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Whisper – open source speech recognition by OpenAI

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Re: Whisper – open source speech recognition by OpenAI

#401

Post by gzer0 »

NaturalPhallacy wrote: post #333
worthless-trash wrote: post #320

Earlier quoted context omitted.

For a good example of this, read ANY of trumps speaches transcribed.

I mean if you want to make it unnecessarily political, Biden's are worse: https://www.youtube.com/watch?v=3bWM1zsnTJc

To be fair, you chose a video that displays an amalgamation of the biggest gaffes of 2021 for Biden.

“During his term as President of the United States, Donald Trump made tens of thousands of false or misleading claims. The Washington Post's fact-checker had tallied the number as 30,573 by January 2021, an average of about 21 per day by the end of his presidency.” [1][2][3][4]

I think it’s fair to say there would be a 100 hour long plus video / documentary if they were all compiled into one. lovely!

  - [1] Fact Checker (January 20, 2021). "In four years, President Trump made 30,573 false or misleading claims". The Washington Post. Archived from the original on January 20, 2021.

  - [2] Kessler, Glenn (January 23, 2021). "Trump made 30,573 false or misleading claims as president. Nearly half came in his final year". The Washington Post. Archived from the original on January 24, 2021. Retrieved January 24, 2021.

  - [3] Elfrink, Tim (August 14, 2020). "'Do you regret at all, all the lying you've done?': A reporter's blunt question to Trump goes unanswered". The Washington Post. Retrieved August 14, 2020.
[4] https://en.m.wikipedia.org/wiki/Veracity_of_statements_by_Do...

Re: Whisper – open source speech recognition by OpenAI

#402

Post by zarzavat »

jfoster wrote: post #93
solarmist wrote: post #52

Earlier quoted context omitted.

This kind of model is harder to abuse, so I guess it passed their internal checks much more easily. I can understand not releasing GPT-3, even if I disagree with the decision.

True. The potential of GPT-3 to cause internet mayhem was/is significant. I would argue that the mere act of announcing it was still a catalyst for an eventual GPT-3-like model being released. In revealing it, they established a target for what open source models could aim to achieve, and simultaneously got bad actors thinking about ways to abuse it.

It was a credible argument when GPT-3 was released. But now there are open models that are as capable as GPT-3 and that mayhem has not materialized, with the possible exception of GPT-4chan. They could release it now under a non-commercial license, if they cared to.

Re: Whisper – open source speech recognition by OpenAI

#403

Post by BartjeD »

notahacker wrote: post #295
thfuran wrote: post #137

Earlier quoted context omitted.

>equally true of human transcription, in which individual words are often [UNINTELLIGEBLE]. ML systems somewhat notoriously do not necessarily make the same sorts of errors that a human would. And I'd expect a large portion of the errors to be transcribing the wrong words rather that indicating that a word couldn't be transcribed. That sort of error means that you can't really get away with manually reviewing just 3%…

ML tending to make weird mistakes rather than subtle ones that make sense in context like human transcribers is likely to make them easier to spot. And there are humans in the loop too, and an enormous amount of redundancy in the questions and answer, so even plausible false transcriptions will get picked up on if they matter. Nobody gets sent to jail simply because the transcription process - human or machine - acci…

The thing is that 'Likely' is very far away from 'always'. There is no guarantee the mistake will be easy to spot.

For entertainment purposes AI transcription is awesome.

For serious business applications the ability to recognize mistakes will continue to be a field to which serious attention is given. It would be interesting to see AI processes double check itself, and also run a logic check on whether the transcription makes sense. So that it can report sections flagged as incongruous or of dubious reliability.

Re: Whisper – open source speech recognition by OpenAI

#405

Post by wodenokoto »

Is it also a translation model? All the example transcripts are in English, regardless of the language of the purportedly transcribed audio.

The description makes it sound like it is a model for transcribing English audio.

> We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.

Re: Whisper – open source speech recognition by OpenAI

#407

Post by donkarma »

gzer0 wrote: post #401
NaturalPhallacy wrote: post #333

Earlier quoted context omitted.

I mean if you want to make it unnecessarily political, Biden's are worse: https://www.youtube.com/watch?v=3bWM1zsnTJc

To be fair, you chose a video that displays an amalgamation of the biggest gaffes of 2021 for Biden. “During his term as President of the United States, Donald Trump made tens of thousands of false or misleading claims. The Washington Post's fact-checker had tallied the number as 30,573 by January 2021, an average of about 21 per day by the end of his presidency.” [1][2][3][4] I think it’s fair to say there would be…

No post body was provided.

Re: Whisper – open source speech recognition by OpenAI

#409

Post by KingMob »

Vetch wrote: post #79
The5thElephant wrote: post #53

Earlier quoted context omitted.

I agree they have the best compared to Apple, Amazon, Microsoft. However I don't think it is as good as what is being shown here by OpenAI.

My experience with the APIs is Google is excellent and Microsoft is slightly better. And the offline model I've been using that's nearly as good as both is facebook's wav2vec2-large-960h-lv60-self. Don't believe what's on marketing pages, they rarely transfer to the real world. Will have to make time to try it and see. In theory, given task diversity and sheer number of hours, it should be a lot more robust but will…

Weird. I started working on an ASR SaaS in my spare time, and at least on the test podcasts, Google was the worst: https://www.sammaspeech.com/blogs/post/speech-recognition-ac...
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