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Whisper – open source speech recognition by OpenAI

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Re: Whisper – open source speech recognition by OpenAI

#431

Post by forgingahead »

MayeulC wrote: post #415
forgingahead wrote: post #374

Earlier quoted context omitted.

Welcome to every single Python ML project - dependency hell will quickly kill any enthusiasm one may have for trying out projects. It really feels archaic to have these issues with such cutting edge technology.

You can blame CUDA quite a bit for that. Proprietary, you need to sort out which driver you need, plus an nvidia GPU... I tried compiling pytorch with vulkan support, but there are a few LDFLAGS that are wrong. I'll try to solve that some time later. One piece of advice: use distribution packages! Arch provides pytorch-cuda, and has PKGBUILDS as well. For reproductibility, I wish we were all on Nix/Guix, but that's n…

CUDA is not the problem, the problem is crappy code being released on Github where basic things like requirements.txt are missing, never mind an earnest attempt to provide details about the environment that the code was running on. This is on top of code that has lots of hard-coded references to files and directories, plus also many python libraries just breaking compatibility with each other on point releases.

I can't find a source now, but I remember reading some code where the maintainer had to change a huge chunk of code because the point change for a dependency library literally flipped either how the library handled height/width or BGR channels (I can't remember which one but it was preposterous) from the 2.5.4 to the 2.5.5 version. There is no reason for doing that - it breaks everything just for grins and giggles.

Python itself is also a problem, but that's a rant for another day. Ah, how I wish Ruby had become the defacto language of choice for ML/Deep Learning!

Re: Whisper – open source speech recognition by OpenAI

#432

Post by TheCapeGreek »

wging wrote: post #127
anigbrowl wrote: post #116

Earlier quoted context omitted.

No it isn't. That just means 2-3% of your content needs to be double-checked by a person at the audio level, saving huge amounts of time - equally true of human transcription, in which individual words are often [UNINTELLIGEBLE]. Would you want to review this fully before going into court, absolutely - because you'd want to play the recording to a jury for emotional impact. Can you rely on it when you want to quickly…

Doesn't it mean 100% of your content needs to be double-checked? You can't easily identify which 2-3% of your content has errors. I'm aware that errors are more likely when the model is less confident of its predictions, but that shouldn't be enough. (edit for clarification: errors are not always something like "[UNINTELLIGIBLE]", where the system knows it doesn't know; they can also be misrecognitions that the syste…

Having done audio transcription in college as a side gig, it takes a lot longer than it sounds. Even at a decent 100wpm you'll take about 5 minutes to type out 1 minute of audio.

Not having to pause + rewind will save a ton of time for that 3%.

Re: Whisper – open source speech recognition by OpenAI

#433

Post by yourapostasy »

anigbrowl wrote: post #213
wging wrote: post #127

Earlier quoted context omitted.

Doesn't it mean 100% of your content needs to be double-checked? You can't easily identify which 2-3% of your content has errors. I'm aware that errors are more likely when the model is less confident of its predictions, but that shouldn't be enough. (edit for clarification: errors are not always something like "[UNINTELLIGIBLE]", where the system knows it doesn't know; they can also be misrecognitions that the syste…

By the time you're prosecuting someone in court, yes of course you double, triple, quadruple check everything. That's why lawyers get paid the big bucks (for now...). But yes you can identify which content probably has errors and flag it as such. Look, I have decades of experience dealing with human speech, and not just as an editor - I can trace the human voice from neural impulses in Broca's region through the phys…

> So when I say that machine transcription is as good as human realtime transcription now...

Would you go as far as to assert machine transcription can be used as an objective benchmark of a speaker’s verbal legibility?

It is fraught with political and interpersonal dynamics to approach someone even privately one on one today and gently suggest their career would get a huge boost if they hired a voice coach to help improve their verbal communication delivery. So even when I don’t directly mention their accent, it becomes a very sensitive subject with many.

However, if audio professionals like you can point to a system and say the raw biomechanics and acoustic physics of the world dictate that this is as physically and psychometrically good as audio parsing of human speech gets regardless whether the system was biologically evolved or ML evolved, the conversation can be couched even more objectively.

I enable recording and voice transcription in every meeting I can (ostensibly for DE&I but really for my own selfish purposes), and already observe in myself I have to work hard to overcome a tendency to gloss over speakers who don’t transcribe well when I review meeting transcripts to jot down any key information I might have missed taking notes upon during the meeting.

Note that I’m perfectly aware that my foreign language verbal skills are nowhere near the English skills of those I have tried to help. If the lingua franca of the coding world switched to Urdu tomorrow, then I’d hire help to learn and polish my spoken Urdu, like I went to a speech coach when learning public speaking because I can always use help in the many skills I lack.

