# Whispy - Fast Speech Recognition CLI
A fast and efficient command-line interface for [whisper.cpp](https://github.com/ggerganov/whisper.cpp), providing automatic speech recognition with GPU acceleration.
[](https://www.youtube.com/watch?v=O6lGEreTbzg)
## Features
- 🚀 **Fast transcription** using whisper.cpp with GPU acceleration (Metal on macOS, CUDA on Linux/Windows)
- 🎯 **Simple CLI interface** for easy audio transcription
- 📁 **Multiple audio formats** supported (WAV, MP3, FLAC, OGG)
- 🌍 **Multi-language support** with automatic language detection
- 📝 **Flexible output** options (stdout, file)
- 🔧 **Auto-detection** of models and whisper-cli binary
- 🏗️ **Automatic building** of whisper.cpp if needed
## Installation
### Quick Install (Recommended)
Install directly from GitHub with automatic setup:
```bash
pip install git+https://github.com/amarder/whispy.git
```
This will automatically:
- Clone whisper.cpp to `~/.whispy/whisper.cpp`
- Build the whisper-cli binary with GPU acceleration
- Install the whispy CLI
### Manual Install
If you prefer to install manually:
#### Prerequisites
- Python 3.7+
- CMake 3.10+ (for building whisper.cpp)
- C++ compiler with C++17 support
- Git (for cloning whisper.cpp)
#### Steps
```bash
# Clone repository
git clone https://github.com/amarder/whispy.git
cd whispy
# Install whispy
pip install -e .
# Clone whisper.cpp if you don't have it
git clone https://github.com/ggerganov/whisper.cpp.git
# Build whisper-cli (or use: whispy build)
cd whisper.cpp
cmake -B build
cmake --build build -j --config Release
cd ..
```
### Requirements
**Basic requirements:**
- Python 3.7+
- CMake (for building whisper.cpp)
- C++ compiler (gcc, clang, or MSVC)
- Git
**For audio recording features:**
- Microphone access
- Audio drivers (pre-installed on most systems)
- Additional Python packages: `sounddevice`, `numpy`, `scipy`
**Supported platforms:**
- 🍎 macOS (Intel & Apple Silicon) with CoreAudio
- 🐧 Linux (with ALSA/PulseAudio)
- 🪟 Windows (with DirectSound)
### Download a model
After installation, download a model to use for transcription:
```bash
# For pip installs from GitHub
cd ~/.whispy/whisper.cpp
sh ./models/download-ggml-model.sh base.en
# For manual installs
cd whisper.cpp
sh ./models/download-ggml-model.sh base.en
# Alternative: Download directly to models/
mkdir -p models
curl -L -o models/ggml-base.en.bin https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin
```
## Usage
### Basic transcription
```bash
# Transcribe an audio file
whispy transcribe audio.wav
# Transcribe with explicit model
whispy transcribe audio.wav --model models/ggml-base.en.bin
# Transcribe with language specification
whispy transcribe audio.wav --language en
# Save transcript to file
whispy transcribe audio.wav --output transcript.txt
# Verbose output
whispy transcribe audio.wav --verbose
```
### Record and transcribe
Record audio from your microphone and transcribe it in real-time:
```bash
# Record and transcribe (press Ctrl+C to stop recording)
whispy record-and-transcribe
# Test microphone before recording
whispy record-and-transcribe --test-mic
# Record with specific model and language
whispy record-and-transcribe --model models/ggml-base.en.bin --language en
# Save both transcript and audio
whispy record-and-transcribe --output transcript.txt --save-audio recording.wav
# Verbose output with device information
whispy record-and-transcribe --verbose
```
### Real-time transcription
Transcribe audio from your microphone in real-time using streaming chunks:
```bash
# Start real-time transcription (press Ctrl+C to stop)
whispy realtime
# With custom settings for faster/slower processing
whispy realtime --chunk-duration 2.0 --overlap-duration 0.5 --silence-threshold 0.02
# Show individual chunks instead of continuous output
whispy realtime --show-chunks
# Save final transcript to file
whispy realtime --output live_transcript.txt
# Test real-time setup
whispy realtime --test-setup
# Verbose mode for debugging
whispy realtime --verbose
```
**Real-time Parameters:**
- `--chunk-duration`: Duration of each audio chunk in seconds (default: 3.0)
- `--overlap-duration`: Overlap between chunks in seconds (default: 1.0)
