🎧 AudioBook Generator

Transform any document into professional audiobooks with AI-powered enhancement and high-quality text-to-speech.

✨ Features

• 📄 Multi-format Support: PDF, DOCX, TXT file processing
• 🤖 AI Enhancement: Gemini API transforms text into engaging audiobook narration
• 🎙️ Premium TTS: Edge TTS with multiple voice styles (storytelling, authoritative, conversational)
• 🔍 Smart Q&A: RAG-powered document search and question answering
• 🌐 Modern UI: React frontend with real-time progress tracking
• ⚡ Fast Processing: Optimized pipeline with caching and batch processing

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• Gemini API key

Installation

1. Clone the repository

   git clone https://github.com/AabidMK/Audiobook_generator_-Infosys_Internship_Aug2025.git
   cd Audiobook_generator_-Infosys_Internship_Aug2025

2. Install Python dependencies

   pip install -r requirements.txt

3. Setup environment

   # Create .env file
   echo "GEMINI_API_KEY=your_api_key_here" > .env

4. Install frontend dependencies

   cd frontend
   npm install
   cd ..

Running the Application

Option 1: Full Stack (Recommended)

run_app.bat  # Windows
# or
python run_localhost.py  # Cross-platform

Option 2: Backend Only

python start_api.py

Option 3: Manual Setup

# Terminal 1 - Backend
python start_api.py

# Terminal 2 - Frontend
cd frontend
npm run dev

🎯 Usage

1. Upload Document: Drag & drop or select PDF/DOCX/TXT files
2. Generate Audiobook: Choose voice style and click generate
3. Download: Get enhanced text and high-quality audio files
4. Ask Questions: Use RAG system to query document content

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   React UI      │────│   FastAPI        │────│   Gemini API    │
│   (Frontend)    │    │   (Backend)      │    │   (Enhancement) │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │
                    ┌─────────┼─────────┐
                    │         │         │
            ┌───────▼───┐ ┌───▼────┐ ┌──▼──────┐
            │ Edge TTS  │ │ ChromaDB│ │Text     │
            │ (Audio)   │ │ (RAG)   │ │Extraction│
            └───────────┘ └────────┘ └─────────┘

🛠️ Tech Stack

• Backend: FastAPI, Python 3.8+
• Frontend: React, Vite, Axios
• AI/ML: Google Gemini API, ChromaDB, Sentence Transformers
• TTS: Microsoft Edge TTS
• Text Processing: PyMuPDF, python-docx, BeautifulSoup

📁 Project Structure

├── main.py                 # FastAPI backend server
├── audiobook_generator.py  # Core audiobook logic
├── enhanced_extraction.py  # Text extraction
├── rag.py                 # RAG system
├── frontend/              # React application
├── requirements.txt       # Python dependencies
└── README.md             # This file

🔧 Configuration

Environment Variables

GEMINI_API_KEY=your_gemini_api_key
LM_STUDIO_BASE_URL=http://localhost:1234  # Optional

Voice Styles

• storytelling: Warm, expressive (default)
• authoritative: Deep, confident
• conversational: Natural, friendly
• narrative: Smooth, professional
• dramatic: Dynamic, emotional

🚨 Troubleshooting

Port Conflicts

# Check ports
netstat -ano | findstr :8000

# Kill processes
taskkill /F /PID <PID>

Common Issues

• Indexing failed: Check file permissions and format
• Audio generation failed: Verify Edge TTS installation
• API errors: Validate Gemini API key

📊 Performance

• Text Processing: ~2-5 seconds per page
• AI Enhancement: ~10-30 seconds per chunk
• Audio Generation: ~1-2x real-time speed
• Supported File Size: Up to 50MB documents

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Infosys Internship Program - Project opportunity
• Google Gemini API - AI text enhancement
• Microsoft Edge TTS - High-quality speech synthesis
• ChromaDB - Vector database for RAG

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Made with ❤️ during Infosys Internship 2025