So it's 100% better than Siri's speech dictation, I see
Whisper – open source speech recognition by OpenAI
371–380 of 508 posts
Re: Whisper – open source speech recognition by OpenAI
#372Earlier quoted context omitted.
I imagine a certain percentage of a given population is on a voice call at any one time. 1. Set up a computer with voice recognition software that flags certain patterns. 2. Connect computer to voice call communication network. 3. Configure computer to switch between calls every x number of seconds. Think of it like a system to generate leads for law enforcement that can be integrated with other systems to produce th…
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.
Re: Whisper – open source speech recognition by OpenAI
#373I tried running it in realtime with live audio input (kind of). If you want to give it a shot, you can find the python script in this repo: https://github.com/tobiashuttinger/openai-whisper-realtime A bit more context on how it works: The systems default audio input is captured with python, split into small chunks and is then fed to OpenAI's original transcription function. It tries (currently rather poorly) to detec…
Haven’t tried it yet but love the concept!
Have you thought of using VAD (voice activity detection) for breaks? Back in my day (a long time ago) the webrtc VAD stuff was considered decent:
https://github.com/wiseman/py-webrtcvad
Model isn’t optimized for this use but I like where you’re headed!
Re: Whisper – open source speech recognition by OpenAI
#374Earlier quoted context omitted.
Did you find out :D?
It was doing it slowly , but hadn't got to the insane bit when I killed it to try and get it working with CUDA, so I had to do some digging and it turns out I need a version of pytorch with CUDA enabled, and so I had to go and install Anaconda, and now now conda is stuck trying to "solve" my environment to install pytorch with CUDA. So...probably? Pre-post edit: I can't get it to work. I've installed pytorch with cud…
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.
Re: Whisper – open source speech recognition by OpenAI
#375I ran it on some fire department radio recordings from scanners on Broadcastify. It did remarkably well.
For reference, GCP's Speech-to-Text didn't detect any speech from this clip -- even when using the enhanced phone model.
Re: Whisper – open source speech recognition by OpenAI
#376Earlier quoted context omitted.
I imagine a certain percentage of a given population is on a voice call at any one time. 1. Set up a computer with voice recognition software that flags certain patterns. 2. Connect computer to voice call communication network. 3. Configure computer to switch between calls every x number of seconds. Think of it like a system to generate leads for law enforcement that can be integrated with other systems to produce th…
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.
Are you sure about that? [0]
Besides I wasn't talking about the USA when I said this. I was remembering a conversation I once had with a person who worked as a technician in a telephone exchange.
Re: Whisper – open source speech recognition by OpenAI
#377A notebook is available to try with your microphone on Colab here: https://colab.research.google.com/drive/1nBZ-pDIaIi3N1DIIXvJ... I'm surprised by the quality on non-English languages, given that 80+% of the training data is English, and the rest is split between tens of languages.
How do you get this to translate instead of just transcribe?
Just specify language and record an audio in another language.
>result = model.transcribe("audio.wav", language="english")
Re: Whisper – open source speech recognition by OpenAI
#378Earlier quoted context omitted.
"Secret listening machines everywhere" was a pretty big thing in East Germany. It's also the central theme of the movie The Lives of Others. Of course, the ability to scale this more cheaply (throwing more compute at it, instead of more people) is somewhat scary, but it's not really introducing a new capability. Especially since you still have to do something with the transcript. An AirBnB landlord who reads the tran…
I'd argue that cheap, pervasive, always-on surveillance with a backlog of searchable transcriptions is a qualitatively different capability.
Exactly - imagine when we get to the point where, regardless of your "crime", your punishment is 'augmented' by the "thing that you said in the past" AND when it starts to be able to connect to APIs of your social/whatever accounts and AI-Auto-Cancel you....
Basically digital assassination.
Re: Whisper – open source speech recognition by OpenAI
#379Earlier 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…
>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%…
+1. There is a widespread "metric fallacy" or "task fallacy" going around. Models of course optimize for metrics, so they tend to perform well on those related metrics.
Humans, however, are not simply metric optimizers. Though it's always in the interest of those corporations producing metric optimizers (i.e. models) to paint humans as such, so their models shine in comparison. They want humans to look like bad machines, so it looks like they should be automated. Not to say they shouldn't in many cases, just that there's a clear one-sidedness in all corporate PR (and funded research, especially that research which is also PR).
All this to say that yes I agree with you. And if we humans don't want our unsustainable economic growth to turn us even more into machines (as our bureaucratic creep has done quite well thus far), we should fight such rhetoric that aims to paint humans simply as machines or task-doers.
Re: Whisper – open source speech recognition by OpenAI
#380Earlier 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…
Since you work on podcasts, do any open source transcription tools currently identity the speaker in the output? This would be particularly helpful for interviews.
Not sure about open source, but in general, automated transcription systems need a separate track for each different speaker. So for example, for a phone call with one person on each end, you need two separate channels (recording systems usually split them left/right on one stereo file).