Edit Mind

Key Features

• •Video Indexing and Processing: A background service automatically watches for new video files and queues them for AI-powered analysis. Users simply drop videos into a folder, and Edit Mind handles the rest, ensuring no manual tagging is needed.
• •AI-Powered Video Analysis: Extracts rich metadata including face recognition, transcription, object and text detection, and scene analysis using PyTorch, Whisper, and Gemini or Ollama. This comprehensive AI analysis captures every aspect of video content.
• •Vector-Based Semantic Search: Uses ChromaDB to store embeddings, enabling natural language queries that understand context. Users can search for scenes by spoken words, objects, or people, not just keywords.
• •Desktop App Integration: The optional commercial desktop app integrates directly with Davinci Resolve and Final Cut Pro, allowing users to export specific scenes into their editing timeline with a single click.
• •Self-Hosted Privacy: Runs fully locally on Docker, keeping all video data and analysis on the user's own hardware. No cloud uploads required, ensuring complete privacy for sensitive footage.
• •Multi-Model Embedding Combines frame analysis, transcription, and other metadata into a unified embedding, allowing search across different modalities. This holistic approach yields more accurate scene retrieval.

Use Cases

• •A documentary editor needs to find all interviews where the subject mentions a key event. With Edit Mind, they simply type a natural language query like 'discussion about the 2022 merger' and instantly retrieve every relevant scene.
• •A video archivist managing a large library of corporate training videos wants to locate specific hands-on demonstrations. Using face recognition and object detection, they can search for 'person demonstrating equipment' and find precise moments.
• •A content creator compiles a highlight reel of customer testimonials. They use the transcription search to find all clips containing phrases like 'amazing service' and then export the scenes directly into their editing software via the desktop app integration.
• •A researcher analyzing interview footage for a study needs to find emotional responses. Using emotion analysis (part of scene analysis), they can locate moments where participants show happiness or surprise, saving hours of manual review.
• •A video editor working on a comedy show wants to find all scenes where a specific prop appears. By searching for the object name using object detection, they quickly gather all relevant clips into a bin for editing.

Who is this for?

Video editors, content creators, media archivists, and self-hosted enthusiasts who need to quickly retrieve specific scenes from large video libraries. Also ideal for researchers, journalists, and any professional who works with extensive video footage and requires a private, local AI solution for semantic search without uploading sensitive content to the cloud. Developers interested in contributing to an open-source video indexing project or leveraging Docker-based deployment will also find Edit Mind valuable.