I really like dictation. For years, I relied on transcription tools that were almost good, but they were all closed-source. Even a lot of them that claimed to be “local” or “on-device” were still black boxes that left me wondering where my audio really went.
So I built Whispering. It’s open-source, local-first, and most importantly, transparent with your data. Your data is stored locally on your device, and your audio goes directly from your machine to a local provider (Whisper C++, Speaches, etc.) or your chosen cloud provider (Groq, OpenAI, ElevenLabs, etc.). For me, the features were good enough that I left my paid tools behind (I used Superwhisper and Wispr Flow before).
Productivity apps should be open-source and transparent with your data, but they also need to match the UX of paid, closed-software alternatives. I hope Whispering is near that point. I use it for several hours a day, from coding to thinking out loud while carrying pizza boxes back from the office.
Here’s an overview: https://www.youtube.com/watch?v=1jYgBMrfVZs, and here’s how I personally am using it with Claude Code these days: https://www.youtube.com/watch?v=tpix588SeiQ.
There are plenty of transcription apps out there, but I hope Whispering adds some extra competition from the OSS ecosystem (one of my other OSS favorites is Handy https://github.com/cjpais/Handy). Whispering has a few tricks up its sleeve, like a voice-activated mode for hands-free operation (no button holding), and customizable AI transformations with any prompt/model.
Whispering used to be in my personal GH repo, but I recently moved it as part of a larger project called Epicenter (https://github.com/epicenter-so/epicenter), which I should explain a bit...
I’m basically obsessed with local-first open-source software. I think there should be an open-source, local-first version of every app, and I would like them all to work together. The idea of Epicenter is to store your data in a folder of plaintext and SQLite, and build a suite of interoperable, local-first tools on top of this shared memory. Everything is totally transparent, so you can trust it.
Whispering is the first app in this effort. It’s not there yet regarding memory, but it’s getting there. I’ll probably write more about the bigger picture soon, but mainly I just want to make software and let it speak for itself (no pun intended in this case!), so this is my Show HN for now.
I just finished college and was about to move back with my parents and work on this instead of getting a job…and then I somehow got into YC. So my current plan is to cover my living expenses and use the YC funding to support maintainers, our dependencies, and people working on their own open-source local-first projects. More on that soon.
Would love your feedback, ideas, and roasts. If you would like to support the project, star it on GitHub here (https://github.com/epicenter-so/epicenter) and join the Discord here (https://go.epicenter.so/discord). Everything’s MIT licensed, so fork it, break it, ship your own version, copy whatever you want!
On key press, start recording microphone to /tmp/dictate.mp3:
On key release, stop recording, transcribe with whisper.cpp, trim whitespace and print to stdout: I keep these in a dictate.sh script and bind to press/release on a single key. A programmable keyboard helps here. I use https://git.sr.ht/%7Egeb/dotool to turn the transcription into keystrokes. I've also tried ydotool and wtype, but they seem to swallow keystrokes. This gives a very functional push-to-talk setup.I'm very impressed with https://github.com/ggml-org/whisper.cpp. Transcription quality with large-v3-turbo-q8_0 is excellent IMO and a Vulkan build is very fast on my 6600XT. It takes about 1s for an average sentence to appear after I release the hotkey.
I'm keeping an eye on the NVidia models, hopefully they work on ggml soon too. E.g. https://github.com/ggml-org/whisper.cpp/issues/3118.
> It is difficult to get a man to understand something, when his salary depends upon his not understanding it!
in the special case where the thing to be understood is "your app doesn't need to be a Big Fucking Deal". Maybe it pleases some users to wrap this in layers of additional abstraction and chrome and clicky buttons and storefronts, but in the end the functionality is already there with a couple of FOSS projects glued together in a bash script.
I used to think the likes of Suckless were brutalist zealots, but more and more I think they (and the Unix patriarchs) were right and the path to enlightenment is expressed in plain text.
Whisper on windows, the openai-whisper, doesn't have these q8_0 models, it has like 8 models, and i always get an error about triton cores (something about timeptamping i guess), which windows doesn't have. I've transcribed >1000 hours of audio with this setup, so i'm used to the workflow.
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Yes! This. I have almost no experience w/ tts, but if/when I explore the space, I'll start w/ Whispering -- because of Epicenter. Starred the repo, and will give some thought to other apps that might make sense to contribute there. Bravo, thanks for publishing these and sharing, and congrats on getting into YC! :)
https://github.com/epicenter-so/epicenter/pull/655
After this pushes, we'll have far more extensive local transcription support. Just fixing a few more small things :)
We all should be.
My biggest gripe perhaps is not being able to get decent content out of a thought stream; the models can't properly filter out the pauses, "uuuuhmms", and much less so handle on the fly corrections to what I've been saying, like going back and repeating something with a slight variation and whatnot.
This is a challenging problem I'd love to see being tackled well by open models I can run on my computer or phone. Are there new models more capable of this? Is it not just a model thing, and I missing a good app too?
In the meanwhile, I'll keep typing, even though it can be quite a bit less convenient to do; especially true for note taking on the go.
One of the features of the project posted above is "transformations" that you can run on transcripts. They feed the text into an LLM to clean it up. If you're willing to pay for the tokens, I think you could not only remove filler-words, but could probably even get the semantically-aware editing (corrections) you're talking about.
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Do you have any sense of whether this type of model would work with children's speech? There are plenty of educational applications that would value a privacy-first locally deployed model. But, my understanding is that Whisper performs pretty poorly with younger speakers.