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Whisper – open source speech recognition by OpenAI

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Re: Whisper – open source speech recognition by OpenAI

#441

Post by jefftk »

petercooper wrote: post #159
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…

I think it might be even less problematic with something like Whisper than with DALLE/SD? Merely consuming data to train a system or create an index is not usually contrary to the law (otherwise Google wouldn't exist) – it's the publication of copyright content that's thorny (and is something you can begin to achieve with results from visual models that include Getty Photos logo, etc.) I think it'd be a lot harder to…

They're not just training a system but publishing the trained system

Re: Whisper – open source speech recognition by OpenAI

#442

Post by Kirkman14 »

I've been trying Whisper on my old setup (Mac Pro 2012 running Mojave, with Radeon RX 580), and it's a pretty amazing tool.

Unfortunately my system is not ideal for today's AI tools. Whisper runs only on the CPU, and it's slow.

I know PyTorch recently added Metal support, but only for M-based Macs. Has anyone found a way to make it work with Intel Macs?

Re: Whisper – open source speech recognition by OpenAI

#443

Post by toss1 »

Tomis02 wrote: post #390
toss1 wrote: post #123

Like every model I've seen there is something like this: >>A decoder is trained to predict the corresponding text... Prediction of expected text in the context of the previous text. While this is valuable in casual transcription, it can be extremely dangerous in serious contexts. From personal experience, having given a deposition with an "AI" transcription, it will literally reverse the meanings of sentences. This i…

I've been saying this for years. Current "AI" algorithm are fundamentally flawed because they rely on a statistical approach. This works moderately well for some use cases but it will rarely give you 100% confidence. Good luck with self-flying planes or self-running nuclear power plants.

>>Current "AI" algorithms are fundamentally flawed because they rely on a statistical approach.

YES! The old joke about "Artificial Stupidity" is actually more true than anyone realized.

These statistical so-called-AI systems actually work to actively REMOVE or sanitize out any unexpected information, making it all conform with the EXPECTED results from the training set.

This not only REMOVES the most high-information 'surprising' or unexpected nuggets, it actively HIDES them. When something unexpected comes up, it gets force fit into the expected prediction algorithms and output as if it were good.

I'm not saying that there are no useful things that can be done with this technology — there is a LOT of mundane work out there to be done.

But, we will never get this type of "AI" saying "Huh, that's odd, I wonder why that is?", which is exactly the kind of observation that leads a prepared and fertile mind to great discoveries.

Re: Whisper – open source speech recognition by OpenAI

#444

Post by magicalhippo »

olao99 wrote: post #430
magicalhippo wrote: post #278

Earlier quoted context omitted.

It's struggling with Norwegian. Which I guess isn't shocking. The large model performs a fair bit better than the small, though neither is "good". Though I assume the amount of Norwegian it has been exposed to is fairly limited, so in that light I'm actually impressed as well. I tried it on a news segment from the radio[1], this is the large model output: [00:14.000 --> 00:17.200] En skamløs krenking av FN pakten. [0…

How are you getting the transcription of the NRK episode? I am learning Norwegian and often struggle to find reliable transcriptions for audio where the text exactly matches the audio (often subtitles are heavily edited compared to what's actually being said)

The stuff I quoted was listed as an abstract of sorts for the episode. I know NRK is very good at providing subtitles for their TV productions, but as you say they're abbreviated.

I'm guessing maybe audio books along with the actual books would be the best source for such? I mean there's Mozilla Voice, but it's quite limited in the Norwegian department and perhaps not quite as interesting as an audio book would be.

Re: Whisper – open source speech recognition by OpenAI

#445

Post by [deleted] »

spullara wrote: post #124
jfoster wrote: post #98

Earlier quoted context omitted.

All of your examples are limited in some way, but GPT-3 wouldn't have any meaningful limits. Stable Diffusion: Marks images as AI-generated. (invisible watermark, but still, it's there) Photoshop: Requires time & effort from a human. Fake news website: Requires time & effort from a human.

SD only does that if you don't delete the line of code that does it...

[deleted]

Re: Whisper – open source speech recognition by OpenAI

#446

Post by nullc »

jfoster wrote: post #98
bigyikes wrote: post #82

Earlier quoted context omitted.

I don’t see how GPT-3 is any more dangerous than Stable Diffusion, Photoshop, that fake news website the crazy person you’re friends with on Facebook really likes, or any of the number of other tools and services that can be used to generate or spread fake information.

All of your examples are limited in some way, but GPT-3 wouldn't have any meaningful limits. Stable Diffusion: Marks images as AI-generated. (invisible watermark, but still, it's there) Photoshop: Requires time & effort from a human. Fake news website: Requires time & effort from a human.

It would be pretty trivial to have an invisible watermark in GPT3 output-- though you don't really need one: just score text with gpt3 to find out if it was likely gpt3 generated or not.

Re: Whisper – open source speech recognition by OpenAI

#448

Post by bruckie »

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?

I think it's just Google for backup, or other apps via Android's standard sharing sheet. You can read the details here: https://support.google.com/pixelphone/answer/9516618?hl=en

Re: Whisper – open source speech recognition by OpenAI

#449

Post by phren0logy »

nshm wrote: post #168
BasilPH wrote: post #120

Any opinions on what this means for speech-to-text companies like rev.ai and assmembly.ai ? We've tested open source solutions for s2t, like kaldi, but the quality was not good enough. However, one of the main advantages of a service like assembly.ai to me was that they offer sentence splitting in form of punctuation and speaker detection, which Kaldi does not. So I guess I answered my own question to some degree: A…

You can apply public punctation model from Vosk on top of Kaldi output, you can also get speaker labels with existing open source software. On quick video transcription test this model is more accurate than AssemblyAI and Rev AI. It will be harder for them to sell pure ASR now. Some more business-oriented applications will still be important though, for example ASR as part of callcenter analytics solution or as a par…

Rev AI will also create a transcription separated by multiple speakers, which it doesn't appear Whisper can do (yet). I expect that Whisper will overtake the alternatives soon, given that it's open source, but today it's not there yet.

Re: Whisper – open source speech recognition by OpenAI

#450

Post by u8 »

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…

I had to do a lot of manual transcription in Journalism school. Using a tool like Descript saved HOURS of my life. Generally it was 80% accurate, but going over an two-hour-long recording again at 3x speed while reading over the transcript, fixing errors from memory or pausing took a five hour job down to 30-40 minutes. Either way, somebody is going to have to listen to the recording. This just removes a layer of grunt work.
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