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How heavy in inference on this? The model would easily fit on many microcontroller modules, I wonder if they could run it?

This is amazing, the quality blow my mind for such small model! I just replaced my old onnx model with yours!

here my implementation with speech dispatcher and server: https://github.com/skorotkiewicz/inflect-speechd

thanks for shearing!

Couple highlights:

> Complete local text-to-waveform speech synthesis under 10M parameters.

In case, like me, you hoped "complete" voice might mean both stt and tts. Not to speak poorly of it, just clarifying.

> English only, with one fixed male voice. This is not zero-shot voice cloning.

(And then a bunch of statements on limitations that I read as 'quality can be spotty but if you play with it it should be fine') But like. In <10M params I'm not judging:)

I think we need more neurons in the human brain than parameters in this model for speech. I wonder what it says about the human brain vs LLM efficiency.

I keep seeing tts stories here. Is it just an interesting subset of the llm world, or is there a huge use case I’m somehow missing?

The inflections are weird but this doesn't sound like a robot. Not bad!

This is impressive. I wish there were a voice clone option.

this is extremely encouraging for individuals/small companies being able to train pareto-frontier TTS models (specifically compute required to run vs quality of model output)

I'd love to hear it but it seems your quota is exhausted.

amazing! was looking for something similar

Amazing quality for small size, but definitely not that enjoyable to listen to.

IMHO, its at about the same quality level of historic TTS tools.

amazing quality for such small size!

Alternative title: Text to speech in 9.36M, English only.

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