Forced Alignment** refers to the process by which orthographic transcriptions are aligned to audio recordings to automatically generate phone level segmentation.
Setup ⚙️
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.
Examples💬
### English
Run whisper on example segment (using default params)
`whisperx examples/sample01.wav --model medium.en --output examples/whisperx --align_model WAV2VEC2_ASR_BASE_960H --align_extend 2`
For increased timestamp accuracy, at the cost of higher gpu mem, use a bigger alignment model e.g.
`WAV2VEC2_ASR_LARGE_LV60K_960H` or `HUBERT_ASR_XLARGE`
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`
https://user-images.githubusercontent.com/36994049/208298804-31c49d6f-6787-444e-a53f-e93c52706752.mov
### 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`
https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-97d4-0aebd07e0eb3.mov
### 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`
https://user-images.githubusercontent.com/36994049/208298819-6f462b2c-8cae-4c54-b8e1-90855794efc7.mov
Limitations ⚠️
- 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.
Coming Soon 🗓
[x] Multilingual init
[x] Subtitle .ass output
[ ] Automatic align model selection based on language detection
[ ] Option to minimise gpu load (chunk wav2vec)
[ ] Incorporating word-level speaker diarization
[ ] Inference speedup with batch processing
Contact
Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk if using this commerically.
Acknowledgements 🙏
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)
Citation
If you use this in your research, just cite the repo,
```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}
}
```