# Project: speech-to-text tools

Speech-to-text command line utilities leveraging local models (faster-whisper, Ollama).

## Environment
- Debian Bookworm, kernel 6.1, X11
- Conda env: `whisper-ollama` (Python 3.10, CUDA 12.2)
- mamba must be initialized before use — run: `eval "$(micromamba shell hook -s bash)"`
- GPU: NVIDIA (float16 capable)
- xdotool installed for keyboard simulation (X11 only)

## Tools
- `assistant.py` / `talk.sh` — transcribe speech, copy to clipboard, optionally send to Ollama
- `voice_to_terminal.py` / `terminal.sh` — voice-controlled terminal via Ollama tool calling
- `voice_to_xdotool.py` / `dotool.sh` — hands-free voice typing into any focused window (VAD + xdotool)

## Testing
- To test scripts: `mamba run -n whisper-ollama python <script.py> --model-size base`
- Use `--model-size base` for faster iteration during development
- Audio device is available — live mic testing is possible
- Test xdotool output by focusing a text editor window

## Dependencies
- Conda: faster-whisper, sounddevice, numpy, pyperclip, requests, ollama
- Pip (in conda env): webrtcvad
- System: libportaudio2, xdotool

## Conventions
- Shell wrappers go in .sh files using `mamba run -n whisper-ollama`
- All scripts set `CT2_CUDA_ALLOW_FP16=1`
- Whisper model loading always has GPU (cuda/float16) -> CPU (cpu/int8) fallback
- Keep scripts self-contained (no shared module)
- Don't print output for non-actionable events

## Preferences
- Prefer packages available via apt over building from source
- Check availability before recommending a dependency
- Prefer snappy/responsive defaults over cautious ones
- Avoid over-engineering — keep scripts simple and focused
