AI Audio Generator

Transform documents into natural-sounding audio summaries using Azure OpenAI's GPT-4O and GPT-4O-Audio-Preview capabilities.

Features

• 📄 Support for PDF, TXT, DOC, and DOCX files
• 🔍 Multiple processing methods (Vision Only or Text + Vision)
• 🎯 Customizable processing goals
• ⏱️ Adjustable summary lengths
• 🌍 Multiple language support with flags
• 🗣️ Various voice options and tones
• 🎨 Modern, responsive UI with intuitive controls
• 💾 PostgreSQL database for reliable history storage
• 🔄 Rerun capability with modified settings
• 🗑️ Easy deletion of history entries
• 📱 Mobile-friendly design with glass morphism effects

History Management

The application now includes a robust history management system:

• View all processed documents with timestamps
• See processing settings for each entry
• Rerun previous entries with new settings
• Delete individual entries with confirmation
• Clear visual feedback for selected entries
• Automatic audio player updates when selecting entries

Prerequisites

• Docker and Docker Compose (recommended)
• Or for local development:
  • Python 3.8 or higher
  • Poppler (required for PDF processing)
  • PostgreSQL 15 or higher
  • Azure OpenAI API access
  • Azure OpenAI API key and endpoint

Quick Start with Docker (Recommended)

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Set up environment variables: Create a file named keys.env in the project root:

   AZURE_OPENAI_ENDPOINT=your_endpoint_here
   AZURE_OPENAI_API_KEY=your_api_key_here

3. Configure model settings: If needed, edit config.py in the project root with the names of your Azure OpenAI model deployments:

   # Model Deployments
   AZURE_MODELS = {
       'text': os.getenv('AZURE_OPENAI_TEXT_DEPLOYMENT', 'gpt-4o'),
       'audio': os.getenv('AZURE_OPENAI_AUDIO_DEPLOYMENT', 'gpt-4o-audio-preview'),
   }

4. Start the application:

   # Default setup (port 5001)
   docker-compose up -d
   
   # If port 5001 is in use, you can override it:
   PORT=8080 docker-compose up -d

5. Access the application:

   • Default: http://localhost:5001
   • Or if you changed the port: http://localhost:YOUR_PORT

6. View logs:

   docker-compose logs -f

7. Stop the application:

   docker-compose down  # Keeps the database data
   docker-compose down -v  # Removes all data (fresh start)

Local Development Setup

1. Install Poppler:

   • macOS:

     brew install poppler

   • Ubuntu/Debian:

     sudo apt-get install poppler-utils

   • Windows:

     • Download from poppler-windows
     • Extract and add bin directory to PATH

2. Install PostgreSQL:

   • PostgreSQL Downloads
   • Create a database named aoai_audio

3. Create and activate virtual environment:

   python -m venv venv
   
   # Windows
   .\venv\Scripts\activate
   
   # macOS/Linux
   source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

5. Configure environment: Create keys.env with your Azure OpenAI credentials and database URL:

   AZURE_OPENAI_ENDPOINT=your_endpoint_here
   AZURE_OPENAI_API_KEY=your_api_key_here
   DATABASE_URL=postgresql://postgres:postgres@localhost:5432/aoai_audio

6. Run the application:

   python -m flask run --host=0.0.0.0 --port=5001

Troubleshooting

Port Already in Use

If port 5001 is already in use:

1. Docker setup:

   # Use a different port (e.g., 8080)
   PORT=8080 docker-compose up -d

2. Local setup:

   # Use a different port (e.g., 8080)
   python -m flask run --host=0.0.0.0 --port=8080

Database Issues

• Ensure PostgreSQL is running
• Check connection string in keys.env
• For Docker: docker-compose logs db to check database logs

Best Practices

1. Environment Variables:

   • Never commit keys.env (it's in .gitignore)
   • Use strong passwords in production

2. Data Persistence:

   • Database data persists in Docker volume postgres_data
   • Backup regularly in production

3. Security:

   • Change default PostgreSQL password in production
   • Use SSL in production
   • Keep dependencies updated

4. Monitoring:

   • Check application logs: docker-compose logs -f web
   • Check database logs: docker-compose logs -f db