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Whisper – open source speech recognition by OpenAI

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Re: Whisper – open source speech recognition by OpenAI

#91

Post by Snitch-Thursday »

TaylorAlexander wrote: post #31

Hey this looks great! I like to record audio notes while driving in my car after work, to kind of decompress my thoughts from the day. But I never go back and listen as they can be long and meandering. Sometimes in the audio log I will sum up my thoughts, but this might be 20 minutes in and hard to find. I really wish I had transcriptions so I could easily scan the full contents. I have tried Mozilla Deepspeech (I do…

Google's recorder app for android will let you record audio files and make some transcriptions, right on the device.

Re: Whisper – open source speech recognition by OpenAI

#92

Post by leobg »

soheil wrote: post #75
pen2l wrote: post #5

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…

Quoted post unavailable.

Seems like this is an urban legend.

https://www.reddit.com/r/IsItBullshit/comments/2rztov/isitbu...

Re: Whisper – open source speech recognition by OpenAI

#93

Post by jfoster »

solarmist wrote: post #52
jfoster wrote: post #10

It seems like OpenAI are finally living up to their name for once with this release? Anything I'm missing? From what I can gather: 1. Includes model weights. I can't find the URL, but they reference them enough and have a CLI tool, so I presume I just haven't found them yet. 2. Includes code: https://github.com/openai/whisper 3. Released under MIT License: https://github.com/openai/whisper/blob/main/LICENSE

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.

Re: Whisper – open source speech recognition by OpenAI

#94

Post by lunixbochs »

gok wrote: post #19

Comparing this model's word error rates to the state of the art [1] on a few common test sets: Whisper SoTA LibriSpeech test-clean 2.7% 1.8% LibriSpeech test-other 5.6% 2.9% Switchboard 13.1% 4.9% CallHome 15.8% 9.5% The authors do explicitly state that they're trying to do a lot of fancy new stuff here, like be multilingual, rather than pursuing just accuracy. [1] https://github.com/syhw/wer_are_we

I suspect Whisper is more robust than other "SOTA" models, but this release is likely leaving a fair bit of accuracy on the table considering the amount of resources OpenAI is capable of throwing at training it.

Comparing the readily available test sets from the paper to some of my personal robust models (for the Talon models, this is greedy decoding, no language model):

                       Talon  Talon  Talon  Whisper  wav2vec 2.0
                       28M    300M   1B     Large    960h
    librispeech clean   3.21   2.52   2.40   2.7      2.7
    librispeech other   8.21   6.56   5.63   5.6      6.2
    common voice       13.88  11.65   8.86   9.5     29.9
    tedlium             7.51   6.55   5.47   4.0     10.5
I have a battery of more difficult tests on hand (including adversarial tests, and diverse accent-specific metrics). I'll look at running these tests on each of the Whisper model sizes and following up with a larger comparison.

Re: Whisper – open source speech recognition by OpenAI

#95

Post by ignoramous »

solarmist wrote: post #52
jfoster wrote: post #10

It seems like OpenAI are finally living up to their name for once with this release? Anything I'm missing? From what I can gather: 1. Includes model weights. I can't find the URL, but they reference them enough and have a CLI tool, so I presume I just haven't found them yet. 2. Includes code: https://github.com/openai/whisper 3. Released under MIT License: https://github.com/openai/whisper/blob/main/LICENSE

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.

> This kind of model is harder to abuse, so I guess it passed their internal checks much more easily.

The version I choose to believe: stability.ai ate DALL-E for lunch, and that woke them up.

Re: Whisper – open source speech recognition by OpenAI

#96

Post by Kuinox »

The5thElephant wrote: post #26

How is it Apple, Google, or Microsoft are not further ahead of the game on speech recognition like this? They have the resources to hire the best ML researchers and throw tons of computing hours at it, yet Siri, Google, and Cortana continue to struggle to get anywhere near this level of comprehension.

OpenAI is owned by Microsoft FYI.

Re: Whisper – open source speech recognition by OpenAI

#98

Post by jfoster »

bigyikes wrote: post #82
dwohnitmok wrote: post #73

Earlier quoted context omitted.

> I can understand not releasing GPT-3, even if I disagree with the decision. Why do you disagree?

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.

Re: Whisper – open source speech recognition by OpenAI

#99

Post by golem14 »

thfuran wrote: post #85
anigbrowl wrote: post #74

Earlier quoted context omitted.

It was already better. I edit a podcast and have > a decade of pro audio editing experience in the film industry, and I was already using a commercial AI transcription service to render the content to text and sometimes edit it as such (outputting edited audio). Existing (and affordable) offerings are so good that they can cope with shitty recordings off a phone speaker and maintain ~97% accuracy over hour-long conve…

>~97% accuracy over hour-long conversations. I'm sure it's been an absolute godsend for law enforcement 97% accuracy means roughly three or four errors per minute of speech. That seems potentially extremely problematic for something like law enforcement use where decisions with significant impact on people's day and/or life might be made on the basis of "evidence".

One would think that the few crucial bits of information gleaned are listened to manually, and the machine translation is not the only thing the judge or a jury sees.

Re: Whisper – open source speech recognition by OpenAI

#100

Post by magicalhippo »

TaylorAlexander wrote: post #37
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 seems these days that language-oriented models are commonly becoming multilingual by default. There are a lot of common threads when understanding sentence construction between different languages. French and English have different rules but they will still have things like nouns, adjectives, subjects, prepositions, etc. It seems that by training models on many languages you get both a more robust understanding of…

Would a multilingual modal perhaps also be better at understanding non-natives speech?
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