Enhancing Video Analysis with MLX: A Guide

Explore the capabilities of the MLX library and leverage the genAI stack on MacOS to interact with any video.

QAVideo.mp4

Key Features

1. Versatile Video Loading: Import videos directly or use YouTube for easy access.
2. Model Selection Flexibility: Choose from a variety of MLX models available on the mlx-community at huggingface.
3. Content Summarization: Efficiently condense video content into Main or AI-driven summaries.

Future Enhancements

4. Interactive Chat Integration: Engage directly with video content through an interactive chat feature.
5. Focused Search in Lengthy Videos: Easily navigate to specific segments in long videos for detailed explanations on chosen topics.

Implementation Details

• Utilize MLX as the foundational framework for model access and loading.
• Facilitate video uploading or download directly from YouTube using specialized libraries.
• Extract audio components from videos with Whisper.
• Ensure language compatibility and accuracy.
• Implement speech-to-text conversion on video content, with translation features as needed.
• Segment text for efficient summarization.
• Employ LLM Models to enhance summary generation.
• Deliver summarized content for user consumption.

Setup and Execution

1. Set up a virtual environment.
2. Install necessary packages from requirements.txt.
3. Execute the application with streamlit run src/app.py.

Acknowledgments

The project builds upon various mlx-related projects, particularly mlx-ui and whisper, focusing primarily on integration and further development.