Amazing that this works. As an aside, and I appreciate this is just a demo, if the use case is to get a device to join a WiFi network - would a single or double line lcd with 3 buttons not be cheaper than 520KB?
(Also, I know it's besides the point but this might be the most painful way to connect to Wifi physically possible. "Make normal everyday tasks slow, tedious and painful" is a bit of an odd choice for a product demo.)
Say, speaking of Sam, what were the memory requirements for SAM (Software Automatic Mouth) on C64. I guess they were not more than 64K? Although, the bulk here is probably for the speech recognition, not the TTS. (And this one does sound a little nicer :)
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.
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.
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.
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.
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.
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.
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?
https://handy.computer is the goto. It can even add an LLM pass (optionally) so if you say "remind me to buy three, actually, four, eggs" it'll type "remind me to buy four eggs".
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[ 5.1 ms ] story [ 52.6 ms ] thread(Also, I know it's besides the point but this might be the most painful way to connect to Wifi physically possible. "Make normal everyday tasks slow, tedious and painful" is a bit of an odd choice for a product demo.)
Say, speaking of Sam, what were the memory requirements for SAM (Software Automatic Mouth) on C64. I guess they were not more than 64K? Although, the bulk here is probably for the speech recognition, not the TTS. (And this one does sound a little nicer :)
Browser demo of a reversed SAM:
https://discordier.github.io/sam/index.html
Flite for comparison: https://github.com/festvox/flite
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!
good job on a clear readme.md tbh
[1] https://github.com/gmn/nanotts
TTS (neural diphone synth @ 16 kHz) ~1.8 MiB voice pack
This is in the realm of Microsoft Sam.
Couldn’t find a link, is that hard to do?
https://esphome.io/
https://github.com/ggml-org/whisper.cpp/
[1] https://blog.trulm.com/posts/speech-as-independent-parts/
Fishing for anecdotes here, does anyone have any good tts/stt experiences?