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Is there a list of system requirements somewhere ? Can it run on cheaper low memory GPUs ? maybe CPUs ?
Their models range from 70mb to 3gb. The largest model is smaller than the optimised stable diffusion. Not sure what the inference speed is like, haven't tried it myself yet.
That's intriguing. You can just set the model to transcribe everything into English, no matter which language the speaker is using, and it just works. Given that many people are much better at understanding English than at speaking it, this might make voice interfaces much more accessible without much work.
I'm not in the Speech Recognition circles and am looking for open source speech recognition I can play around with - would this be the new state of the art?
On one hand, it may capture something "deeper" about language.
On the other hand, it's likely to do great in general, but miss particularities of some language.
Understanding the coverage of the training model seems a perennial problem. Is there any (shorthand) way to compare language model training corpora?
Clearly if they use common subsets we have a literal comparison. I'm more interested in whether there's progress in characterizing corpora by speech styles, fluency, vocabulary sets, (noise) environment, emotionality, proposition types, etc.
(btw: 25 minutes for a 9-minute segment on a 12-thread x86. Lots of jargon spelled as it sounds. Sentences capitalized but no punctuation. Overall good.)
What an interesting approach. What benefits does this have over having two dedicated models, one for speech-to-text, and another for translation?
It just seems so odd, given the problems of speech-to-text and Spanish-to-English seems so different from one another (in terms of the problem domain). Seems so unusual to have both handled by one model!
Does knowledge of speech-to-text carry over into knowledge of translation? Does knowledge of translation carry over into knowledge of speech-to-text? So weird.
Neat, https://github.com/openai/whisper - they have open-sourced it, even the model weights, so they are living up to their name in this instance. The 4 examples are stunningly good (the examples have speakers with heavy accents, speaking in foreign language, speaking with dynamic background noise, etc.), this is far and away better than anything else I've seen. Will be super curious to see other folks trying it out…
I will try to put the code to the test, see how it goes.