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Not in the library itself, it’s pure inference. Some models have this trained out of them anyhow. Otherwise this is a post processing task which is not really inference
So it looks like something that app would be doing, or run another model over the output to smoothen out and remove these things?
The post makes it seem like ONNX is CPU only. I've used ONNX runtime to run models on Nvidia GPUs. The runtime can even dispatch to TensorRT. I'm not sure what the performance is on Apple hardware so maybe that was the motivation for moving away from ONNX.
For anyone looking to build on top of this. I have tried a few different STT systems, and they accurately capture what I am saying. Unfortunately, they don't support the reasonable workflow I want to open an office document, for example, and start talking. And I want the software to continuously type what I am saying at the cursor with minimal latency. The continuous part is crucial. Many software will paste whatever…
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What would be the benefit of this, besides from looking cool?
In iOS this means you can edit the text as it’s being transcribed. For example, I want to dictate a todo list and after each item I can hit enter to go to the next line.
I'm using Handy on macOS and love it. Unfortunately, hotkeys still doesn't seem to work on Wayland, which make it unusable.
Yeah I’m working on it, Linux is a big pain point especially Wayland Once things are more or less ironed out on MacOS and Windows a lot of attention will be turned towards Linux I know a lot of Linux PRs are open it just takes me so long to get around and test them. And often multiple different implementations trying to fix similar issues which is a lot of overhead sometimes
Is there any way people can help? From your last sentence, it sounds like another PR isn't it and the opposite might be needed. But would love to contribute with testing if helpful. I'm regularly jumping between XFCE, KDE, GNOME, Niri, etc..
Is transcription a form of _inference_ though? I mean I see the word being thrown around and I understand what it means (or at least I think I do) in context of LLMs doing the thing that they do -- intelligently predict the next token, but do speech-to-text models do that?