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An intelligent system that automatically generates engaging podcast conversations using LLMs and text-to-speech technology.
An intelligent system that automatically generates engaging podcast conversations using LLMs and text-to-speech technology.
Listen to sample podcasts generated using Podcast-LLM:
Install using pip:
pip install podcast-llm
Set up environment variables in .env
:
OPENAI_API_KEY=your_openai_key
GOOGLE_API_KEY=your_google_key
ELEVENLABS_API_KEY=your_elevenlabs_key
TAVILY_API_KEY=your_tavily_key
ANTHROPIC_API_KEY=your_anthropic_api_key
Generate a podcast about a topic:
# Research mode (default) - automatically researches the topic
podcast-llm "Artificial Intelligence"
# Context mode - uses provided sources
podcast-llm "Machine Learning" --mode context --sources paper.pdf https://example.com/article
Options:
# Customize number of Q&A rounds per section
podcast-llm "Linux" --qa-rounds 3
# Disable checkpointing
podcast-llm "Space Exploration" --checkpoint false
# Generate audio output
podcast-llm "Quantum Computing" --audio-output podcast.mp3
# Generate Markdown output
podcast-llm "Machine Learning" --text-output podcast.md
Customize voices and other settings in config/config.yaml
Launch the Gradio web interface:
# Start the web UI
podcast-llm-gui
This launches a user-friendly web interface where you can:
This project is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
This means you are free to:
Under the following terms:
For commercial use, please contact evandempsey@gmail.com to obtain a commercial license.
The full license text can be found at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
This project was inspired by podcastfy, which provides a framework for generating podcasts using LLMs.
This implementation differs by automating the research and content gathering process, allowing for fully autonomous podcast generation about any topic without requiring manual research or content curation.
FAQs
An intelligent system that automatically generates engaging podcast conversations using LLMs and text-to-speech technology.
We found that podcast-llm demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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