PodBro

PodBro is a Python-based tool that generates podcasts from various content sources including YouTube videos, web pages, PDFs, and audio/video files. It uses OpenAI's APIs for content processing and text-to-speech conversion.

Features

• Multi-source content extraction from:
  • YouTube videos (with transcript support)
  • Web pages and articles
  • PDF documents
  • Audio/video files
• Text-to-speech conversion using:
  • Microsoft Edge TTS
  • OpenAI TTS
• Automatic content processing and podcast script generation
• Support for multiple voices in generated podcasts

Installation

# Clone the repository
git clone https://github.com/detunjisamuel/podbro.git

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
export OPENAI_API_KEY=your_api_key

Usage

Command Line Interface

# Basic usage with a YouTube URL
python -m podbro.main create --url "https://www.youtube.com/watch?v=example"

# Combine multiple sources
python -m podbro.main create \
    --url "https://www.youtube.com/watch?v=example" \
    --file "presentation.pdf" \
    --text "Additional content to include" \
    --tts-model edge

Python API

from podbro.main import PodcastGenerator, TTSModel

# Initialize generator
generator = PodcastGenerator()

# Create podcast from multiple sources
result_file = generator.create_podcast(
    urls=["https://www.youtube.com/watch?v=example"],
    files=["document.pdf"],
    text="Additional content",
    tts_model=TTSModel.EDGE
)

Sample Output

Here's a workflow example:

1. Input: YouTube video about AI advances
2. Content extraction:

# Extract content from YouTube
youtube_parser = YouTube("https://www.youtube.com/watch?v=example")
content = youtube_parser.extract_content_from_source()

3. Generate podcast script:

# Process content and generate script
transcript = generate_podcast_transcript(content)

# Sample output:
"""
Speaker 1: Welcome to today's episode about artificial intelligence...
Speaker 2: That's fascinating! Could you tell us more about...
"""

4. Convert to audio using Edge TTS or OpenAI TTS

Testing

# Run all tests
python -m unittest discover tests

# Run specific test case
python -m unittest tests.test_main.TestPodcastGenerator

Contributing

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

License

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