Whistle: Speech to Text in 16.9 MB(cactuscompute.com) |
Whistle: Speech to Text in 16.9 MB(cactuscompute.com) |
I just setup Windows speech to text for him last week and it's great to see how he can write an entire page in 10 minutes, it would take him days using the keyboard.
But every single sound he makes with his mouth ends up on the page too.
Gemini team just released Gemini 3.5 Transcribe that’s supposed to be good at this; it’s available via api: https://blog.google/innovation-and-ai/models-and-research/ge...
I was researching STT for people with speech disorders two years ago and essentially everything was boiling down to three problems at the end of the day - data scarcity, irregularity of way of speaking and thus constant ambiguity in translation, and individual differences in speech patterns among patients.
Handy has Nemotron Streaming and it works fabulously, FWIW. I’ve vibed a kind-of-working Deepgram API server into it but haven’t gotten around to finishing it. It’s something that should exist IMO!
This definitely seems lighter and faster. How does accuracy compare?
I ended up having AI optimize Whisper Large and create a plugin for TypeWhisper, and that's what I use (feeding the results through local Qwen 3.8 running under MTPLX).
If you’re feeding the results into a very smart LLM, it will figure out what you meant (but crucially ONLY if you warn it or tell it to do so, in some cases!). If you’re writing code directly or creating something for public consumption, you can’t tolerate mistakes. If you’re taking notes for yourself you just want it to work cheaply.
If you are ok with the complexity you can run both, and a Meta/Google open model with native audio, and let a smart LLM doctor it up. If performance really matters you can pay for a proprietary model or train one yourself. Until you get to that point, I think it probably doesn't matter much either way. Voice is just too easy to fiddle with
I use parakeet with superwhisper, and I’m making another app that has SST and TTS built in, and I want to use my downloaded parakeet model, but it seems there’s so many different implementations from ONNX to whisper, it’s not easy to use your downloaded models. So models like moonshine and this one allow you to just embed it into your application simply. It might not be as good as parakeet, but it gets you 80% of the way there.
I know it's slightly off topic but surely it must be easy by now to train a spell checker that doesn't annoy the crap out of everyone using it (looking at you here Apple)!
opening this thread for questions/feedback if you have any
Since it doesn't support Norwegian - I tried English - and it mis-transcribed "cleaning" for "training" - probably a failure due to context/training (Hello everyone, today we are going to do some cleaning).
So, reasonable, but limited?
Edit: As others have pointed out, this is not actually open source. It's source-available, which is quite a bit different because folks can't fork and distribute it as easily. The license also appears to be revocable and non-transferable, which makes it different from open source licenses.
https://github.com/futo-org/android-keyboard/blob/master/LIC...
https://github.com/futo-org/voice-input/blob/master/LICENSE....
...Okay that was pretty good.
[1]: https://fr.wikipedia.org/wiki/Langage_siffl%C3%A9_d%27Aas
You can set it "Push to talk" mode (like a walkie-talkie radio), and when you're done talking and release the button, it can paste the text into any text field.
You can even replicate ChatGPT voice conversation mode, by having Handy as your speech input, and then (I forgot the extension) enabling a speech-to-text model for OpenCode. Surprisingly relaxing flow for certain tasks, like tweaking a website's styles.
Because I have seemingly mixed opinions on it, on one hand, I did put the effort but on the other, the output is AI generated so I am unsure about sharing it with others (because they might think its AI generated)
Do you use it for very small edits (removing just the uhhm's?) or for slightly more edits.
The way that I use it sometimes is that while thinking, I will write something which can sometimes make me feel as if a better re-write can better explain my thoughts or rephrasing it as such. For example. I will think about X topic, connect it to Y, then try to add some more points about X again.
I found LLM's to do a really decent job at generating the final outputs as such, but as I said, I am left sometimes feeling a little confused as to sharing it or not because of it being AI generated and the end user not knowing if I put an actual effort into creation of it or not.
Should I try to share the actual transcript of it as well, I really wish if some good ethics and internet ettiquette could be established about it.
"what are your observations on feeling as if sharing that output though?"
and "Should I try to share the actual transcript of it as well, I really wish if some good ethics and internet ettiquette could be established about it."
could you rephrase the question
for STT, it's literally you saying it, with a model transcribing, and then another model correcting a little, and you also have control over editing it. I don't think any of the arguments on "etiquette re: sharing AI output" apply here.