Insanely Fast Whisper
github.com
github.com
./main -m models/ggml-small.bin -f alice.wav --prompt "Audiobook reading by a British woman:"
./main -m models/ggml-small.bin -f output.wav --prompt "Research talk by Junyao, Harbin Institute of Technology, Shenzhen, research engineer at MaiMemo"
Although I seem to have trouble to get the context to persist across hundreds of tokens. Tokens that are corrected may revert back to the model's underlying tokens if they weren't repeated enough.
It only affects the first 30 second window of the transcription, as far as I’ve been able to tell. If the word in question appears in that window, then it will influence the next window, and so on… but as soon as it doesn’t exist in one 30 second window, it’s effectively gone, from what I’ve seen.
It’s “underused” precisely because this feature is pretty much useless if you’re transcribing anything other than quick snippets of speech.
It’s also hard to use since you have to know in advance what hard-to-transcribe words are going to be in the audio.
We need a better solution. It would be much better if there were an easy way to fine tune Whisper to learn new vocab.
I'm not sure why you're so dismissive when real-time transcription is an important use-case that falls under that bucket of "quick snippets".
> It’s also hard to use since you have to know in advance what hard-to-transcribe words are going to be in the audio.
I think it's more context-dependent than it is "hard". It's ideal for streaming meeting transcripts. In my use-cases, I use the prompt to feed in participant names, company terms/names, and other potential words. It's also much easier to just rattle off a list of potential words that you know are going to be in the transcription that are difficult or spelled differently.
> We need a better solution. It would be much better if there were an easy way to fine tune Whisper to learn new vocab.
Prompting is infinitely easier than fine-tuning in every aspect. I can reuse the same model in any context and just swap out the prompt. I don't have to spend time/money finetuning... I don't have to store multiple fine-tuned copies of whisper for different contexts... I'm not sure what better solution you envision but fine-tuning is certainly not easier than prompting.
It may be easy to rattle off a list of words, but it doesn’t work nearly as well as it should, so what’s the point? I also never said fine tuning would be easier than prompting. I said it would be better. It would just need to be easier than fine tuning currently is, not easier than prompting.
Fine tuning that I’m talking about would not be limited to only a few new words. You would only need one model, like we have today. It would just be your model that knows all the specific words and spellings you prefer. By analogy to other machine learning models, I would expect a lightweight LoRA approach would also work.
I just haven’t seen anyone working on these solutions that would actually be scalable, unlike the initial prompt.
Initial prompt works in extremely specific scenarios, but it has been so unreliable for long transcripts in my experience that I certainly don’t bother with it anymore. Someone mentioned Alexa-style home assistants, which would have short enough audio snippets that initial prompt would actually be useful.
It is missing the most recent commits from what appears to be the real source: https://github.com/Vaibhavs10/insanely-fast-whisper
The only added commit is adding a replicate.com example, whatever that means.
Somebody happened to stumble across our fork of the model and submitted it. We didn't submit it nor intend for it to be an ad. I hope the submission gets replaced with the upstream repo so the author gets full credit. :)
[0] https://github.com/Vaibhavs10/insanely-fast-whisper/pull/42
Edit: Also this seems a bit suspicious - this seems like someone just forked another persons active repo (https://github.com/Vaibhavs10/insanely-fast-whisper) and posted as their own?
Author of Wordcab-Transcribe here. We use faster-whisper + NeMo for diarization, if you want to take a look.
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en...
https://github.com/Vaibhavs10/insanely-fast-whisper/issues/3...
It’s whisper in the browser using WASM with an transcription annotation Ui.
WhisperX with the new model. It's not fast.