You’re only partially correct about input speed. If you want to dictate an email then yes you need to think about each word you want to say and the order in which to say them. Coupled with an LLM that problem is diminished because you can just kind of have a conversation with the LLM and tell it to draft an email.
and how much of that conversation with an LLM is “No, what I want is…” because it assumed something; or just straight up hallucinated or the typo made it go off on a tangent?
As for whisper, I can find sources that are saying for American-English speakers in a not-noisy environment (aka the best case scenario,) the model has a word error rate between 2-8%. For reference, Dragon NaturallySpeaking had a WER of 3-5%. So I wouldn’t say that Whisper has made any substantial improvements, and they’re OpenAi. you can trust them if you want. I don’t think that’ll work out well in the long run, though.
I’d like to see the source that says Dragon’s WER in the 90s was 3-5%. I used Dragon in the 2000s and it just wasn’t comparable to the current state of the art.
whisper.cpp is an opensource implementation, although I’m not certain exactly how open.
when you’re providing context rather than instructions the tendency for a model to hallucinate or run off on a tangent is minimal, because the context you’re providing has it’s own cohesion.
I’d like to see the source that says Dragon’s WER in the 90s was 3-5%. I used Dragon in the 2000s and it just wasn’t comparable to the current state of the art.
https://dragon-medical-transcription.com/history_speech_recognition.html, for example. a lot of adverts and awards were given to it (admittedly awards like PC Mag that were probably paid advertising… but that’s why I went with Open AI’s assessment on whisper at 2%.) Dragon was boasting 99% accuracy after (admittedly months) of training; and it frequently reached it. there were some gotchas in that- the months-long training was a big one. The other was that you frequently had to slow down and be careful to enunciate that you don’t have to do with modern systems (including the MS versions of Dragon- they bought it out at some point)
whisper.cpp is an opensource implementation, although I’m not certain exactly how open.
It’s on the MIT license, if that helps. I take issue with anything OpenAI is involved in. for oh-so-many reasons.
and how much of that conversation with an LLM is “No, what I want is…” because it assumed something; or just straight up hallucinated or the typo made it go off on a tangent?
As for whisper, I can find sources that are saying for American-English speakers in a not-noisy environment (aka the best case scenario,) the model has a word error rate between 2-8%. For reference, Dragon NaturallySpeaking had a WER of 3-5%. So I wouldn’t say that Whisper has made any substantial improvements, and they’re OpenAi. you can trust them if you want. I don’t think that’ll work out well in the long run, though.
I’d like to see the source that says Dragon’s WER in the 90s was 3-5%. I used Dragon in the 2000s and it just wasn’t comparable to the current state of the art.
whisper.cpp is an opensource implementation, although I’m not certain exactly how open.
when you’re providing context rather than instructions the tendency for a model to hallucinate or run off on a tangent is minimal, because the context you’re providing has it’s own cohesion.
https://dragon-medical-transcription.com/history_speech_recognition.html, for example. a lot of adverts and awards were given to it (admittedly awards like PC Mag that were probably paid advertising… but that’s why I went with Open AI’s assessment on whisper at 2%.) Dragon was boasting 99% accuracy after (admittedly months) of training; and it frequently reached it. there were some gotchas in that- the months-long training was a big one. The other was that you frequently had to slow down and be careful to enunciate that you don’t have to do with modern systems (including the MS versions of Dragon- they bought it out at some point)
It’s on the MIT license, if that helps. I take issue with anything OpenAI is involved in. for oh-so-many reasons.