I'm thinking of releasing a plugin in for Unity to that can be used to match a phrase to an action. Seeing Whisper is making me think I should include a way to use voice and not just text.
Whisper – open source speech recognition by OpenAI
181–190 of 508 posts
Re: Whisper – open source speech recognition by OpenAI
#182Earlier quoted context omitted.
This isn't exactly a hard story to fact check. There is 0 evidence for this in either the reddit thread or really anywhere? If they were willing to lie about the company name why not just lie about the beef in their burgers it would be equally scandalous
The company name could be 100% legit, there is nothing stopping you from a forming a company with that name and not even sell beef.
You are right, it could be. The problem is that its the kind of thing that would be almost impossible to disprove if it were false. So you can always raise doubts about a supposed disproof.
But it'd be really easy to prove if it were true and noone has offered proof. And there've been plenty of people who've looked for such proof, afaict.
My default assumption in such cases is that it is likely false.
Re: Whisper – open source speech recognition by OpenAI
#183[deleted]
Re: Whisper – open source speech recognition by OpenAI
#184Earlier quoted context omitted.
This seems to not be true for McDonald: https://www.snopes.com/fact-check/mcdonalds-100-beef/
Quoted post unavailable.
In the US, for a while I remember we had billboards advertising McDonald's burgers as being "1 % beef". Because the hamburgers were of course circular, it looked kind of like "100%".
I remember thinking that surely an image of a hamburger does not legally constitute a zero.
Re: Whisper – open source speech recognition by OpenAI
#185Comparing 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…
I'm looking forward to your comparison. It's really hard to make sense of how good this model actually is without being an expert in the area.
Re: Whisper – open source speech recognition by OpenAI
#186This really makes me want to build a Amazon Echo/Google Nest/etc replacement that's open hardware, open source and most importantly recognises voice completely offline. I find that I don't use these smart devices for much more than setting timers anyway so this seems like an easy project. I just wonder what system requirements Whisper has and whether there are open source voice recognition models that are specificall…
I really want all this too. The smallest model is ~80mb and the largest is 3gb. Not sure about system requirements yet; but models that small suggest this may be doable locally on a single board computer.
Edit: According to this comment[0] the base model runs in real time on an M1 CPU. The tiny model apparently decodes an audio file twice as fast. These are promising results.
Re: Whisper – open source speech recognition by OpenAI
#187Earlier quoted context omitted.
There's a --device flag you can pass. I've been trying to get `--device cuda` to work on my Windows machine and it's saying that torch wasn't compiled with CUDA. Trying to figure out what's going on there. And on the M1, supposedly PyTorch has support for hardware acceleration using MPS (Metal Performance Shaders, announced here https://pytorch.org/blog/introducing-accelerated-pytorch-tra... ) but when I tried `--dev…
Yep, same for me, on M1 after enabling MPS (with `model.to("mps")`) it just either SIGSEGV or SIGABRTs every time with that line. The extremely unclean nature of the abort is making it hard to debug :(
I noticed the size seems to correspond to the model. With a large model, the error is tensor. With tiny, it's tensor, and with medium it's tensor. It also seems like a bad thing that those are f16's but the "expected" data is f32.
Re: Whisper – open source speech recognition by OpenAI
#188I know this isn't a tech support forum but maybe someone here knows. I'm attempting the sample python code from the github and almost get a transcription running on my work laptop without a GPU, but I run into this error message:
>>> result = whisper.decode(model, mel, options)
Traceback (most recent call last):
[snip]
RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'
It looks like a Torch error, is there some twiddling with "options" I can do to get it to run?
Re: Whisper – open source speech recognition by OpenAI
#189Re: Whisper – open source speech recognition by OpenAI
#190Earlier quoted context omitted.
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…
17x realtime on a 3090
I did some basic tests on CPU, the "small" Whisper model is in the ballpark of 0.5x realtime, which is probably not great for interactive use.
My models in Talon run closer to 100x realtime on CPU.