This project enables searching within YouTube videos using natural language queries, leveraging OpenAI's CLIP model. It is designed for researchers and users interested in content discovery and analysis within video media. The primary benefit is the ability to locate specific moments in videos based on descriptive text rather than relying on manual scrubbing or inaccurate transcriptions.

How It Works

The system extracts frames from a YouTube video at a specified interval. Each frame is then encoded into a vector representation using OpenAI's CLIP model. A natural language search query is similarly encoded by CLIP. The project identifies frames whose embeddings are most similar to the query embedding, effectively matching visual content with textual descriptions.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.7+, OpenAI API key (for CLIP encoding).
• Demo: Available on Google Colab and Hugging Face Spaces.

Highlighted Details

• Utilizes OpenAI's CLIP for joint text-image embedding.
• Demonstrates search capabilities on a San Francisco car driving video.
• Offers a parallel project for searching Unsplash images.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels is provided in the README.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not addressed.

Limitations & Caveats

The project requires an OpenAI API key, which may incur costs. The effectiveness of the search is dependent on the quality of CLIP's embeddings and the frame extraction interval.