b47bb769a0
This allows the alignment method to be used for other languages -- depending on what characters are in the align_model dictionary.
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, 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.
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 usingpip 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.
Example
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
Outputs both word-level, and phrase level.
Expected outputs:
Example: Using normal whisper out of the box, many transcriptions are out of sync:
Now, using WhisperX with forced alignment to wav2vec2.0:
(a) refining segment timestamps
(b) word-level timestamps
Limitations ⚠️
- Hacked this up quite quickly, there might be some errors, please raise an issue if you encounter any.
- Currently only working and tested for ENGLISH language.
- 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)
Coming Soon 🗓
[ ] Incorporating word-level speaker diarization
[ ] Inference speedup with batch processing
Contact
Contact maxbain[at]robots.ox.ac.uk non-bug related queries.
Acknowledgements 🙏
Of course, this is mostly just a modification to openAI's whisper. As well as accreditation to this PyTorch tutorial on forced alignment