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Is that Microsoft Sam? :)

(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

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?

Thanks for that ... impressive!

I think Google's Conformer paper is SOTA at the <30M model size, where I think they put an incredible amount of flops into a 10M param model to reach around 2% lsc clean (the whole model and RNN decoder were trained domain specific to librispeech here).

I think my small Talon models are next, around 3% lsc clean at ~28M (greedy CTC decoding, no external encoder, no LM, not trained in a domain specific way). I reached around 6.5% at 10M.

I've been working on some new baselines I want to release soon as public artifacts. This article is inspiring me to try pushing the param size down a bit. I suspect we can do large vocabulary end to end in the <5M range.

I've always dreamed of having the ability to just talk to my computer (in the right circumstances) so I actually worked in the field for many years. The main reason I never use speech recognition today is because I have zero interest of sending recordings of my voice to the servers of some global corporations.

Running speech recognition and TTS locally is quite feasible, as projects like this one show.

I do a ton of coding (codex) with a tts/stt wrapper. During walks, cycling, in the car. Not every task is suited to this style of interaction, but many are. Long form codex replies are condensed, code blocks are suppressed all in the name of making it work for tts feedback. So it works best on well defined projects with guardrails, where you know the agent can perform well.

I use the Hex[1] app on MacOs for near instant transcription with Parakeet V3. This is how I speak to Code agents at least 80% of the time. The idea is - I tend to be lazy if I need to type lots of details, so speaking lets me get into details that I otherwise wouldn’t and this helps give more useful context to the agent. This often tends to be an unstructured brain dump so I sometimes ask the agent to repeat back what it understood, so I can make sure, and this also likely helps it stay on track.

[1] hex https://github.com/kitlangton/Hex

I used to use Handy but as of a few months ago it had stuttering and other issues so I switched to Hex. Even for relatively long minute long dictation, transcription is near instant and accuracy is more than sufficient, especially when talking to AI since it that can “read between the lines “.

Using voice with code agents is a huge unlock and I’m surprised to see some people I recommend it to, still resist it.

As for TTS I found it fun to make a voice plugin for Claude Code that uses PocketTTS, so it can give brief updates whenever it finishes a turn:

https://pchalasani.github.io/claude-code-tools/plugins-detai...

I (well CC and I) wrote a tts/stt pipeline for the CLI of CC. It's a lot more, immersive I guess, when I open my dev environment and it gives me a verbal walk through of what's going on.

(this inspired some more demo-y stuff I have where claude can manipulate the mouse and audit things it's built visually in conjunction with that). I'm sure this has already been wrapped up into some MCP framework, but it was fun to build it super early on and it just sort of works for me.

I don't use this in my day job, but it does feel very futuristic when I pull up my home lab.

What I want is a 1940s style “taking dictation” where the words I say go through a step where the goal is to create the text Im imagining. So if I say “… very significant, actually just significant…” what Claude Code receives is “…significant…”.

I built this myself with whisper -> “secretary” prompt -> Claude Code, but having the first two steps be interactive is really what I would want.

I'm founder of ottex.ai, I use stt pretty much all the time when work with AI and quite often for communications to draft emails and chat messages.

I started ottex half a year ago after I tested gemini 2.5 flash native audio support. I was blown away by the quality of transcripts and decided to built an app to use it myself.

Currently the default model in the app is Gemini 3 flash, but you can connect to 9 providers and God knows how many models to play with.

I would suggest you to try this models for ai prompting:

- Gemini 3 / 3.5 flash - Soniox rtt v5 - Mistral transcribe v2 - assembly 3.5 pro

On some days, half of my promts to Copilot at work are spoken to a local whisper medium model running on an Intel ARC GPU.

One of my side projects is a tool that lets you control your entire system with STT. It's built on Whisper and supports hot swapping custom profiles, so you can add easy commands for any software.

I intend to use it to work on low stakes vibe coding projects while I'm doing other stuff. Todays LLMs are a lot better at interpreting rambling dictation with mid-message corrections.

There are a few paid programs out there that do the same, but they made my vibe slop sense tingle and are not aimed at development.

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".

Industry leading Interactive Voice Response systems have become very good at filling in ambiguous information from context, and modulating pronunciation to Ape emotional information.

However, being able to interact with these natural language systems in uncontrolled settings is still a fools errand. For STT, there is also regional dialect, slang, and individual differences.

Witnessing blind users hit unrecognizable reading-speeds on old Gordon 8 TTS systems was surprising. I learned people adapt to imperfect systems pretty quickly. =3

For STT, wispr flow has a generous free tier. For TTS, I have Claude read out loud what it just finished as a stop hook, so I know which claude finished up.

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

This actually holds for everything in AI.

If you look at this chart here it seems the tiny model has a WER of ~12%… not sure about the micro model:

https://github.com/moonshine-ai/moonshine#when-should-you-ch...

`uvx moonshine-voice mic --language en` That is even simpler.

500k memory but not sure about disk.

Should be very doable. I ship a small CNN in a browser extension via onnxruntime-web and the model weights were never the bottleneck, the runtime was. The wasm backend adds a few MB of runtime before your first inference, so a 500kb model with a lean hand-rolled wasm build would actually beat most "tiny" browser ML deployments in total download.

One gotcha if anyone wants this in a Chrome extension: MV3 requires 'wasm-unsafe-eval' in the CSP for any wasm at all, which surprised me the first time a build that worked fine as a web page died silently as an extension.

Presumably it's not, but the TTS voice in the video sounds to me more like formant synthesis than diphone - it reminds me of my DECtalk.

The project credits does mention espeak (which is formant based) as well as various other TTS projects, although it sounds like they are only using the pronunciation part of espeak, not the voice synthesis.

https://github.com/moonshine-ai/moonshine#acknowledgements

Could you get people to use the NATO phonetic alphabet for the spelling part? I suppose a challenge is that many people don't know the whole thing, even if they're aware it exists.

I remember someone training smart kettle to use its speaker as microphone

IIRC the Alexa enabled voice remotes also used a similarly small model though perhaps not this small

I suppose, but for home automation, esps are best for getting the audio to something more powerful. If this lets a raspberry pi do voice recognition really fast, that alone is worth it.

What work?