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How much work would it be to use the C++ ONNX run-time with this instead of Python? Is it a Claudeable amount of work? The iOS version is Swift-based.
shouldn't be hard. what backend/hardware are you interested in running this with? i'll add an example for using C++ onnx model. btw check out roadmap, our inference engine will be out 1-2 weeks and it is expected to be faster than onnx.
Is it English only?
as of now its english only. the training for multilingual model is underway and should be out in April! what languages are you most interested in? Right now, we are providing deployments for custom languages + voices through support form on the github.
I'm still looking for the "perfect" setup in order to clone my voice and use it locally to send voice replies in telegram via openclaw. Does anyone have auch a setup? I want to be my own personal assistant... EDIT: I can provide it a RTX 3080ti.
This is awesome, well done. Been doing lot of work with voice assistants, if you can replicate voice cloning Qwen3-TTS into this small factor, you will be absolute legends!
thanks a lot, our voice cloning model will be out by May. we're experimenting w some very cool ways of doing voice cloning at 15M but will have a range of models going upto 500M
A very clear improvement from the first set of models you released some time ago. I'm really impressed. Thanks for sharing it all.
One of the core features I look for is expressive control. Either in the form of the api via pitch/speed/volume controls, for more deterministic controls. Or in expressive tags such as [coughs], [urgently], or [laughs in melodic ascending and descending arpeggiated gibberish babbles]. the 25MB model is amazingly good for being 25MB. How does it handle expressive tags?
thank you so much. Right now, it cannot handle expressive tags. what kind of tags would be most helpful according to you?
Great stuff. Is your team interested in the STT problem?