Show HN: Ghost Pepper – Local hold-to-talk speech-to-text for macOS

This thread is a support group for people who have each independently built the same macOS speech-to-text app.

This is great, and I'm not knocking it, but every time I see these apps it reminds me of my phone.

My 2021 Google Pixel 6, when offline, can transcribe speech to text, and also corrects things contextually. it can make a mistake, and as I continue to speak, it will go back and correct something earlier in the sentence. What tech does Google have shoved in there that predates Whisper and Qwen by five years? And why do we now need a 1Gb of transformers to do it on a more powerful platform?

https://handy.computer/ already exists?

Speech-to-text has become integral part of my dev flow especially for dictating detailed prompts to LLMs and coding agents.

I have collected the best open-source voice typing tools categorized by platform in this awesome-style GitHub repo. Hope you all find this useful!

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

Nice one! For Linux folks, I developed https://github.com/goodroot/hyprwhspr.

On Linux, there's access to the latest Cohere Transcribe model and it works very, very well. Requires a GPU though. Larger local models generally shouldn't require a subordinate model for clean up.

Have you compared WhisperKit to faster-whisper or similar? You might be able to run turbov3 successfully and negate the need for cleanup.

Incidentally, waiting for Apple to blow this all up with native STT any day now. :)

Thank you for sharing, I appreciate the emphasis on local speed and privacy. As a current user of Hex (https://github.com/kitlangton/Hex), which has similar goals, what are your thoughts on how they compare?

I see a lot of whisper stuff out there. Are these the same old OpenAI whispers or have they been updated heavily?

I've been using parakeet v3 which is fantastic (and tiny). Confused why we're still seeing whisper out there, there's been a lot of development.

Would also like to know how it compares to https://github.com/openwhispr/openwhispr

I like that openwhisper lets me do on device and set a remote provider.

Genuinely curious — what's your approach to memory and context management? That's where most agent frameworks hit walls in production. We run a multi-agent orchestration system and the biggest unlock was treating each agent's context as a finite resource rather than unbounded context windows. Worth thinking about before you scale too far.

I see quite a few of these, the killer feature to me will be one that fine tunes the model based on your own voice.

E.G. if your name is `Donold` (pronounced like Donald) there is not a transcription model in existence that will transcribe your name correctly. That means forget inputting your name or email ever, it will never output it correctly.

Combine that with any subtleties of speech you have, or industry jargon you frequently use and you will have a much more useful tool.

We have a ton of options for "predict the most common word that matches this audio data" but I haven't found any "predict MY most common word" setups.

That’s awesome! Do you know how it compares to Handy? Handy is open source and local only too. It’s been around a while and what I’ve been using.

https://github.com/cjpais/handy

How does this compare with Superwhisper, which is otherwise excellent but not cheap?

Parakeet is significantly more accurate and faster than Whisper if it supports your language.

Cool, I've been doing a lot of "coding" (and other typing tasks) recently by tapping a button on my Stream Deck. It starts recording me until I tap it again. At which point, it transcribes the recording and plops it into the paste buffer.

The button next to it pastes when I press it. If I press it again, it hits the enter command.

You can get a lot done with two buttons.

Not sure why I should use this instead of the baked-in OS dictation features (which I use almost daily--just double-tap the world key, and you're there). What's the advantage?

If you don't feel like downloading a large model, you can also use `yap dictate`. Yap leverages the built-in models exposed though Speech.framework on macOS 26 (Tahoe).

Project repo: https://github.com/finnvoor/yap

Feature request or beg: let me play a speech video and transcribe it for me.

why isn't the cleanup done on the transcription (as opposed to screen record)

I think the jab at the bottom of the readme is referring to whispr flow?

https://wisprflow.ai/new-funding

This is great. I'm typing this message now using Ghost Pepper. What benefits have you seen from the OCR screen sharing step?

I've been looking for the opposite - wanting to dump text and it be read to me, coherently. Anyone have good recommendations?

Sadly the app doesn't work. There is no popup asking for microphone permission.

EDIT: I see there is an open issue for that on github

MacWhisper is also a good one

Hi Matt, there's lots of speech-to-text programs out there with varying levels of quality. 100% local is admirable but it's always a tradeoff and users have to decide for themselves what's worth it.

Would you consider making available a video showing someone using the app?

how does this compare to macos built in siri TTS, in quality and in privacy?

Great job. How about the supported languages? System languages gets recognised?

does it input the text as soon as it hears it? or does it wait until the end?

[dead]

[dead]

always mac. when windows? why can you just make things multios