I understand that it uses ML models. My point is that it is an end-user application making use of such models. It is recording audio, passing it to the model, and pasting in the resulting text to the focused input. The fact that the middle step happens to involve an ML model is not really intrinsic to anything the app does. If there was a good speech to text program that did not use ML, the app could use that instead and not really be any different.
To be fair on the other side there is a fair lack of specific ML inference libraries in Rust, and this project is pushing some of that forward with Parakeet at the very least. The Rust library `transcribe-rs` came from it and hopefully will support more models in the future.
While certainly it's not an ML project in the sense of I am not training models, the inference stack is just as important. The fact is the application does do inference using ONNX and Whisper.cpp.
Repo says:
CPU-optimized speech recognition with Parakeet models
I understand that it uses ML models. My point is that it is an end-user application making use of such models. It is recording audio, passing it to the model, and pasting in the resulting text to the focused input. The fact that the middle step happens to involve an ML model is not really intrinsic to anything the app does. If there was a good speech to text program that did not use ML, the app could use that instead and not really be any different.
To be fair on the other side there is a fair lack of specific ML inference libraries in Rust, and this project is pushing some of that forward with Parakeet at the very least. The Rust library `transcribe-rs` came from it and hopefully will support more models in the future.
While certainly it's not an ML project in the sense of I am not training models, the inference stack is just as important. The fact is the application does do inference using ONNX and Whisper.cpp.