Smart Audiobook Reader

A Python application that converts PDF files into high-quality audiobooks using local AI models. Optimized for Intel Core Ultra 7 (Meteor Lake) processors with iGPU acceleration.

Features

• 📚 PDF Processing: Extract text from PDF documents with intelligent chunking
• 🤖 AI-Powered Text Processing: Uses Ollama with local LLM models for text enhancement
• 🎤 High-Quality TTS: Piper neural text-to-speech for natural voice generation
• ⚡ Intel Core Ultra 7 Optimization: GPU acceleration and multi-core processing
• 🎵 Audio Streaming: Real-time playback with pygame
• 🔧 Configurable: Customizable voice models, processing parameters, and output settings

System Requirements

• Processor: Intel Core Ultra 7 (Meteor Lake) recommended
• RAM: 8GB+ (16GB recommended for large documents)
• Storage: 2GB+ for models and temporary files
• OS: Linux (tested on Arch Linux)
• Docker: Required for Ollama container

Installation

1. Install Dependencies

# Using uv (recommended)
uv sync

# Or using pip
pip install -r requirements.txt

2. Setup Ollama

# Start Ollama container (optimized for Intel iGPU)
./start-ollama.sh

# Pull a lightweight model (recommended for Meteor Lake)
docker exec -it ollama-intel ollama pull llama3.2:3b

3. Setup Piper TTS

# Run the setup script
./setup_piper.sh

Usage

Basic Usage

# Convert PDF to audiobook and play immediately
python ireader.py path/to/your/document.pdf

# Convert and save to specific directory
python ireader.py path/to/your/document.pdf ./my_audiobook

Advanced Configuration

Edit config.json to customize settings:

{
  "ollama_host": "http://localhost:11434",
  "ollama_model": "llama3.2:3b",
  "voice_model": "lessac/en_US/lessac-medium.onnx",
  "chunk_size": 1000,
  "max_concurrent_tts": 2,
  "gpu_acceleration": true,
  "speed": 1.0,
  "pitch": 1.0
}

Configuration Options

Setting	Description	Default
ollama_model	Ollama model for text processing	llama3.2:3b
voice_model	Piper TTS voice model	lessac-medium
chunk_size	Text chunk size (characters)	1000
max_concurrent_tts	Concurrent TTS processes	2
gpu_acceleration	Use GPU acceleration	true
speed	Speech speed multiplier	1.0
pitch	Voice pitch adjustment	1.0

Intel Core Ultra 7 Optimization

The application is specifically optimized for Intel Core Ultra 7 processors:

• iGPU Acceleration: Utilizes Intel Arc Graphics for neural network processing
• Multi-Core Processing: Concurrent TTS generation optimized for P-cores and E-cores
• Memory Management: Intelligent chunking to balance RAM usage and performance
• Thermal Optimization: Balanced workload to prevent thermal throttling

Voice Models

Available Models

• Lessac (Medium): Natural, clear voice - Default choice
• Additional models: Can be downloaded from Piper Voices

Adding New Voice Models

1. Download model files to ./models/ directory
2. Update voice_model in config.json
3. Restart the application

Troubleshooting

Common Issues

1. Ollama Connection Failed

   # Check if Ollama is running
   docker ps | grep ollama
   
   # Restart if needed
   ./start-ollama.sh

2. Piper Not Found

   # Reinstall Piper
   ./setup_piper.sh

3. Audio Playback Issues

   # Install audio system
   sudo pacman -S pulseaudio alsa-utils
   
   # Test audio
   speaker-test -c 2

4. GPU Acceleration Not Working

   • Ensure Intel GPU drivers are installed
   • Check gpu_acceleration is set to true in config
   • Verify Docker has GPU access

Performance Tips

• Use llama3.2:3b for best performance on Meteor Lake
• Adjust chunk_size based on document complexity
• Increase max_concurrent_tts for multi-core optimization
• Use SSD storage for temporary files

Development

Project Structure

ireader/
├── ireader.py          # Main application
├── config.json         # Configuration file
├── setup_piper.sh      # Piper setup script
├── start-ollama.sh     # Ollama startup script
├── models/             # Voice model storage
└── pyproject.toml      # Project dependencies

Adding Features

1. Modify ireader.py for core functionality
2. Update config.json for new settings
3. Test with various PDF documents
4. Ensure Intel Core Ultra 7 compatibility

License

This project is open source. Please refer to the license file for details.

Contributing

Contributions are welcome! Please ensure:

• Code follows Python best practices
• Intel Core Ultra 7 optimization is maintained
• Tests are added for new features
• Documentation is updated

Support

For issues and questions:

1. Check the troubleshooting section
2. Verify system requirements
3. Test with minimal PDF files
4. Check logs for error details