MURMUR

Your voice, your machine. An open-source alternative to Wispr Flow: hold a key, speak, release — clean text lands wherever your cursor is. Speech-to-text runs locally with faster-whisper; the optional cleanup pass is bring-your-own-key or fully local via Ollama. No subscription, no audio leaving your box unless you choose it.

Push-to-talk dictation, frameless pill UI, per-app context-aware cleanup, searchable history, and text-to-speech read-aloud of any selected text.

Windows-first. macOS / Linux deferred to later phases.

Stack

• GUI: PySide6 (LGPL — commercial-friendly)
• STT: faster-whisper Small.en int8, loaded once and kept resident in-process
• VAD: faster-whisper's built-in silero filter (vad_filter=True)
• Audio: sounddevice ringbuffer
• Hotkey: keyboard library (push-to-hold)
• Paste: clipboard set + Ctrl+V injection with previous-clipboard restore
• Cleanup: pluggable provider — disabled / anthropic (Claude Haiku 4.5, BYOK) / ollama (local)
• Context: Win32 GetForegroundWindow → process+title → category injected into cleanup prompt

Quickstart

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt
python -m murmur

On first run, the small.en model (~244 MB) downloads to %USERPROFILE%\.murmur\models\.

Use

1. Wait for the pill at the bottom-center to switch to dim-purple READY (model loaded).
2. Click into any text field anywhere on the system.
3. Press and hold Right Alt, speak, release.
4. The transcript pastes at the cursor.
5. Hold Shift while releasing Right Alt to skip cleanup for that utterance (raw Whisper output — use for code dictation, name spellings, exact phrasing).
6. Select any text and tap F9 to hear it read aloud. Tap F9 again to stop. Uses the fully offline Windows SAPI voice by default; set tts_voice in config to a Microsoft Edge neural voice (see edge-tts --list-voices) for much better quality — note that this sends the selected text to Microsoft's Edge TTS service.

Tray menu (right-click the icon, or double-click to open History):

• Show History — searchable archive + stats
• Settings… — full settings UI (no JSON editing needed)
• Show/Hide Pill
• Quit

Features

• Resident model, no subprocess. ~0.5–1.5s end-to-end on a Ryzen 3700X CPU.
• History window: searchable archive of every transcript with copy, delete, export to .txt / .json.
• Stats tab: total transcripts, words dictated, audio captured, today's count, average STT and cleanup latencies, top target apps, breakdown by category.
• LLM cleanup pass (optional): removes filler ("um", "uh", "like"), adds punctuation, fixes grammar without changing meaning, preserves proper nouns and code identifiers.
• Per-app context: the cleanup prompt knows whether the cursor is in a code editor, terminal, chat app, mail client, doc editor, or browser — and adapts tone accordingly (e.g. drops trailing periods in chat, preserves identifiers in code).
• Custom vocabulary: terms in the Dictionary tab are biased into Whisper's initial_prompt and the cleanup prompt so proper nouns survive transcription.
• Hold-Shift-for-raw: skip cleanup for a single utterance — handy for code or exact phrasing.
• Read-aloud (F9): speaks the current selection. Offline SAPI voice by default; opt into a Microsoft Edge neural voice via tts_voice in config (online — the selected text is sent to Microsoft; falls back to SAPI without network). Tap again to stop.
• Single-instance guard: double-launching just points you at the tray icon instead of spawning a duplicate.
• No lone-Alt menu steal: a phantom F24 is injected during the Alt hold so Windows never yanks focus to the menu bar when you release the push-to-talk key.
• Sound cues (optional): soft blips on start / stop / done / error.
• Atomic archive writes: tmp-file + os.replace — crash-safe history.
• Pill never steals focus: WA_ShowWithoutActivating + WindowDoesNotAcceptFocus, target caret stays put.

Cleanup providers

In Settings → Cleanup:

Provider	When to pick it	Cost
disabled	Privacy-only / no cleanup	Free, instant
anthropic	Best quality (Claude Haiku 4.5). BYOK.	~$0.25/M input, $1.25/M output (fractions of a cent per utterance)
ollama	Local privacy, no API key. Needs ollama serve running locally.	Free, ~800–1500ms TTFT on CPU

Set ANTHROPIC_API_KEY in Settings (stored locally at ~/.murmur/config.json — plaintext, change permissions if needed).

Files & data

• %USERPROFILE%\.murmur\config.json — settings (now includes cleanup config + dictionary)
• %USERPROFILE%\.murmur\archive.json — transcript history (atomic writes)
• %USERPROFILE%\.murmur\models\ — downloaded faster-whisper models
• %USERPROFILE%\.murmur\murmur.log — app log

Architecture notes

• One resident process, one model load. Unlike VoxMemo's per-utterance subprocess, MURMUR loads WhisperModel once at startup in a background QThread and reuses it. This is the entire latency fix.
• Push-to-talk, not VAD-driven start. End-of-utterance is the key-up event, not a VAD silence detection.
• Window flags are set in a single bitwise-OR'd call to avoid the qt.io setWindowFlags-overwriting bug.
• Foreground capture happens before the pill paints, so the cleanup prompt knows the correct target app.
• Cleanup runs in the Transcriber QThread, never on the GUI thread — paste happens regardless of LLM latency.
• Settings updates without restart: hotkey, modifier, paste, sounds, app-context apply immediately; model/compute changes trigger a background re-load with the pill briefly showing PROCESSING.

Roadmap

• Phase 3: Moonshine v2 streaming with partial-result preview, MyChatArchive capture_thought "brain mode" hotkey, voice search via search_brain, Context Q&A with Kokoro TTS read-back, Linux X11.
• macOS port: pyobjc Accessibility shim, notarized .app build via $99/yr Apple Developer Program.

License

AGPL-3.0-or-later. Use it, fork it, ship it — but derivatives (including hosted/commercial ones) must publish their source under the same terms.