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The only thing Whisper misses is speaker diarization. I'm currently working on a model that uses Whisper + pyannote to transcribe Interviews and also detects who is speaking. It's working but damn it takes so long
Related/Off Topic: Is there a documented way to improve the accuracy of a particular language model? Say we can put in the effort to collect 1000's of verified/transcribed samples of a language that is currently scored poorly (WER). What steps do I have to take to get those improvements into the system?
Yes, you need to fine-tune the model with your data. This might be easy or hard, depending on your experience level and complexity of the model and available tooling. For this model specifically ( https://github.com/openai/whisper ) it would be a significant challenge for a newcomer. Luckily Huggingface has a blog post that will get you started: https://huggingface.co/blog/fine-tune-whisper
I've been looking at using compilation in torch but not successful yet as otherwise it can take awhile to run. https://pytorch.org/tutorials/intermediate/torch_compile_tut...
Has anyone looked at the code? Because as a Swedish citizen I must say that anything I use by Schibsted is a hot mess from a UX perspective.
This looks really good, thanks! Really appreciate this and all the other Whisper implementations in this thread as I am sorting up transcriptions for my 120+ podcast episodes.
There's no GUI though
Use case: I want to transcribe my poker hands while playing, eg: "Flop was 2 of spaces, 3 of diamonds and King of spades", "Button raised to $20" etc.
When I tried using Whisper and some other model, the recognition accuracy was atrocious, and it kept finding non-poker words that sounded similar to poker words. I want to restrict its search space to my own list of poker words which should significantly increase the accuracy (theoretically).
Any suggestions on how to go about this?
Is there an open source speech recognition model which can be restricted to a smaller domain-specific dictionary? Use case: I want to transcribe my poker hands while playing, eg: "Flop was 2 of spaces, 3 of diamonds and King of spades", "Button raised to $20" etc. When I tried using Whisper and some other model, the recognition accuracy was atrocious, and it kept finding non-poker words that sounded similar to poker…
Whisper source is very readable, check out https://github.com/openai/whisper/blob/main/whisper/decoding...