modelscope/FunClip

What it solves

FunClip is an automated video clipping tool that eliminates the need for manual scrubbing through long videos to find specific moments. It allows users to extract video segments based on spoken text, specific speakers, or AI-driven content analysis, significantly speeding up the process of creating highlights or short-form content.

How it works

The tool uses a pipeline of speech and vision models to analyze video files:

1. Speech-to-Text: It employs the Paraformer series (including Paraformer-Large and SeACo-Paraformer for hotword customization) to transcribe audio and predict precise timestamps.
2. Speaker Identification: It integrates the CAM++ model to recognize different speakers, allowing users to clip segments based on who is talking.
3. AI-Assisted Selection: It can route transcripts to LLMs (via OrcaRouter) or use video-understanding models like TwelveLabs Pegasus to identify highlight segments based on natural language prompts.
4. Clipping: Once segments are identified via text or AI, the tool automatically trims the video and can generate corresponding SRT subtitles.

Who it’s for

• Content Creators: Who need to quickly turn long-form videos into short clips.
• Video Editors: Looking to automate the tedious process of transcription-based trimming.
• Developers: Who want a locally deployable, open-source tool for automated video processing.

Highlights

• Industrial-grade ASR: Uses high-performance Chinese ASR models with accurate timestamp prediction.
• Speaker-based Clipping: Ability to trim videos based on auto-recognized speaker IDs.
• LLM Integration: Supports AI-driven clipping using external LLM gateways and video-reasoning models.
• Flexible Interface: Provides both a Gradio web UI and a command-line interface for automation.
• Multilingual Support: Compatible with various models for English, Japanese, and multiple Chinese dialects.