This project enables users to extract information from YouTube videos by providing a link. It transcribes audio using OpenAI Whisper, generates embeddings for segments with OpenAI's API, and allows users to query the video content via a chat interface. The target audience includes researchers, content creators, and anyone needing to quickly digest or analyze YouTube video information.

How It Works

The system downloads YouTube video audio using pytube, then transcribes it with OpenAI's Whisper model. Each transcribed segment is embedded using OpenAI's text-embedding-ada-002 model, creating vector representations of the content. For querying, it uses streamlit-chat and OpenAI's text-davinci-003 model, performing semantic search on the embeddings to retrieve relevant context before generating an answer.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Run the Streamlit server: streamlit run app.py
• Prerequisites: Python, OpenAI API key, pytube, openai, streamlit, whisper (from git+https://github.com/openai/whisper.git).

Highlighted Details

• Leverages OpenAI Whisper for transcription and text-embedding-ada-002 for segment embeddings.
• Enables conversational querying of video content using text-davinci-003.
• Uses pytube for video downloading and streamlit-chat for the interactive interface.

Maintenance & Community

No specific contributor, sponsorship, or community links (Discord, Slack, etc.) are mentioned in the README.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is described as having upcoming features like semantic search with embeddings and emotional analysis charts. The current state and potential limitations for production use are not detailed.