Summary

AutoClip is an AI-powered system designed for automated video clipping and highlight generation. It targets users who need to efficiently extract key segments from online videos (YouTube, Bilibili) or local files, offering a streamlined content creation workflow. The primary benefit is significant time savings through intelligent automation of video analysis and editing tasks.

How It Works

The system employs a modern, decoupled architecture featuring a React/TypeScript frontend and a FastAPI backend. Video processing and AI analysis are handled asynchronously via a Celery task queue, with Redis for caching and SQLite for data persistence. Core AI capabilities, including content understanding and highlight identification, are powered by the Tongyi Qianwen large language model. Real-time progress is communicated to the user via WebSocket.

Quick Start & Requirements

Installation is supported via Docker (recommended) or local setup.

• Docker: Requires Docker 20.10+ and Docker Compose 2.0+.
• Local: Needs Python 3.8+ (3.9+ recommended), Node.js 16+ (18+ recommended), Redis 6.0+ (7.0+ recommended), and FFmpeg.
• Resources: Minimum 4GB RAM (8GB+ recommended) and 10GB of free storage are advised.
• Setup: One-click scripts (docker-start.sh, start_autoclip.sh) are provided. The GitHub repository is the primary source for setup instructions: https://github.com/zhouxiaoka/autoclip.git.

Highlighted Details

• Multi-Platform Support: Downloads videos and subtitles from YouTube and Bilibili, also supports local file uploads.
• AI-Driven Analysis: Utilizes AI for content summarization, topic identification, highlight scoring, and intelligent collection recommendations.
• Automated Clipping: Automatically segments videos into highlights and generates video compilations.
• Real-time Feedback: Asynchronous processing with WebSocket for live progress updates and task status.
• Modern Stack: Built with React, TypeScript, Ant Design for the frontend and FastAPI for the backend.

Maintenance & Community

The project is maintained by the "AutoClip Team". Primary community interaction and support channels are GitHub Issues and Discussions. A roadmap indicates planned features like Bilibili upload and subtitle editing, suggesting active development. No specific community forums like Discord or Slack are mentioned.

Licensing & Compatibility

AutoClip is released under the permissive MIT License. This license allows for broad usage, modification, and distribution, including within commercial and closed-source applications, with minimal restrictions beyond attribution.

Limitations & Caveats

Several key features are marked as "under development" (【开发中】), including mobile support, automated Bilibili uploads, subtitle editing capabilities, and advanced Bilibili account management. The system relies on an external API key for the Tongyi Qianwen model (DashScope), requiring configuration and potentially incurring costs. Resource requirements for AI processing are substantial.