System-wide push-to-talk dictation, powered by the OpenRouter API. Hold a hotkey anywhere on your desktop, speak, release — your words appear at your cursor. Built with Rust + Tauri 2 as a reference app for OpenRouter's speech-to-text endpoint.
| You want… | Read |
|---|---|
| The API call, minimally | src-tauri/examples/minimal.rs — mic → WAV → transcript in ~100 lines |
| The full integration, explained | docs/INTEGRATION.md — transports, biasing, OAuth, model choice |
| The integration code itself | src-tauri/src/stt/ — client, model registry, vocabulary |
| The desktop-app machinery | src-tauri/src/platform/ — hotkeys, tray, overlay (no API code) |
# smallest possible taste — record 5s, print the transcript
export OPENROUTER_API_KEY=sk-or-...
cd src-tauri && cargo run --example minimal- Push-to-talk anywhere — device-wide hotkey (keyboard or mouse side button); hold to dictate, release to type the transcript into whatever app has focus, optional second hotkey that presses Enter to send
- Rolling live preview — while you hold, the whole utterance is re-transcribed every ~1.2s into a floating overlay pill, so the text self-corrects as context grows
- 16 models, benchmarked — every dedicated STT model on OpenRouter plus streaming-capable audio chat models, with real WER/RTFx/price data and an in-app benchmark that ranks them by measured latency on your connection and voice
- Accuracy stack — vocabulary biasing (Deepgram keyterms / Whisper prompts), mis-hearing suggestions, filler-word removal, and an opt-in LLM correction pass
- Background service — menu-bar tray, close-to-tray, launch at login, starts hidden; the main window is optional
- OpenRouter OAuth — "Log in with OpenRouter" (PKCE, loopback callback), or paste an API key; credentials never reach the webview
src-tauri/src/
├── stt/ ★ the OpenRouter integration (start here)
│ ├── client.rs one-shot + SSE streaming transports, LLM cleanup, warm pool
│ ├── models.rs registry: WER/RTFx/price per model, transport routing
│ └── vocab.rs per-provider vocabulary biasing + suggestion mining
├── audio/ capture (cpal) → resample (rubato) → VAD gate (Silero)
├── pipeline/ session orchestration
│ ├── mod.rs hold-buffer, rolling re-transcription, dispatch
│ ├── text.rs filler cleanup, continuity, type-into-app
│ └── commands.rs Tauri command surface for the UI
├── platform/ OS machinery — zero API code (skippable for API readers)
│ ├── hotkey.rs device-wide push-to-talk (poll + raw HID)
│ ├── hid.rs macOS IOHID mouse-button listener
│ ├── tray.rs menu-bar presence, background lifecycle
│ ├── overlay.rs floating dictation pill window
│ └── traffic.rs macOS traffic-light positioning
├── auth.rs OpenRouter OAuth PKCE (reusable in any native app)
├── settings.rs / history.rs / benchmark.rs persistence
└── lib.rs wiring
ui/ vanilla JS/CSS, no framework, no build step
Audio flows one direction: audio → pipeline → stt → OpenRouter. The only
network calls in the codebase live in stt/client.rs and auth.rs.
Prereqs: Rust stable, Node 18+, Tauri prerequisites.
npm install
npm run devFirst run: grant Microphone access, plus Input Monitoring and
Accessibility (System Settings → Privacy & Security) for global hotkeys and
type-into-app. The app starts in the menu bar — open the window from the
tray icon, log in (or export OPENROUTER_API_KEY), bind a hotkey, dictate.
| Status | |
|---|---|
| macOS | ✅ fully supported (primary target) |
| Windows / Linux | platform/hid.rs (side buttons), platform/traffic.rs, and overlay behavior need porting — see module docs |
MIT — see LICENSE. Voice activity detection uses the
Silero VAD v5 model (MIT), bundled
via the voice_activity_detector
crate (MIT); release builds redistribute the model weights.
The global input listener is a matcher, not a recorder: key/button state is
compared against your two bindings and discarded — no keystroke history exists,
in memory or on disk, and nothing but microphone audio (plus model id and
optional vocabulary hints) is ever sent to OpenRouter. Logging deliberately
excludes key identities and transcript contents. Local files: settings.json
(preferences), history.jsonl (your transcripts, local only), benchmark.wav
(your recorded reference clip).