- `--silence-threshold`: Voice activity detection threshold (default: 0.01)
- `--show-chunks`: Show individual chunk transcripts instead of continuous mode
- `--test-setup`: Test real-time setup without starting transcription
### System information
```bash
# Check system status
whispy info
# Show version
whispy version
# Build whisper-cli if needed
whispy build
```
### Supported audio formats
- WAV
- MP3
- FLAC
- OGG
### Available models
Download models using whisper.cpp's script or directly:
- `tiny.en`, `tiny` - Fastest, least accurate
- `base.en`, `base` - Good balance of speed and accuracy
- `small.en`, `small` - Better accuracy
- `medium.en`, `medium` - High accuracy
- `large-v1`, `large-v2`, `large-v3` - Best accuracy, slower
## Examples
```bash
# Quick transcription with auto-detected model
whispy transcribe meeting.wav
# High-quality transcription
whispy transcribe interview.mp3 --model whisper.cpp/models/ggml-large-v3.bin
# Transcribe non-English audio
whispy transcribe spanish_audio.wav --language es
# Save results and show details
whispy transcribe podcast.mp3 --output transcript.txt --verbose
# Record and transcribe in real-time
whispy record-and-transcribe
# Record with high-quality model and save everything
whispy record-and-transcribe \
--model whisper.cpp/models/ggml-large-v3.bin \
--output meeting-notes.txt \
--save-audio meeting-recording.wav \
--verbose
# Quick voice memo transcription
whispy record-and-transcribe --language en --output memo.txt
# Real-time transcription with live output
whispy realtime
# Real-time transcription with custom settings
whispy realtime --chunk-duration 2.0 --show-chunks --output live_notes.txt
```
## Testing
Whispy includes a comprehensive test suite to ensure the CLI works correctly with different scenarios.
### Running Tests
```bash
# Install development dependencies
pip install -e ".[dev]"
# Run all tests
pytest
# Run tests with verbose output
pytest -v
# Run only unit tests
pytest tests/test_unit.py
# Run only CLI tests
pytest tests/test_cli.py
# Run tests with coverage
pytest --cov=whispy --cov-report=html
# Skip slow tests
pytest --fast
```
### Test Categories
- **Unit tests** (`tests/test_unit.py`): Test individual functions and modules
- **CLI tests** (`tests/test_cli.py`): Test command-line interface functionality
- **Integration tests**: Test full workflows with real audio files
### Using the Test Runner
```bash
# Use the convenience script
python run_tests.py --help
# Run unit tests only
python run_tests.py -t unit -v
# Run with coverage
python run_tests.py -c -v
# Run fast tests only
python run_tests.py -f
```
### Test Requirements
- pytest >= 7.0.0
- pytest-cov >= 4.0.0
- pytest-mock >= 3.10.0
- Sample audio files (JFK sample from whisper.cpp)
### What's Tested
- ✅ CLI commands (help, version, info, transcribe, record-and-transcribe)
- ✅ Audio file transcription with sample files
- ✅ Audio recording from microphone
- ✅ Real-time record-and-transcribe workflow
- ✅ Microphone testing functionality
- ✅ Error handling for invalid files/models/devices
- ✅ Output file generation
- ✅ Language options and verbose modes
- ✅ System requirements and binary detection
- ✅ Model file discovery and validation
## Development
### Project Structure
```
whispy/
├── whispy/
│ ├── __init__.py # Package initialization
│ ├── cli.py # Command-line interface
│ └── transcribe.py # Core transcription logic
├── whisper.cpp/ # Git submodule (whisper.cpp source)
├── models/ # Model files directory
├── pyproject.toml # Project configuration
└── README.md
```
### How it works
Whispy works as a wrapper around the `whisper-cli` binary from whisper.cpp:
1. **Auto-detection**: Finds whisper-cli binary and model files automatically
2. **Subprocess calls**: Runs whisper-cli as a subprocess for transcription
3. **Output parsing**: Captures and returns the transcribed text
4. **Performance**: Gets full GPU acceleration and optimizations from whisper.cpp
### Building from source
```bash
# Clone with whisper.cpp submodule
git clone --recursive https://github.com/your-username/whispy.git
cd whispy
# Install in development mode
pip install -e .
