modelscope/FunClip

FunASR-powered video transcription, subtitle generation, and LLM-assisted clipping tool with a local Gradio UI.

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modelscope/FunClip has 6k+ stars and 750+ forks on GitHub, is written in Python, and was last pushed on Oct 8, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 2 hours ago
Added to GitGenius on September 10th, 2026
Created on May 17th, 2023
Open Issues & Pull Requests: 14 (+0)
GitHub issues: Enabled
Number of forks: 759
Total Stargazers: 6,396 (+3)
Total Subscribers: 44 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 43.4 days
Mean response time: 175.1 days
90th percentile: 550.7 days
Tracked items: 55

Maintainer activity

1 person did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits.

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 6% of issues opened in the past year have been closed. Three people close 91% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 9
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 346 days
Stale 30+ days: 8
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (8)
  • needs feedback (5)
  • enhancement (1)
  • question (1)

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Detailed Description

FunClip is a video transcription and editing tool that combines automatic speech recognition with language model assistance through a local Gradio interface.

The tool addresses the workflow of converting video content into transcribed text and generating subtitles by leveraging FunASR for speech recognition. It processes video files to produce transcriptions, automatically generates subtitle files, and uses large language models to help identify and extract relevant clips from longer videos. The local Gradio UI provides an accessible interface for these operations without requiring deployment to external services.

FunClip suits content creators and video producers who need to transcribe multilingual video content, particularly those working with Chinese-language material where the underlying FunASR models show strong performance. It works well for projects requiring both subtitle generation and intelligent clip extraction from raw footage. The tool positions itself as an alternative to cloud-based transcription services by running locally, which appeals to users prioritizing privacy or working in environments with limited internet connectivity.

The project maintains active development with regular commits addressing bug fixes and feature improvements. Pull requests receive timely review and integration into the codebase. The maintainers respond consistently to issues raised by users, indicating ongoing engagement with the community. Documentation updates accompany new features, suggesting attention to keeping guidance current with the tool's capabilities.