Article URL: https://workshop.cjpais.com/projects/transcribe-cpp Comments URL: https://news.ycombinator.com/item?id=48963879 Points: 508 # Comments: 100

transcribe.cpp is a ggml based transcription library which supports all the latest transcription models. Every model published under the handy-computer HF org has been numerically validated and WER tested to match the reference implementation. It's accelerated everywhere. I'm the author and maintainer of Handy. This library grew from the pains of distributing a cross-platform speech-to-text application to many people. This is a v0.1.0 library which means that there are some rough edges which I cannot discover alone! Please report them, and let's fix them together! Let me say this. I think distributing a cross-platform application with the current ASR inference stack is terrible. You've basically got whisper.cpp and ONNX. That's it. You could roll MLX in for Apple devices, but now you've to support two different engines and port models to each. I've been a fan of ONNX for getting model support into Handy quickly, but so much performance is left on the table with CPU only. There are a few random libraries out there which claim to support a lot of models, but they have unknown authors, and unknown testing, as far as I've seen. They leave me with more questions than answers. When will they stop maintaining this library? Has the creator thought about bindings so you can actually use it in a real desktop or mobile app? Is this effectively demo code? Have they benchmarked it? Is it faster than ONNX? And this is what led to transcribe.cpp. As Handy's maintainer I needed a library I could trust. Where I could download a file and run inference on it. Where I can know that the inference coming from the model in the engine is as good as the reference implementation. The inference should run on the GPU for the best performance. It should be trivially embeddable in Handy, it cannot be a huge pytorch lib. It must be something that works on Mac, Windows, and Linux. And ggml seemed like by far the best way forward. It has a strong community, and a great distribution story.