silero-vad
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snakers4
Feature request - Finetuning or Pretraining for Urdu
Please tell me if I can fine-tune this model on urdu dataset or train from scratch with same architecture for Urdu?? In short please elaborate the architecture of this model and how it is so fast.
Thank you so much for the reply Alexander.Can you please tell me the architecture it is based on or how you achieved this perfection in speed and accuracy both at the same time?And also the expected time in days or months for a new release you're talking about as I'm greatly impressed by and looking for this over last few months so can't wait more.If you elaborate the architecture, I can contribute alot in your project.
On Wed, 17 Jan 2024 at 20:34, Alexander Veysov @.***> wrote:
This year we will probably share a fine-tuning recipe
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Any news on this??? For me this model turned out very much useless without fine-tuning, because I get too much false positives and that makes the system unstable. Will stick to other options until the fine-tuning recipe is available.
No updates yet. Exactly, any model like this is worthless without having control of finetuning or modifications.
On Sun, 17 Mar 2024, 19:38 filtercodes, @.***> wrote:
Any news on this??? For me this model turned out very much useless without fine-tuning, because I get too much false positives and that makes the system unstable. Will stick to other options until the fine-tuning recipe is available.
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As a first step - we released the dataset - https://github.com/snakers4/silero-vad/tree/master/datasets
This is excting news, I'm looking forward to the finetuning scripts being released
The new VAD version was released just now - https://github.com/snakers4/silero-vad/issues/2#issuecomment-2195433115
It supports more than 6,000 languages now
Fine-tuning code will be released soon
The new VAD version was released just now - #2 (comment)
It supports more than 6,000 languages now
Fine-tuning code will be released soon
Looking forward to try it out! Is the state in/out basically combined c and h? I see it's tensor: float32[2,?,128], while c and h were float32[2,batch,64]) each.