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I'm so amazed to find out just how close we are to the start trek voice computer.

I used to use Dragon Dictation to draft my first novel, had to learn a 'language' to tell the rudimentary engine how to recognize my speech.

And then I discovered [1] and have been using it for some basic speech recognition, amazed at what a local model can do.

But it can't transcribe any text until I finish recording a file, and then it starts work, so very slow batches in terms of feedback latency cycles.

And now you've posted this cool solution which streams audio chunks to a model in infinite small pieces, amazing, just amazing.

Now if only I can figure out how to contribute to Handy or similar to do that Speech To Text in a streaming mode, STT locally will be a solved problem for me.

[1] https://github.com/cjpais/Handy

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Happy to answer questions about this (or work with people on further optimizing the open source inference code here). NVIDIA has more inference tooling coming, but it's also fun to hack on the PyTorch/etc stuff they've released so far.
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I've been using Parakeet V3 locally and totally ancedotaly this feels more accurate but slightly slower
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I liked Parakeet v3 a lot until it started to drop whole sentences, willy-nilly.
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Yeah, I think the multilingual improvements in V3 caused some kind of regression for English - I've noticed large blocks occasionally dropped as well, so reverted to v2 for my usage. Specifically nvidia/parakeet-tdt-0.6b-v2 vs nvidia/parakeet-tdt-0.6b-v3
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Parakeet is really good imo too, and it's just 0.6B so it can actually run on edge devices. 4B is massive, I don't see Voxtral running realtime on an Orin or fitting on a Hailo. An Orin Nano probably can't even load it at BF16.
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Came here to ask the same question!
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