Are there models I'm not aware of that are trained for this task? Taking audio in an unknown language, and rather than identifying the language, just transcribing the sounds to IPA? That would not be useful to most people, but it would be a Godsend to many, many linguists working with minority languages around the world.

Noob question: How do you think about funding from a foundation(i have no clue if you need it or not, I do hope you have a way to get paid one way or another because handy is amazing) for maintenance of this? if you did or were going to get paid by asking for maintaining such a project what might be the kind of organizations you would look for to get supported and how would you do it?

I want to open an office document, for example, and start talking. And I want the software to continuously type what I am saying at the cursor with minimal latency. The continuous part is crucial. Many software will paste whatever I said after I have stopped recording, but that is not useful.

Nice. Here's the Python one: https://github.com/handy-computer/transcribe.cpp/tree/main/b... - looks like it's not yet available as a binary wheel on PyPI with the dependency included (the library on PyPI right now uses ctypes to call a separately installed library) but that's planned for a future release.

It's a nice reminder: You can use AI to slop cannon at maximum speed, or you can use it to scale your ambitions and build something more rigorous and lasting than ever before.

I'd build Transcribe.cpp into the apps I maintain, but I feel like this functionality should (generally) be integrated into the OS or "everywhere" via an app like Handy.

> I think as we look forward to the future, more inference will start happening locally for one reason or the other. This brings the distribution story front and center. In order to have more applications running inference locally, we need to make running inference easier.

This makes these projects so much more trustworthy and easier to approach:

> Were any of the words here written using AI? Nope. They came from my mouth or my fingers.

It works very well, the integration is much easier than before, users have model choice. So happy that this exists!

Will definitely check this out since it sounds like it eases through the pain of dealing with these.

I assume this is going to make maintaining SubtitleEdit a lot easier from now on, too: https://github.com/SubtitleEdit/subtitleedit/.

Anyone know a good Windows app that's just a window that transcribes - and translates - whatever goes through your output device, and not the microphone like most apps do?

tysm for shipping this, keep up the great work OP

Only way I have found that's relatively easy to inject technical vocab is to use whisper, but limited, I think to about 220 or so tokens. Whisper has sort of like a priming prompt where one can put in a bunch of technical words and it will try to recognize those. But again, that's limited to small number tokens. And that limits one use a relatively slow, by today's standards, whisper.cpp.

I benchmarked it across a bunch of different hardware that I have available, and Whisper gives decent performance as far as speed goes only on a pretty top-end GPU, such as a 5090 or 4070, like for example on Strix Halo, it's still relatively slow for longer transcriptions because I prefer just a stream of consciousness ramblings for minutes and then that being transcribed and cleaned up versus short sentences. So in that scenario something like 5090 really is good because the cleanup prompt runs fast using usually Qwen 3.6 MOE model. Whisper on 4070 itself is about 0.7 seconds for two or three minute transcription. So the total wait time for a three-minute transcription is roughly a second, or a little bit more than a second, so totally acceptable. But it does take decent hardware, and it grows to be double that on if running totally local. Well, in my case, it's all local, but it's my own hardware all over the place, but truly running on laptop, it's much faster using Parakeet, but then the cleanup is the bottleneck.

Anyway, it's just my experience messing around with this for the last year. I did start using AquaVoice, but their speed was exceptional, and tech vocab was exceptional, but they would have some annoying delays occasionally, and I didn't like paying the money and sending sensitive topics and screenshots into the cloud, and I had hardware, so my local solution is basically almost as good as commercial one. But I think they train their own model. So what I'm doing is I collect all the samples of my transcriptions, and I am slowly building my own data set that hopefully at some point when I get energy I will find some way to fine tune something.

After seeing so many *subscription based* transcription apps all wrapping *open source models*, finding Handy was a real delight and I'm happy to see the author keep on building!

I mean, I naturally should try myself, and plan to do so, but slightly lower on my free time priority list and I figured someone else might have explored this already.

I maintain this list of all the best open-source ones in this awesome-style GitHub repo. People looking for open-source dictation tools, hope you find something that works for you here:

https://github.com/primaprashant/awesome-voice-typing