Hey r/MachineLearning!
At Hugging Face, we’ve worked hard the last months to create a powerful, but fast distilled version of Whisper. We’re excited to share our work with you now!
Distil-Whisper is 6x faster than Whisper-large-v2 and performs within 1% WER on out-of-distribution datasets. On long-form audio, we even achieve better results thanks to a reduction in hallucinations.
For more information, please have a look:
- GitHub page: https://github.com/huggingface/distil-whisper/tree/main
- Paper: https://github.com/huggingface/distil-whisper/blob/main/Distil_Whisper.pdf
Quick summary:
- Distillation Process
We’ve kept the whole encoder, but reduced the decoder to just 2 layers. Encoding takes O(1) forward passes, decoding takes O(N). To improve speed, all that matters is the decoder! The encoder is frozen during distillation while we fine-tune all of the decoder. Both KL loss and pseudo-labeling next word prediction is used.
- Data
We use 20,000h of open-sourced audio data coming from 9 diverse audio datasets. A WER-filter is used to make sure low-quality training data is thrown out.
- Results
We’ve evaluated the model only on out-of-distribution datasets and are only 1% worse than Whisper-large-v2 on short-form evals (CHiME-4, Earnings-22, FLEURS, SPGISpeech). On long-form evals (Earnings, Meanwhile, Rev 16) we beat Whisper-large-v2 thanks to a reduction in hallucinations.
- Robust to noise
Distil-Whisper is very robust to noise (similar to its teacher). We credit this to keeping the original encoder frozen during training.
- Pushing for max inference time
Distil-Whisper is 6x faster than Whisper on both short-form and long-form audio. In addition, we employ Flash Attention and chunked decoding which helps us achieve a real-time factor of 0.01!
- Checkpoints?!
Checkpoints will be released this Thursday and will be directly integrated into Transformers. All checkpoints will be licensed under MIT.
The ideas in the paper seem to be using a very similar concept from a NMT paper called Deep Encoders, Shallow Encoders. Surprised to see no citation in the related work.
Very cool. Is this still compatible with faster whisper / whisperx and if not how does the speed up compare? Whisperx is already way more than 6x faster using the same v2 models (and still multi lingual).
If we can stack these improvements - awesome!
Will you release your fine tuning framework so others can find tune on their own data?
Does the improvement itself depend on large scale data or can we achieve positive results with smaller fine tuning sets via your approach here?
Why did you bury the RTF results in the appendix? These are way more interesting than relative latency compared to vanilla whisper!
One thing I would question: A100GPU might be ‘typical’ for a lot of cloud offerings like what you see coming from DeepGram and the like, but it’s definitely not typical of what you’d find or have access to for any sort of on-site client. Here CPU is still king.
Have you run any experiments with CPU only inference? What does that look like? Here you can still achieve 0.01x-0.06xRT with CPU only inference and basically the same accuracy with a fine-tuned model utilizing the latest production releases from K2/icefall. This looks like it’s getting closer, but I’d still be inclined to recommend using this distilled model to pre-generate a bunch of pseudo labeled training data for a smaller, dedicated K2/sherpa production system for anything on site.
Hype!
not sure if someone also read the paper, in this paper they picks pseudo label via WER which means it already have the ground-truth, why not using those ground-truth as training goal but use pseudo label as goal?
wondering they must do the experiment but not reveal in the paper