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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
AutoClip: AI-powered video clipping and highlight generation · 一款智能高光提取与剪辑的二创工具
| Date | Stars |
|---|---|
| 2026-07-24 | 978 |
| 2026-07-25 | 978 |
| 2026-07-28 | 978 |
| 2026-07-30 | 978 |
| 2026-08-06 | 978 |
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growth rate 0.00%/day
# AutoClip - AI-Powered Video Clipping Tool 🎬 An intelligent video clipping and collection recommendation system based on AI, supporting automatic Bilibili video download, subtitle extraction, intelligent slicing, and collection generation. ## 📋 Table of Contents - [✨ Features](#-features) - [🚀 Quick Start](#-quick-start) - [📁 Project Structure](#-project-structure) - [🔧 Configuration](#-configuration) - [📖 User Guide](#-user-guide) - [🛠️ Development Guide](#️-development-guide) - [🐛 FAQ](#-faq) - [📝 Changelog](#-changelog) - [📄 License](#-license) - [🤝 Contributing](#-contributing) - [📞 Contact](#-contact) ## ✨ Features - 🔥 **Intelligent Video Clipping**: AI-powered video content analysis for high-quality automatic clipping - 📺 **Bilibili Video Download**: Support for automatic Bilibili video download and subtitle extraction - 🎯 **Smart Collection Recommendations**: AI automatically analyzes slice content and recommends related collections - 🎨 **Manual Collection Editing**: Support drag-and-drop sorting, adding/removing slices - 📦 **One-Click Package Download**: Support one-click package download for all slices and collections - 🌐 **Modern Web Interface**: React + TypeScript + Ant Design - ⚡ **Real-time Processing Status**: Real-time display of processing progress and logs ## 🚀 Quick Start ### Requirements #### Development Environment - Python 3.8+ - Node.js 16+ - DashScope API Key or SiliconFlow API Key (for AI analysis) #### Docker Deployment (Recommended) - Docker 20.10+ - Docker Compose 2.0+ - DashScope API Key or SiliconFlow API Key (for AI analysis) ### Installation #### 🐳 Docker Deployment (Recommended) **One-click deployment, no complex environment setup required!** ```bash # 1. Clone the project git clone [email protected]:zhouxiaoka/autoclip_mvp.git cd autoclip_mvp # 2. Configure environment variables cp env.example .env # Edit .env file and configure your API keys # 3. One-click deployment ./docker-deploy.sh ``` **Access URL**: http://localhost:8000 📖 **Detailed Deployment Guide**: [Docker Deployment Guide](DOCKER_DEPLOY.md) #### 🔧 Development Environment 1. **Clone the project** ```bash git clone [email protected]:zhouxiaoka/autoclip_mvp.git cd autoclip_mvp ``` 2. **Install backend dependencies** ```bash # Create virtual environment python3 -m venv venv source venv/bin/activate # Linux/Mac # or venv\Scripts\activate # Windows # Install dependencies pip install -r requirements.txt ``` 3. **Install frontend dependencies** ```bash cd frontend npm install cd .. ``` 4. **Configure API keys** ```bash # Copy example configuration file cp data/settings.example.json data/settings.json # Edit configuration file and add your API key # Choose between DashScope and SiliconFlow APIs: # For DashScope: { "api_provider": "dashscope", "dashscope_api_key": "your-dashscope-api-key", "model_name": "qwen-plus", "chunk_size": 5000, "min_score_threshold": 0.7, "max_clips_per_collection": 5, "default_browser": "chrome" } # For SiliconFlow: { "api_provider": "siliconflow", "siliconflow_api_key": "your-siliconflow-api-key", "siliconflow_model": "Qwen/Qwen2.5-72B-Instruct", "chunk_size": 5000, "min_score_threshold": 0.7, "max_clips_per_collection": 5, "default_browser": "chrome" } ``` ### Start Services #### Method 1: Using startup script (Recommended) ```bash chmod +x start_dev.sh ./start_dev.sh ``` #### Method 2: Manual startup ```bash # Start backend service source venv/bin/activate python backend_server.py # Open new terminal, start frontend service cd frontend npm run dev ``` #### Method 3: Command line tool ```bash # Process local video files python main.py --video input.mp4 --srt input.srt --project-name "My Project" # Process existing project python main.py --project-id <project_id> # List all projects python main.py --list-projects ``` ### Access URLs #### Docker Deployment - 🌐 **Frontend Interface**: http://localhost:8000 - 📚 **API Documentation**: http://localhost:8000/doc
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matched fp:95be7f103d4b71e7, topic:llm
matched fp:95be7f103d4b71e7, topic:video-editing