301–310 of 508 posts
Is it feasible to use this for Talon-like voice-driven computer usage?
Talon's speech engine backend is modular, with Dragon, Vosk, the WebSpeech API, and Talon's own engine all used in different ways by users.
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…
Just tested this on some developer podcasts which usually fail hard given they're full of technical jargon, brand names, etc. Whisper is a revolution! It's picking up terms like Heroku, DigitalOcean, GitHub, ECS, AWS, etc. and capitalizing properly - something nothing else did unless you provided a whole pile of guiding vocabulary.
Did these podcasts have transcripts? You might be inadvertently evaluating it on data that it was trained on, which is basically cheating. Even if not, it might be trained on similar podcasts. Judging how good these kinds of models are is really hard.
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Are you thinking about reimplementing Mycroft? The Mycroft has done a lot of cool and important work in the field to ship an actual personal assistant product (stuff like wake word detection).
hah, of course someone had the idea already and executed on it. But yeah, basically that but without the screen (probably would go a long way to decrease the cost, $299 is pretty steep for such a device)
[0]: https://github.com/Sheepybloke2-0/trashbot - It was called trashbot because the final implementation was going to look like oscar the grouch in a trashcan displaying the reminders.
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Siri and Cortana have to run at least in real time, with reasonable compute resources. Probably faster than real time when the audio gets shipped off to the cloud and transcribed there. This model can't do that (in the "large" version, which the examples use). Also, you are comparing Whisper's highlight reel with everyday performance of other models. Nobody shows their weaknesses in their highlight reel.
Someone else in this thread[0] said Whisper was running at 17x real time for them. So, even a weak machine might be able to do an acceptable approximation of real time with Whisper. Also, I feel like shipping to the cloud and back has been shown to be just as fast as on device transcription in a lot of scenarios. Doing it on device is primarily a benefit for privacy and offline, not necessarily latency. (Although, in…
Like the colab notebook linked on the official Whisper github project page?
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I wouldn't really say Stable Diffusion marks images as AI-generated. There's a script in the Stable Diffusion repository that will do that, but it's not connected to the model itself in a meaningful way. I use Stable Diffusion a lot and I've never touched this script. https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a...
What "script" are you using for doing txt2img? The watermark function is automatically called when you use the CLI in two places, https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a... and https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a... Trivial to remove, I give you that. But AFAIK, the original repository + most forks put the watermark automatically unless you've removed it on your own.
almost all of the 'low-vram' variant forks either have an argument to turn off the watermark (it saves a bit of memory) or come with it disabled all together.
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Someone else in this thread[0] said Whisper was running at 17x real time for them. So, even a weak machine might be able to do an acceptable approximation of real time with Whisper. Also, I feel like shipping to the cloud and back has been shown to be just as fast as on device transcription in a lot of scenarios. Doing it on device is primarily a benefit for privacy and offline, not necessarily latency. (Although, in…
> I really wish there was an easy demo for Whisper that I could try out. Like the colab notebook linked on the official Whisper github project page?
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Maybe you could run the text through a grammar checker to identify the errors.
That might work if people were required to speak grammatically.
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>~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".
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…
>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.