Looks very cool. One thing I have been looking for, which this doesn't seem to cover (at least I didn't see any mention of IPA in the model documentation), is a way to transcribe unknown languages phonetically, using the International Phonetic Alphabet to spell them (sound-based spelling rather than meaning-based spelling). I know several linguists doing research on minority languages (fewer than 10,000 speakers in some cases), which are small enough that they will never have enough effort made towards training language-specific models in that language.

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.

Congrats on shipping this. I love handy on my Mac, my phone for STT in situations where it’s not possible/poor performance of the native Model for STT(e.g apple’s thing is not upto scruff, like mistranslating words corresponding to a domain).

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?

For anyone looking to build on top of this. I have tried a few different STT systems, and they accurately capture what I am saying. Unfortunately, they don't support the reasonable workflow

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.

Is there something but for transcribing what you watch like videos and not your microphone? Samsung has this in my phone and it's useful for language learning. (Thought is not that accurate)

Really cool that every model is actually tested for accuracy instead of just claiming it works, I think alot of 'we support everything' tools skip that step. How are you checking accuracy for models that don't have an obvious "official" version to compare against?

What good timing to spot this. I've been reading more and more people talk about bringing TTS into their prompting toolkit and wanted to give that a try. The idea of rambling brain dump into a doc -> edit pass -> send to the robot loop sounds appealing.

The post makes it seem like ONNX is CPU only. I've used ONNX runtime to run models on Nvidia GPUs. The runtime can even dispatch to TensorRT. I'm not sure what the performance is on Apple hardware so maybe that was the motivation for moving away from ONNX.

This is an incredible contribution to the community and it's just... one guy? I kept reading expecting a Series A funding announcement at the bottom.

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'm using Handy on macOS and love it. Unfortunately, hotkeys still doesn't seem to work on Wayland, which make it unusable.

Nice. I did transcriptions on a casual project before that went through something like this. Transcribing videos or audio files with Whisper? Very common. But having to swap it out with Qwen3 or a different family of ASR models? Oops, not as straightforward. For Qwen for example you gotta deal with the forced aligner or it won't be good as subtitles, and then gotta deal with some requirements and considerations if you want to make use of MLX on a Mac or something.

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

> Maintainer supported bindings in 4 Languages

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.

Amazing, i've been looking for something like this and ended up doing transcription + diarization on a local server for now. Are you looking for contributions? Have you tried this one for diarization - https://huggingface.co/pyannote/speaker-diarization-communit... - it performed much better than Sortformer for me.

What's the easiest way to add speaker separation to this?

Spot on:

> 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.

Is transcription a form of _inference_ though? I mean I see the word being thrown around and I understand what it means (or at least I think I do) in context of LLMs doing the thing that they do -- intelligently predict the next token, but do speech-to-text models do that?

Thanks CJ, you've put some pretty cool things out there!

handy has been invaluable in my workflow, and having a fast, local, c++-based transcription library with first-party ts bindings is incredibleee

tysm for shipping this, keep up the great work OP

three separate people in this thread independently remembered Dragon NaturallySpeaking and I think that is the funniest possible review of the state of speech recognition in 2026

I saw that metal is almost x10 faster than vulkan? Why so much gap?

So it's mostly intended to be a better replacement for whisper? Mostly? With better support for more models and maybe acceleration backends?

What's the best local TTS model right now? I'm running parakeet on a mac which transcribes all my uh's and aahs. I'm running whisper on linux/cuda and I by far prefer that one over parakeet.

Does this support filtering of “umm”,”err”, “ugh”, or that is nit yet possible with open source models?

Hey, thanks for this.

Another happy user of Handy here!

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!

Excellent work CJ

Totally understandable, but I’ve found that software that transcribes everything after I finish recording actually works better for me. I’ve tried both kinds, and systems that continuously type what I’m saying distract me from completing my thought. I end up reading what’s being typed and noticing transcription mistakes instead of focusing on what I’m trying to say.

I often prefer to dictate everything in my head about a particular thing for 5–10 minutes and then go through it afterward. I find that much more useful because it doesn’t break my thought process the way continuous transcription does.

You can fairly easily modify [Handy](https://handy.computer) to do this if you want

I’m planning on having it as a first class feature of the app too just too many other issues to work on first

You know English doesn't work like that. The word you're saying only becomes clear with the surrounding context. Eg, 'there' vs 'their'.

> The continuous part is crucial. Many software will paste whatever I said after I have stopped recording, but that is not useful.

It really depends on how one uses transcription.

For example, I really value being able to open different windows, and look at graphs, or scroll some data while I'm dictating, because it can help me with providing some support information for what I'm saying.

Some apps can even take into account things you copy or look at as part of the transcription's context to improve the results [0].

[0]: https://superwhisper.com/docs/common-issues/context#types-of...

This is what I attempted with https://github.com/electronstudio/low_latency_dictation

However the accuracy of the real time models is poor, so I did a second pass with a higher accuracy model before committing the text.

You used to be able to do this with dragon naturally speaking (don’t remember if that was it’s exact name) 10 ish years ago

Agreed. It's something I've found annoying about a few systems.

It looks like the rust bindings have streaming examples so hopefully there is a nice solution here.

Apple Dictation does this, or something similar, in my experience. Some apps (e.g. terminals in my experience) buffer the entire transcript but in most apps it's identical to typing as you speak. Have you tried it?