liqui-speak 0.3.1

One-command setup for real-time audio transcription with LFM2.5-Audio model

Liqui-Speak 🎤

One-command setup for real-time audio transcription using LFM2.5-Audio-1.5B

Liqui-Speak automates the entire setup process for audio transcription, handling system dependencies, model downloads, and format conversions automatically.

🚀 Quick Start

# Install the package
uv tool install liqui-speak

# Run one-time setup (installs everything)
liqui-speak config

# Transcribe any audio file
liqui-speak audio.m4a

✨ Features

• 🔄 Auto-setup: Single command installs all dependencies
• 📁 Format support: M4A, AAC, WAV, MP3, FLAC, and more
• ⚡ Fast conversion: PyDub-based in-memory processing
• 🎯 Cross-platform: macOS, Linux, Windows support
• 📦 Complete automation: Downloads models, binaries, libraries
• 🔧 Zero configuration: Works out of the box
• 📱 macOS Shortcut: Voice-to-clipboard with one keystroke

📋 Installation

Prerequisites

• Python >= 3.12
• libmagic (for audio format detection)
• Package manager: Homebrew (macOS/Linux), apt/yum/pacman (Linux), or Chocolatey (Windows)

Installing libmagic:

# macOS
brew install libmagic

# Ubuntu/Debian
sudo apt-get install libmagic1

# Fedora/RHEL/CentOS
sudo dnf install file-libs

# Arch Linux
sudo pacman -S file

# Windows
pip install python-magic-bin

Install Package

uv tool install liqui-speak

First-time Setup

liqui-speak config

This will:

• Install PortAudio and FFmpeg system dependencies
• Download LFM2.5-Audio-1.5B model files
• Download platform-specific llama.cpp binary
• Install macOS Shortcut for voice transcription (macOS only)
• Verify installation

Quantization Options

Choose model size vs. quality trade-off:

# F16 - Full precision (default, ~3.4GB, best quality)
liqui-speak config

# Q8_0 - 8-bit quantization (~1.8GB)
liqui-speak config --quant Q8_0

# Q4_0 - 4-bit quantization (~1GB, smallest)
liqui-speak config --quant Q4_0

🎤 Usage

Basic Transcription

# Transcribe any audio file (both formats work)
liqui-speak audio.m4a                    # Simple format
liqui-speak transcribe audio.m4a         # Explicit format

# Or with different file types
liqui-speak recording.wav
liqui-speak podcast.mp3

Advanced Options

# Play audio during transcription
liqui-speak audio.m4a --play-audio

# Verbose output
liqui-speak audio.mp3 --verbose

Python API

from liqui_speak import transcribe

# Transcribe audio file
text = transcribe("audio.m4a")
print(text)

� macOS Shortcut

During liqui-speak config, a macOS Shortcut is automatically installed that:

1. Records audio - Start speaking immediately
2. Transcribes - Runs liqui-speak on the recording
3. Copies to clipboard - Ready to paste anywhere

First run permissions: macOS will ask for microphone access, file access, and shell script execution permissions.

�🔧 Configuration

Environment Variables

export LIQUI_SPEAK_MODEL_DIR="/custom/path"
export LIQUI_SPEAK_SAMPLE_RATE="44100"

Setup Directory

Configuration and models are stored in ~/.liqui_speak/

📊 Supported Formats

✅ Direct support: WAV (no conversion needed) ✅ Auto-converted: M4A, AAC, MP3, FLAC, OGG, WMA, ALAC ❌ Not supported: DRM-protected files

All supported formats are automatically converted to WAV internally for optimal transcription performance.

🏗️ Development

Setup Development Environment

# Clone repository
git clone https://github.com/abhishekbhakat/liqui-speak.git
cd liqui-speak

# Install with dev dependencies
make install-dev

# Run quality checks
make lint
make type-check
make test

🧪 Tests

"Tests? Where we're going, we don't need tests." — Doc Brown, probably

The code works on my machine. Ship it. 🚀

🔍 Troubleshooting

"Format not recognized" error

Your file might be M4A with wrong extension. Use:

liqui-speak config  # Will detect and convert automatically

Missing system dependencies

Run setup again:

liqui-speak config --verbose

Model download fails

Check internet connection and available disk space (~2GB needed).

Permission errors

Make sure you have admin/sudo access for system dependency installation.

🚀 Performance

• Setup time: < 5 minutes (first run)
• Conversion speed: < 10% of audio duration
• Memory usage: ~2GB during transcription
• Model size: ~1.5GB

🔗 Dependencies

Python Packages

• pydub - Audio conversion
• huggingface-hub - Model downloads
• python-magic - Format detection

System Dependencies

• portaudio - Audio I/O library
• ffmpeg - Audio format support

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature-name
3. Make changes and test: make quality
4. Commit changes: git commit -am 'Add feature'
5. Push to branch: git push origin feature-name
6. Submit pull request

📞 Support

• Issues: GitHub Issues

🙏 Acknowledgments

• LFM2.5-Audio-1.5B model: LiquidAI team
• llama.cpp: Georgi Gerganov
• PyDub: James Robert
• Hugging Face: Model hosting platform