Show HN: Whispering – Open-source, local-first dictation you can trust

Does this support using the Parakeet model locally? I'm a MacWhisper user and I find that Parakeet is way better and faster than Whisper for on-device transcription. I've been using push-to-transcribe with MacWhisper through Parakeet for a while now and it's quite magical.

For those checking out the repo this morning, I'm in the middle of a release that adds Whisper C++ support!

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 :)

> "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."

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! :)

I've been interested in dictation for a while, but I don't want to be sending any audio to a remote API, it all has to be local. Having tried just a couple of models (namely the one used by the FUTO Keyboard), I'm kinda feeling like we're not quite there yet.

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.

excellent tool and easy to get started.

on win11, i installed ffmpeg using winget but it's not detecting it. running ffmpeg -version works but the app doesn't detect it.

one thing, how can we reduce the number of notifications received?

i like the system prompt option too.

This is wonderful, thank you for sharing!

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.

> I’m basically obsessed with local-first open-source software.

We all should be.

Not a fan of high resource use or reliance on proprietary vendors/services. DeepSpeech/Vosk were pre-AI and still worked well on local devices, but they were a huge pain to set up and use. Anyone have better versions of those? Looks like one successor was Coqui STT, which then evolved into Coqui TTS which seems still maintained. Kaldi seems older but also still maintained.

edit: nvm, this overview explains the different options: https://www.gladia.io/blog/best-open-source-speech-to-text-m... and https://www.gladia.io/blog/thinking-of-using-open-source-whi...

Wait, I'm confused. The text here says all data remains on device and emphasises how much you can trust that, that you're obsessed with local-first software, etc. Clicking on the demo video, step one is... configuring access tokens for external services? Are the services shown at 0:21 (Groq, OpenAI, Antrophic, Google, ElevenLabs) doing the actual transcription, listening to everything I say, and is only the resulting text that they give us subject to "it all stays on your device"? Because that's not at all what I expected after reading this description

Thanks for sharing! Transcription suddenly became useful to me when LLMs started being able to generate somewhat useful code from natural language. (I don't think anybody wants to dictate code.) Now my workflow is similar to yours.

I have mixed feelings about OS-integration. I'm currently working on a project to use a foot-pedal for push-to-transcribe - it speaks USB-HID so it works anywhere without software, and it doesn't clobber my clipboard. That said, an app like yours really opens up some cool possibilities! For example, in a keyboard-emulation strategy like mine, I can't easily adjust the text prompt/hint for the transcription model.

With an application running on the host though, you can inject relevant context/prompts/hints (either for transcription, or during your post-transformations). These might be provided intentionally by the user, or, if they really trust your app, this context could even be scraped from what's currently on-screen (or which files are currently being worked on).

Another thing I've thought about doing is using a separate keybind (or button/pedal) that appends the transcription directly to a running notes file. I often want to make a note to reference later, but which I don't need immediately. It's a little extra friction to have to actually have my notes file open in a window somewhere.

Will keep an eye on epicenter, appreciate the ethos.

Looks like a really cool project. Do you have any opinions on which transcription models are the best, from a quality perspective? I have heard a lot of mixed opinions on this. Curious what you've found in your development process?