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.

    air a
    bat b
    cap c
    drum d
    each e
    fine f
    gust g
    harp h
    sit i
    jury j
    crunch k
    look l
    made m
    near n
    odd o
    pit p
    quench q
    red r
    sun s
    trap t
    urge u
    vest v
    whale w
    plex x
    yank y
    zip z

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