I made a little python wrapper around it to serve an HTTP endpoint that’s OpenAI/elevenlabs compatible https://github.com/clayrosenthal/bootlegger

Quick link to the video where he demos it: https://www.youtube.com/watch?v=kMliOFYBiz4

This is awesome. I am trying to build a full scale ASR system within 20-25MB. Now that we have Claude code to run experiments, I have started running some experiments. Promising results so far. First realization is that you can capture the nuances of speech in just 3300 embedding vectors(786d). This sequence can be decoded with a small CTC system to get text. Next experiments are on reducing the 768 dimension space into a 64D space. Thats also show some promising results. Hooking up my system so that the agent blogs the results everyday[1]. So my research "claw" setup does the experiments and posts results which I check in the morning and adjust the experiment direction as needed. Its not fully automated yet, but almost there.

[1] https://blog.trulm.com/posts/speech-as-independent-parts/

Stt/tts systems always seem to me so promising, but I pretty much never use voice to interface with a computer. Sometimes instead of typing on my phone, I use a voice dictation. I would be keen to use voice to control Claude code, but I've always felt that the way I speak is different from the way I write good prompts.

Fishing for anecdotes here, does anyone have any good tts/stt experiences?

Do you have any accuracy benchmarks?

I’ve worked in this space. TTS in a small footprint isn’t the hard part —- it’s doing it accurately that’s hard.

Although for the use cases OP is targeting, lower accuracy may be good enough!

Wow, it seems like this might beat out flite for very-low-memory TTS? I ended up abandoning a project of mine because I couldn't get high enough quality or low enough memory usage out of flite, so I'm very excited to try this out.

Flite for comparison: https://github.com/festvox/flite

For TTS I wonder how this compares to nanotts[1] with the en-GB voice, which is sort of unreasonably good.

[1] https://github.com/gmn/nanotts

I installed the command line version using uv

    uv init
    uv add moonshine-voice
    uv run moonshine-voice mic --language en

super nice to be able to run it to test it like this

good job on a clear readme.md tbh

So at that tiny 500kb size I imagine it could be compiled to web assembly, and run entirely in the browser right?

Couldn’t find a link, is that hard to do?

Very cool. I've done TTS on a 32K Arduino but it was pretty croaky. https://youtu.be/ErGDboTpwM0

I've a local dictation workflow for coding, and one thing I've learned is that transcription accuracy is only half or even less than half the problem now. The other half is latency. Once the delay gets low enough that you stop noticing it, voice input starts feeling much more natural. It'll be interesting to see where this lands compared to Whisper-based setups for continuous dictation

This looks like an extreme point for AI-based TTS, as formant/tract modeling synths tend to be more accurate if you want TTS in a tiny amount of compute, but sound distinctly robotic.

TTS (neural diphone synth @ 16 kHz) ~1.8 MiB voice pack

This is in the realm of Microsoft Sam.

This is really impressive.

If I get time, I would like to try compiling it to WASM. This would allow me to swap my robot poet’s native browser voice synthesis for it. Not sure if it is worth it, but it will be fun to play around with.

Edit: typo

[0] https://muffinman.io/bard/

this is good to see. i also trained a stt under 500kb for sub dollar chips. it had about 20 words that it could understand(like start, stop, left, right, go, up etc) and then the spell mode where you could say the word spell and then say the individual english alphabets and close with spell. it was super fun to work on. these tend to be extremely unstable though, like confusion between p and t (at least for my accent). will have to try this one now.

It looks great, thank you! I'll see if I can use it for my in browser AI assistant project's ( https://aidekin.com ) voice part. It's currently using Nemotron-3.5-ASR and supertonic-3 but overall it requires 1.2gb download.

The voice activity detection alone here is compelling - very useful for doing things like highlighting a speaker who's transmitting in realtime. At that rate the impact on perf will be so minimal that you could easily run it in the browser across devices.

Given the tiny size of this, I wonder about possible future integration with esphome compatible hardware

https://esphome.io/

Compare

https://github.com/ggml-org/whisper.cpp/

This makes me want to have a server room with 5 of these around my house and control everything that house in LabRats

Voice is one of the most latency-sensitive modalities in AI. Moonshine is doing awesome stuff

Will it be able to understand my English with an Indian accent?

wow now that's a really tiny tts model. is there a comparison to https://github.com/kyutai-labs/pocket-tts ?

Is the dataset open

Great work!

ngl, it looks incredible

very nice I love it

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Thank you for this. I love your work on Curb Your Enthusiasm.