# Build whisper.cpp
whispy build
# OR manually:
# cd whisper.cpp && cmake -B build && cmake --build build -j --config Release
```
### Adding new features
The CLI is built with [Typer](https://typer.tiangolo.com/) and can be easily extended:
```python
@app.command()
def new_command():
"""Add a new command to the CLI"""
console.print("New feature!")
```
## Performance
Whispy automatically uses the best available backend:
- **macOS**: Metal GPU acceleration
- **Linux/Windows**: CUDA GPU acceleration (if available)
- **Fallback**: Optimized CPU with BLAS
Typical performance on Apple M1:
- ~10x faster than real-time for base.en model
- ~5x faster than real-time for large-v3 model
## Troubleshooting
### whisper-cli not found
```bash
# Check if whisper-cli exists
whispy info
# Build whisper-cli
whispy build
# Or build manually
cd whisper.cpp
cmake -B build && cmake --build build -j --config Release
```
### No model found
```bash
# Download a model
cd whisper.cpp
sh ./models/download-ggml-model.sh base.en
# Or specify model explicitly
whispy transcribe audio.wav --model /path/to/model.bin
```
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
### Development setup
```bash
git clone --recursive https://github.com/your-username/whispy.git
cd whispy
pip install -e .
whispy build
```
## Acknowledgments
- [whisper.cpp](https://github.com/ggerganov/whisper.cpp) - Fast C++ implementation of OpenAI's Whisper
- [OpenAI Whisper](https://github.com/openai/whisper) - Original Whisper model
- [Typer](https://typer.tiangolo.com/) - CLI framework
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"description": "# Whispy - Fast Speech Recognition CLI\n\nA fast and efficient command-line interface for [whisper.cpp](https://github.com/ggerganov/whisper.cpp), providing automatic speech recognition with GPU acceleration.\n\n[](https://www.youtube.com/watch?v=O6lGEreTbzg)\n\n## Features\n\n- \ud83d\ude80 **Fast transcription** using whisper.cpp with GPU acceleration (Metal on macOS, CUDA on Linux/Windows)\n- \ud83c\udfaf **Simple CLI interface** for easy audio transcription\n- \ud83d\udcc1 **Multiple audio formats** supported (WAV, MP3, FLAC, OGG)\n- \ud83c\udf0d **Multi-language support** with automatic language detection\n- \ud83d\udcdd **Flexible output** options (stdout, file)\n- \ud83d\udd27 **Auto-detection** of models and whisper-cli binary\n- \ud83c\udfd7\ufe0f **Automatic building** of whisper.cpp if needed\n\n## Installation\n\n### Quick Install (Recommended)\n\nInstall directly from GitHub with automatic setup:\n\n```bash\npip install git+https://github.com/amarder/whispy.git\n```\n\nThis will automatically:\n- Clone whisper.cpp to `~/.whispy/whisper.cpp`\n- Build the whisper-cli binary with GPU acceleration\n- Install the whispy CLI\n\n### Manual Install\n\nIf you prefer to install manually:\n\n#### Prerequisites\n\n- Python 3.7+\n- CMake 3.10+ (for building whisper.cpp)\n- C++ compiler with C++17 support\n- Git (for cloning whisper.cpp)\n\n#### Steps\n\n```bash\n# Clone repository\ngit clone https://github.com/amarder/whispy.git\ncd whispy\n\n# Install whispy\npip install -e .\n\n# Clone whisper.cpp if you don't have it\ngit clone https://github.com/ggerganov/whisper.cpp.git\n\n# Build whisper-cli (or use: whispy build)\ncd whisper.cpp\ncmake -B build\ncmake --build build -j --config Release\ncd ..\n```\n\n### Requirements\n\n**Basic requirements:**\n- Python 3.7+\n- CMake (for building whisper.cpp)\n- C++ compiler (gcc, clang, or MSVC)\n- Git\n\n**For audio recording features:**\n- Microphone access\n- Audio drivers (pre-installed on most systems)\n- Additional Python packages: `sounddevice`, `numpy`, `scipy`\n\n**Supported platforms:**\n- \ud83c\udf4e macOS (Intel & Apple Silicon) with CoreAudio\n- \ud83d\udc27 Linux (with ALSA/PulseAudio) \n- \ud83e\ude9f Windows (with DirectSound)\n\n### Download a model\n\nAfter installation, download a model to use for transcription:\n\n```bash\n# For pip installs from GitHub\ncd ~/.whispy/whisper.cpp\nsh ./models/download-ggml-model.sh base.en\n\n# For manual installs\ncd whisper.cpp\nsh ./models/download-ggml-model.sh base.en\n\n# Alternative: Download directly to models/\nmkdir -p models\ncurl -L -o models/ggml-base.en.bin https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin\n```\n\n## Usage\n\n### Basic transcription\n\n```bash\n# Transcribe an audio file\nwhispy transcribe audio.wav\n\n# Transcribe with explicit model\nwhispy transcribe audio.wav --model models/ggml-base.en.bin\n\n# Transcribe with language specification\nwhispy transcribe audio.wav --language en\n\n# Save transcript to file\nwhispy transcribe audio.wav --output transcript.txt\n\n# Verbose output\nwhispy transcribe audio.wav --verbose\n```\n\n### Record and transcribe\n\nRecord audio from your microphone and transcribe it in real-time:\n\n```bash\n# Record and transcribe (press Ctrl+C to stop recording)\nwhispy record-and-transcribe\n\n# Test microphone before recording\nwhispy record-and-transcribe --test-mic\n\n# Record with specific model and language\nwhispy record-and-transcribe --model models/ggml-base.en.bin --language en\n\n# Save both transcript and audio\nwhispy record-and-transcribe --output transcript.txt --save-audio recording.wav\n\n# Verbose output with device information\nwhispy record-and-transcribe --verbose\n```\n\n### Real-time transcription\n\nTranscribe audio from your microphone in real-time using streaming chunks:\n\n```bash\n# Start real-time transcription (press Ctrl+C to stop)\nwhispy realtime\n\n# With custom settings for faster/slower processing\nwhispy realtime --chunk-duration 2.0 --overlap-duration 0.5 --silence-threshold 0.02\n\n# Show individual chunks instead of continuous output\nwhispy realtime --show-chunks\n\n# Save final transcript to file\nwhispy realtime --output live_transcript.txt\n\n# Test real-time setup\nwhispy realtime --test-setup\n\n# Verbose mode for debugging\nwhispy realtime --verbose\n```\n\n**Real-time Parameters:**\n- `--chunk-duration`: Duration of each audio chunk in seconds (default: 3.0)\n- `--overlap-duration`: Overlap between chunks in seconds (default: 1.0)\n- `--silence-threshold`: Voice activity detection threshold (default: 0.01)\n- `--show-chunks`: Show individual chunk transcripts instead of continuous mode\n- `--test-setup`: Test real-time setup without starting transcription\n\n### System information\n\n```bash\n# Check system status\nwhispy info\n\n# Show version\nwhispy version\n\n# Build whisper-cli if needed\nwhispy build\n```\n\n### Supported audio formats\n\n- WAV\n- MP3 \n- FLAC\n- OGG\n\n### Available models\n\nDownload models using whisper.cpp's script or directly:\n\n- `tiny.en`, `tiny` - Fastest, least accurate\n- `base.en`, `base` - Good balance of speed and accuracy\n- `small.en`, `small` - Better accuracy\n- `medium.en`, `medium` - High accuracy\n- `large-v1`, `large-v2`, `large-v3` - Best accuracy, slower\n\n## Examples\n\n```bash\n# Quick transcription with auto-detected model\nwhispy transcribe meeting.wav\n\n# High-quality transcription\nwhispy transcribe interview.mp3 --model whisper.cpp/models/ggml-large-v3.bin\n\n# Transcribe non-English audio\nwhispy transcribe spanish_audio.wav --language es\n\n# Save results and show details\nwhispy transcribe podcast.mp3 --output transcript.txt --verbose\n\n# Record and transcribe in real-time\nwhispy record-and-transcribe\n\n# Record with high-quality model and save everything\nwhispy record-and-transcribe \\\n --model whisper.cpp/models/ggml-large-v3.bin \\\n --output meeting-notes.txt \\\n --save-audio meeting-recording.wav \\\n --verbose\n\n# Quick voice memo transcription\nwhispy record-and-transcribe --language en --output memo.txt\n\n# Real-time transcription with live output\nwhispy realtime\n\n# Real-time transcription with custom settings\nwhispy realtime --chunk-duration 2.0 --show-chunks --output live_notes.txt\n```\n\n## Testing\n\nWhispy includes a comprehensive test suite to ensure the CLI works correctly with different scenarios.