Re: Whisper – open source speech recognition by OpenAI

#434

Post by zed1726 »

capableweb wrote: post #414
bruckie wrote: post #384

Earlier quoted context omitted.

Yes, it works completely offline, including transcription and recognition of music. There's an optional cloud sync feature, which I assume is the reason for the notice on Google Play. (Work for Google, don't speak for them.)

Thanks. Whose the third party that might get access to the audio? First party would be me, second party would be Google and then the third?

No post body was provided.

Re: Whisper – open source speech recognition by OpenAI

#435

Post by beanlog »

andy_xor_andrew wrote: post #27

Hold on, it does not only speech recognition, but also language translation, in the same model? 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 b…

It sounds useful to me because you can use tone information to help with the translation, which text-to-text translation can't do. But I'm not sure if that's how this model actually works.

Re: Whisper – open source speech recognition by OpenAI

#436

Post by mazork »

bambax wrote: post #61
pen2l wrote: post #32

Earlier quoted context omitted.

Interesting, I'm a non-native French speaker, the original French piece struck me as being entirely normal (but maybe it was just the perfect French accent that swayed me). Can you please point out what he said which wasn't idiomatic or naturally-worded French?

Little details. The second sentence is really bizarre: > Nous établissons que l'utilisation de données d'un tel nombre et d'une telle diversité est la raison pour laquelle le système est à même de comprendre de nombreux accents... It doesn't sound natural at all. An idiomatic formulation would be more along the lines of: Le recours à un corpus [de données] si riche et varié est ce qui permet au système de comprendre…

Gonna have to agree with the other reply, as a french-canadian, except for "servir comme un point de départ" which should be "servir de point de départ", that all sounds perfectly fine.

Re: Whisper – open source speech recognition by OpenAI

#438

Post by kurisufag »

jjoonathan wrote: post #372
NaturalPhallacy wrote: post #310

Earlier quoted context omitted.

This is called "a fishing expedition" and is wildly unconstitutional in the US. > The right of the people to be secure in their persons, houses, papers, and effects, against unreasonable searches and seizures, shall not be violated, and no Warrants shall issue, but upon probable cause, supported by Oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized.

Yes, it is wildly unconstitutional, but in practice don't the courts endorse the asinine "it's not a search unless we find something" argument from the NSA? Power always just finds a way to rationalize what it wants to do.

see: Operation PRISM

Re: Whisper – open source speech recognition by OpenAI

#439

Post by bambax »

mazork wrote: post #436
bambax wrote: post #61

Earlier quoted context omitted.

Little details. The second sentence is really bizarre: > Nous établissons que l'utilisation de données d'un tel nombre et d'une telle diversité est la raison pour laquelle le système est à même de comprendre de nombreux accents... It doesn't sound natural at all. An idiomatic formulation would be more along the lines of: Le recours à un corpus [de données] si riche et varié est ce qui permet au système de comprendre…

Gonna have to agree with the other reply, as a french-canadian, except for "servir comme un point de départ" which should be "servir de point de départ", that all sounds perfectly fine.

If this is actually "good" or even acceptable French Canadian, then it's a different language from French (and the blog post should mention it).

I kind of doubt it though -- the speaker doesn't have a Canadian accent (which is hard to miss), and in my (admittedly limited) experience, French Canadian isn't that different from French.

Re: Whisper – open source speech recognition by OpenAI

#440

Post by jefftk »

bscphil wrote: post #399
jefftk wrote: post #68

Earlier quoted context omitted.

This is a big dispute right now: OpenAI and other AI companies generally take the position that models learning from data does not make the output of the models a derivative work of that data. For example, GitHub Co-pilot uses all publicly available GitHub code regardless of license, and DALLE-2/StableDiffusion/etc use lots of non-free images. I don't think this has been challenged in court yet, and I'm very curious…

> models learning from data does not make the output of the models a derivative work of that data Most of the debate seems to be happening on the question of whether everything produced by models trained on copyrighted work represents a derivative work. I argue that at the very least some of it does; so the claim said to be made by the AI companies (see quote above) is clearly a false one. We're in a weird place now…

That's right: the model is definitely capable of creating things that are clearly a derivative work of what they were trained on. But this still leaves two questions:

* Does the model require a copyright license? Personally I think it's very likely a derivative work, but that doesn't necessarily mean you need a license. The standard way this works in the US is the four factors of fair use (https://copyright.columbia.edu/basics/fair-use.html) where Factor 1 is strongly in favor of the model being unrestricted while 2-4 are somewhat against (and in some cases 4 is strongly against).

* Is all output from the model a derivative work of all of the input? I think this is pretty likely no, but unclear.

* Does the model reliably only emit derivative works of specific inputs when the user is trying to get it to do that? Probably no, which makes using one of these models risky.

(Not a lawyer)

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