localwhisper 0.1.1

Local, offline push-to-talk and wake-word voice-to-text for Windows — types speech at your cursor.

LocalWhisper 🎙️

Local, offline voice-to-text for Windows. Speak, and it types wherever your cursor is — in any app. No cloud, no account, no data leaves your PC.

Three ways to trigger it:

Trigger	How	Best for
Ctrl + B	Hold and speak, release to insert	quick phrases (walkie-talkie)
Alt + N	Tap to start, tap again to stop	longer dictation (hands free)
"Hey Jarvis"	Say it, then speak	fully hands-free

Powered by faster-whisper (small.en model) and openWakeWord.

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Install (easiest — no Python needed)

1. Go to the Releases page and download LocalWhisper-Setup.zip.
2. Extract the ZIP (right-click → Extract All). Keep the folder structure.
3. Double-click Install.bat and approve the Windows admin prompt.

That's it. LocalWhisper installs, starts immediately, and launches automatically every time you log in. A small tray icon shows its state:

• ⚪ grey = idle    🟢 green = recording    🟠 amber = transcribing

Right-click the tray icon to Quit. To remove it later, run Uninstall.bat.

First-launch notes

• Windows SmartScreen may show "Windows protected your PC" — this is expected for a new, unsigned app. Click More info → Run anyway.
• Administrator rights are required because global hotkeys (Ctrl+B / Alt+N) need a system-level keyboard hook. The installer sets this up for you.
• If nothing happens on launch, check the log at %LOCALAPPDATA%\LocalWhisper\localwhisper.log.
• Needs a 64-bit Windows 10/11 PC and a microphone. If the app won't start, install the Microsoft Visual C++ Redistributable (x64).

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Install via pip (for Python users)

If you already have Python 3.9+:

pip install localwhisper
localwhisper

On first run it downloads the small.en model (~240 MB) and the wake-word models automatically. Run your terminal as Administrator so the global hotkeys work.

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Tuning

Behavior lives in localwhisper/config.py (edit + rerun from source, or rebuild):

Setting	Default	What it does
model_name	small.en	Transcription model. base.en is faster, less accurate.
wakeword_model	hey_jarvis	Also: alexa, hey_mycroft, hey_rhasspy.
wakeword_threshold	0.5	Lower = triggers more easily; raise if it false-fires.
endpoint_silence_ms	1500	Silence (ms) that ends a hands-free utterance.
energy_threshold	0.01	Loudness that counts as speech.

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Build from source / run the tests

git clone https://github.com/Bhargava-R-dev/localwhisper
cd localwhisper
py -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
.\.venv\Scripts\python.exe -c "import openwakeword.utils as u; u.download_models()"

# run from source
.\.venv\Scripts\python.exe -m localwhisper.app

# run the tests
.\.venv\Scripts\python.exe -m pytest -v

Build the distributable EXE

.\.venv\Scripts\python.exe -m pip install pyinstaller
.\.venv\Scripts\python.exe packaging\build_exe.py

Output lands in dist\LocalWhisper\. See packaging/ for the installer scripts and how the release ZIP is assembled.

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How it works

One microphone stream feeds a small state machine. When idle it runs the wake-word detector; the two hotkeys post events from a background thread. Any trigger records audio, hands it to the warm whisper model, and pastes the result at the cursor via the clipboard. Everything runs locally on CPU.

License

MIT