491–500 of 508 posts
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What tools do you use to do this? I once hacked together an editor like this maybe a decade ago -- edit speech as text from OCR -- and sorely need one now. Alignment of video to text is a big problem for me too.
This can be done via https://www.descript.com/ You can edit video/audio by editing the transcript. You can even add/modify words that weren't originally there https://www.descript.com/overdub
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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".
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…
I 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 so…
I am running on work laptop not using GPU. (I'm running in docker). I just get warnings.warn("FP16 is not supported on CPU; using FP32 instead") And it works.
Tested this out in the span of a few hours and got a solution up and running to download the video from Youtube, spit out the transcription and upload the resulting transcription file externally. We're still missing a piece to upload directly to YouTube, but it's a start!
As a part of this experiment, we built out some templates that will allow anyone to play around with Whisper in our platform. If you're interested in seeing it, we built a video for doing the process with our templates [2], or directly with Python [3].
Hope someone finds this useful!
[1] https://www.shipyardapp.com [2] https://www.youtube.com/watch?v=XGr4v3aY1e8 [3] https://www.youtube.com/watch?v=xfJpGgyUkvM
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At the start, the "Nous établissons" part, for example. You wouldn't write that if you were starting scratch from French.
That's the first thing that I discovered when I visited Paris for the first time. No one says "Nous", there, ever. Perhaps the politicians, while giving a speech. Everyone else uses the more informal "On". I felt duped by my French classes.
It is often used for larger groups or when the group is not very personally connected. For instance when talking about your company doing something you will often use "nous". I would also use "nous" to refer to the whole list of invitees to a wedding. And in formal contextes like research papers, reports etc. You would never use "on", always "nous".
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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…
Re-reading the transcription, I guess I was a bit harsh by saying it's not "good". It gets most of it right, but it keeps messing up some key words. Like "regnstyr" (not a word) rather than "reinsdyr" (reindeer), or "Dagsnytten" rather than "Dagsnytt 18". It also didn't handle the hanging "... menn", instead thinking it was the start of the following sentence. Almost everyone would understand it was the end of the se…
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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.
My point is that the Python API is more interesting than the txt2img script, and it doesn't add any watermarks.
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> It's one model and in a non-strategic area where there are existing open source projects (Kaldi, DeepSpeech, ...). I can already tell this is much better than any of the existing open source projects with the exception of the wav2* sequence of projects and potentially nvidia's nemo.
Kaldi is an open, pluggable framework and is a ton more flexible and powerful than this. It's used by hundreds of teams, including a number of consumer tech companies you've heard of. They're not going to move to this over it. Especially because ASR is a living organism. You have to constantly update your language model as new people, ideas, and words move into the normal lexicon. As people start talking about "COVID…