AutoClip
Automated Video Highlight Slicing Tool
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Description
#AI Editing #Video Slicing #Open Source Project #GitHub #Short Video Tool #Automated Editing
Today, I want to share an open-source tool suitable for slicing long videos—AutoClip.
It is an AI automatic slicing system that can take long videos from downloading, analyzing, editing, to generating compilations, creating a fully automated pipeline. In the past, when creating derivative videos, one had to manually watch the content, find highlights, slice, title, and compile; now, just by providing a link, the system can automatically run the entire process.
Automatic Video and Subtitle Download: The system will automatically retrieve video content and subtitles to prepare for subsequent analysis.
AI Content Understanding: Uses Tongyi Qianwen to analyze video content, extracting outlines, topics, and key timestamps.
Highlight Scoring: Scores each segment to help filter out the most suitable highlight content for sharing.
Automatic Highlight Segment Extraction: Completes video processing using FFmpeg and automatically generates segment titles.
Compilation Recommendations: Recommends compilation combinations based on content themes, allowing one-click generation of compilation videos.
Real-time Task Progress: Pushes processing progress via WebSocket for easy task status monitoring.
Modern Technology Stack: Utilizes FastAPI, React, Celery, Redis, and supports Docker deployment.
Diverse Applicable Scenarios: Suitable for cutting long videos into short videos, extracting podcast highlights, creating derivative interviews, segmenting live stream replays, batch production of content matrices, and Bilibili highlight slicing.
Future Feature Planning: Development is ongoing for automatic uploading to Bilibili, subtitle editing, and other capabilities.
It is an AI automatic slicing system that can take long videos from downloading, analyzing, editing, to generating compilations, creating a fully automated pipeline. In the past, when creating derivative videos, one had to manually watch the content, find highlights, slice, title, and compile; now, just by providing a link, the system can automatically run the entire process.
Software Features
Multi-source Input: Supports input from YouTube / Bilibili links, and also allows local video uploads.Automatic Video and Subtitle Download: The system will automatically retrieve video content and subtitles to prepare for subsequent analysis.
AI Content Understanding: Uses Tongyi Qianwen to analyze video content, extracting outlines, topics, and key timestamps.
Highlight Scoring: Scores each segment to help filter out the most suitable highlight content for sharing.
Automatic Highlight Segment Extraction: Completes video processing using FFmpeg and automatically generates segment titles.
Compilation Recommendations: Recommends compilation combinations based on content themes, allowing one-click generation of compilation videos.
Real-time Task Progress: Pushes processing progress via WebSocket for easy task status monitoring.
Modern Technology Stack: Utilizes FastAPI, React, Celery, Redis, and supports Docker deployment.
Diverse Applicable Scenarios: Suitable for cutting long videos into short videos, extracting podcast highlights, creating derivative interviews, segmenting live stream replays, batch production of content matrices, and Bilibili highlight slicing.
Future Feature Planning: Development is ongoing for automatic uploading to Bilibili, subtitle editing, and other capabilities.
