Wayland Speech-to-Text Tool

Hacker News

I vibe-coded a speech-to-text tool for Wayland that works for me. You trigger it with a keybind, speak into your mic, and it transcribes using OpenAI Whisper or Google STT (local is coming), then either types it directly into your active text field or saves it to the clipboard. It uses PipeWire for audio capture and works signal-driven, so there's no background process running. Just on-demand transcription when you need it. I've tested it on Niri and it should work on Hyprland, though I haven't tested GNOME or KDE yet. This was a one day Rust project and probably has some bugs since I just implemented it. It's definitely rough around the edges, but it serves its purpose for quick dictation. I'm open to feedback and input from anyone who tries it out.

Visit product View on Hacker NewsJun 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, openai, using · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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