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AI system for automated video editing
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Summary This project presents an AI-powered automatic video editing system designed to intelligently analyze video content and generate customized edited clips based on user-defined requirements. It targets users and developers seeking to automate video post-production workflows, offering significant benefits by reducing manual editing effort and accelerating content creation through sophisticated AI analysis and generative capabilities.
How It Works The system's core functionality relies on advanced Computer Vision (CV) and Machine Learning (ML) models to perform in-depth analysis of video content, identifying key elements and scenes. It supports a diverse range of pre-defined video style templates, catering to various output formats such as social media, commercial advertisements, and educational materials. A key differentiator is its integration of generative AI for content creation, encompassing text generation, image synthesis, and speech synthesis to enrich video narratives. The system is built with extensibility in mind, offering a robust FastAPI-based API service that facilitates programmatic control, efficient batch processing, and seamless integration with external protocols like the Model Context Protocol (MCP). Furthermore, it incorporates cutting-edge features through Coze integration, enabling dynamic digital human performances and intelligent, context-aware image insertion directly into video streams.
Quick Start & Requirements
git clone https://github.com/LumingMelody/Ai-movie-clip.git
), navigating into the directory (cd Ai-movie-clip
), and installing Python dependencies (pip install -r requirements.txt
)..env
file with API keys for services like DashScope, OpenAI, Alibaba Cloud OSS, and Coze (including a Coze Workflow ID). A specific resource package must also be downloaded and extracted to the resources/
directory.https://pan.quark.cn/s/5a16054e18eb
Highlighted Details
Maintenance & Community The project is actively maintained by its author, LumingMelody. The README does not specify dedicated community channels such as Discord or Slack, nor does it detail a public roadmap. The project acknowledges contributions and support from Alibaba Cloud DashScope and OpenAI.
Licensing & Compatibility Released under the permissive MIT License, this project is suitable for commercial use and can be integrated into closed-source applications without significant licensing restrictions.
Limitations & Caveats The initial setup is complex, requiring the procurement and configuration of multiple third-party API keys and services. While the system supports automatic sharding for large video files, optimal performance may necessitate GPU acceleration or careful tuning of concurrency settings. The reliance on external AI models and cloud services introduces potential operational costs and dependencies on provider availability and policies.
2 months ago
Inactive