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> even on an A100 40GB for the 3 billion parameter model

Would any of the models run on something like a raspberry pi?

How about a smartphone?



They're going to be releasing a few more smaller models, as small as 150M

That said if you want something to use today on a Pi you should check out Kokoro


What kind of binary do you run Kokoro with for audio output


I use sherpa-onnx, which is great because it also does Piper without any dependencies that recent python versions get angry about.


Is there some sort of better tutorial for sherpa-onnx? I tried looking into it but it seemed quite complex to get going, last I checked.


I am one of the authors of sherpa-onnx. Can you describe why you feel it is complex? If you use Python, all you need is to run pip install sherpa-onnx, and then download a model and use the example code from the folder python-api-exmaples


Hi, I vouched your comment since it was dead, presumably because yours is a new account. I'm looking to use it in a Flutter app, possibly with flutter_rust_bridge FFI if needed, so I was wondering how to do that, as well as where to get the models and how to use them. I didn't see any end to end example in the docs.


I used FFI in the past (C header) but sherpa-onnx is available on pub.dev these days. Look for "sherpa_onnx".

You can also point sherpa_onnx in your pubspec.yaml file to a local dir (after cloning the repo somewhere on your file system) or point to a specific git commit hash, and don't forget to specify the path because its not the root of the repo. Here's a link to the dir of the flutter package https://github.com/k2-fsa/sherpa-onnx/tree/master/flutter.

The downloads of compatible models can be found at their GitHub Releases but tbh it's a bit of a strange setup IMO. Here's the page for TTS models for example: https://github.com/k2-fsa/sherpa-onnx/releases/tag/tts-model...

That same "release" (page) gets updated from time to time with new models. Use a bookmark :p

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@csukuangfj, thanks for sharing the hard work. Nice to see you here.


You can run it with Python or in the browser with WASM




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