\n\n### Running Tests\n\n```bash\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run all tests\npytest\n\n# Run tests with verbose output\npytest -v\n\n# Run only unit tests\npytest tests/test_unit.py\n\n# Run only CLI tests\npytest tests/test_cli.py\n\n# Run tests with coverage\npytest --cov=whispy --cov-report=html\n\n# Skip slow tests\npytest --fast\n```\n\n### Test Categories\n\n- **Unit tests** (`tests/test_unit.py`): Test individual functions and modules\n- **CLI tests** (`tests/test_cli.py`): Test command-line interface functionality\n- **Integration tests**: Test full workflows with real audio files\n\n### Using the Test Runner\n\n```bash\n# Use the convenience script\npython run_tests.py --help\n\n# Run unit tests only\npython run_tests.py -t unit -v\n\n# Run with coverage\npython run_tests.py -c -v\n\n# Run fast tests only\npython run_tests.py -f\n```\n\n### Test Requirements\n\n- pytest >= 7.0.0\n- pytest-cov >= 4.0.0 \n- pytest-mock >= 3.10.0\n- Sample audio files (JFK sample from whisper.cpp)\n\n### What's Tested\n\n- \u2705 CLI commands (help, version, info, transcribe, record-and-transcribe)\n- \u2705 Audio file transcription with sample files\n- \u2705 Audio recording from microphone\n- \u2705 Real-time record-and-transcribe workflow\n- \u2705 Microphone testing functionality\n- \u2705 Error handling for invalid files/models/devices\n- \u2705 Output file generation\n- \u2705 Language options and verbose modes\n- \u2705 System requirements and binary detection\n- \u2705 Model file discovery and validation\n\n## Development\n\n### Project Structure\n\n```\nwhispy/\n\u251c\u2500\u2500 whispy/\n\u2502 \u251c\u2500\u2500 __init__.py # Package initialization\n\u2502 \u251c\u2500\u2500 cli.py # Command-line interface\n\u2502 \u2514\u2500\u2500 transcribe.py # Core transcription logic\n\u251c\u2500\u2500 whisper.cpp/ # Git submodule (whisper.cpp source)\n\u251c\u2500\u2500 models/ # Model files directory\n\u251c\u2500\u2500 pyproject.toml # Project configuration\n\u2514\u2500\u2500 README.md\n```\n\n### How it works\n\nWhispy works as a wrapper around the `whisper-cli` binary from whisper.cpp:\n\n1. **Auto-detection**: Finds whisper-cli binary and model files automatically\n2. **Subprocess calls**: Runs whisper-cli as a subprocess for transcription\n3. **Output parsing**: Captures and returns the transcribed text\n4. **Performance**: Gets full GPU acceleration and optimizations from whisper.cpp\n\n### Building from source\n\n```bash\n# Clone with whisper.cpp submodule\ngit clone --recursive https://github.com/your-username/whispy.git\ncd whispy\n\n# Install in development mode\npip install -e .\n\n# Build whisper.cpp\nwhispy build\n# OR manually:\n# cd whisper.cpp && cmake -B build && cmake --build build -j --config Release\n```\n\n### Adding new features\n\nThe CLI is built with [Typer](https://typer.tiangolo.com/) and can be easily extended:\n\n```python\n@app.command()\ndef new_command():\n \"\"\"Add a new command to the CLI\"\"\"\n console.print(\"New feature!\")\n```\n\n## Performance\n\nWhispy automatically uses the best available backend:\n\n- **macOS**: Metal GPU acceleration \n- **Linux/Windows**: CUDA GPU acceleration (if available)\n- **Fallback**: Optimized CPU with BLAS\n\nTypical performance on Apple M1:\n- ~10x faster than real-time for base.en model\n- ~5x faster than real-time for large-v3 model\n\n## Troubleshooting\n\n### whisper-cli not 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Please feel free to submit a Pull Request.\n\n### Development setup\n\n```bash\ngit clone --recursive https://github.com/your-username/whispy.git\ncd whispy\npip install -e .\nwhispy build\n```\n\n## Acknowledgments\n\n- [whisper.cpp](https://github.com/ggerganov/whisper.cpp) - Fast C++ implementation of OpenAI's Whisper\n- [OpenAI Whisper](https://github.com/openai/whisper) - Original Whisper model\n- [Typer](https://typer.tiangolo.com/) - CLI framework \n",
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