https://github.com/AdrianScott/froshine
Speech-to-Text/Code using a fast local LLM, for Linux, uses Whisper
https://github.com/AdrianScott/froshine
linux tts whisper whisper-ai
Last synced: 21 days ago
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Speech-to-Text/Code using a fast local LLM, for Linux, uses Whisper
- Host: GitHub
- URL: https://github.com/AdrianScott/froshine
- Owner: AdrianScott
- Created: 2025-01-23T18:30:42.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-11-15T23:22:43.000Z (8 months ago)
- Last Synced: 2026-02-13T23:29:41.851Z (5 months ago)
- Topics: linux, tts, whisper, whisper-ai
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Whisper-Apps - froshine - square) - Linux desktop app (By Platform / Linux)
README
# Froshine VoiceCommander: Offline Voice-to-Text IDE Integration
A privacy-focused voice command system for developers that works entirely offline. Uses OpenAI's Whisper AI locally on your machine for speech-to-text and WebRTC VAD for voice activity detection.
Copyright 2025 Adrian Scott
## Features
- **100% Offline** - No audio data leaves your machine
- **Real-time Monitoring** - Continuous voice input detection
- **IDE Integration** - Direct text insertion into code editors
- **Voice Commands** - Custom commands for common actions
- **Command Word Support** - "Flow" prefix for commands (configurable)
- **Pause/Unpause Transcription** - Say "Flow pause" or "Flow unpause"
- **GPU Acceleration** - Optional CUDA support for faster processing
## Requirements
- Ubuntu 20.04+ (other Linux distros may work)
- Python 3.8+
- Working microphone or audio input device
- xdotool (`sudo apt install xdotool` on Ubuntu/Debian)
- PortAudio libraries (`sudo apt install portaudio19-dev`)
## Installation
1. Clone the repository
2. Install dependencies:
```bash
sudo apt install portaudio19-dev python3-dev xdotool ffmpeg
pip install -r requirements.txt
```
## Configuration
Froshine can be configured using either:
1. Command-line arguments
2. Environment variables in a `.env` file
3. System environment variables
Command-line arguments take precedence over environment variables.
### Environment File
Copy the example configuration file to create your own:
```bash
cp .env.example .env
```
Then edit `.env` to customize your settings. See `.env.example` for available options.
### Audio Input Configuration
By default, Froshine uses your system's default audio input device. You can configure the audio input using environment variables:
- `FROSHINE_AUDIO_DEVICE`: Specify a preferred audio device by name or index
- `FROSHINE_LIST_DEVICES`: Set to "1" to list all available audio devices
Examples:
```bash
# List all available audio devices
FROSHINE_LIST_DEVICES=1 python voice_monitor_command_word.py
# Use a specific device by name (partial match)
FROSHINE_AUDIO_DEVICE="USB" python voice_monitor_command_word.py
# Use a specific device by index
FROSHINE_AUDIO_DEVICE="2" python voice_monitor_command_word.py
```
### Whisper Model Selection
Froshine supports different Whisper models for speech recognition. You can choose the model using the `--model` or `-m` flag:
```bash
# Use the tiny English model (fastest, less accurate)
python voice_monitor_command_word.py --model tiny.en
# Use the large v3 model (slower, most accurate)
python voice_monitor_command_word.py --model large-v3
```
Available models:
- `tiny.en`: Tiny model (English only) - Fastest, lowest accuracy
- `base.en`: Base model (English only) - Fast, basic accuracy
- `small.en`: Small model (English only) - Default, good balance
- `medium.en`: Medium model (English only) - Better accuracy, slower
- `large-v3`: Large v3 model (All languages) - Best accuracy, slowest
The default model is `small.en`, which provides a good balance between speed and accuracy.
## Usage with voice_monitor_command_word.py
This script continuously listens for voice input, transcribes it locally with Whisper, and types the transcribed text directly into your active window.
Start the script:
```bash
python3 voice_monitor_command_word.py
```
Begin speaking: The system will detect speech and automatically type the transcribed text into your currently focused application.
**Issue commands:**
- Say "Flow enter" to press Enter.
- Say "Flow save file" to simulate Ctrl+S.
- Say "Flow pause" to stop typing text (commands still work).
- Say "Flow unpause" to resume typing text.
- Stop the script: Say "Flow quit", or press Ctrl+C in the terminal to exit.
## Troubleshooting
**Common Issues:**
- **ALSA/JACK warnings**: Normal and safe to ignore
- **No audio input**:
```bash
# Check recording devices
arecord -l
```
- **Permission issues**:
```bash
sudo usermod -a -G audio $USER
# Reboot after running
```
## Privacy & Security
- All audio processing happens locally
- No internet connection required
- No tracking or data collection
## Copyright
Copyright 2025 Adrian Scott
---
**Acknowledgements**:
- OpenAI Whisper AI model
- WebRTC VAD for voice detection
- PyAudio for audio capture
````
early work in progress
```bash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
sudo apt install xdotool
````
```
WINDOW_ID=$(xdotool search --name "\(Workspace\) \- Windsurf")
echo $WINDOW_ID
xdotool windowactivate --sync $WINDOW_ID; xdotool type --window $WINDOW_ID --delay 0 "windsurf test froshine"
```
Current mechanism is to start voice recorder, voice_to_ide.sh, then click in the field of Windsurf I want it to go into.
Next step: voice detection to automatically fire up the recorder.
After that: voice commands to choose window, and especially use Freepoprompt and o1-xml-parser to update files.