Transcribe.cpp

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?

Oh, I like this! I’ve been looking into locally hosting a transcription API server and came away feeling pretty close to the problem statement. The things most frequently lacking were streaming support (which I’m so glad this has!) and the support for special words to boost during recognition (which I guess there’s some hope they might add???).

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.

Well this almost seems to be to good to be true. :)

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?

> 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.

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.

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.

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.

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.

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.

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

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)

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.

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

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

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?

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

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?

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.

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

Excellent work, paired with the 500kb TTS model headlining today I can see the full stack coming together.

Excellent work CJ

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

Hey, thanks for this.

Is there a way to add speaker identification easily?

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!

I don't suppose this works in the browser?

Nice - I'm definitely going to take a look at this. I've built my own cross-platform (Mac/Win/Linux) live captioning app on top of Nemotron, and it works well but dealing with ONNX is kind of annoying. With this having Rust support (I built it on Rust/Tauri) it should be a pretty solid candidate; I'll have to see if I can find a Silero VAD implementation that doesn't depend on ONNX, or maybe I'll see if the clankers can migrate it for me.

Congrats on delivering good value to the people. I have used transcribe.cpp a few weeks ago to do near realtime offline stt on a 10 year old phone, writing simple adhoc app for my use case, it's crazy what is happening right now.

Excellent work

Looks interesting, I'll give it a try. Though I'm really happy with faster-whisper on a GPU.

would love to see a demo handy is fantastic although its still behind the frontier models

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Handy is an amazing cross-platform app for dictation from the author. There are other awesome open-source dictation tools as well like native macOS ones. You do not need SaaS subscription in this day and age for transcription.

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

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