# mli/autocut

用文本编辑器剪视频

Repository: https://github.com/mli/autocut
Canonical: https://ross.abutalabs.com/products/autocut
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-10-05T15:59:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1405, "days_push": 697, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7790, forks 827 (observed 2026-08-28T04:10:05.022012+00:00)

## What it is
AutoCut is a Python CLI tool that automatically transcribes video audio into subtitles using Whisper, then cuts video segments based on which sentences you keep in a Markdown file. It lets you edit videos by editing text instead of using video editing software.

## Use cases
- cut videos by editing a text file
- auto-generate subtitles for recorded videos
- remove unwanted sentences from a screencast recording
- transcribe videos to srt and markdown files
- merge selected video clips into one output
- edit OBS recordings without a video editor

## When to choose
- you record talking-head or screencast videos and want fast text-based editing
- you need automatic transcription and subtitle generation
- you prefer editing Markdown over timeline-based video editors

## When to avoid
- you need complex multi-track or visual effects editing
- your videos have poor audio quality that breaks transcription
- you need a GUI video editor

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: speech-recognition, video-processing, cli, nlp
- domain: speech-processing, media, developer-tools
- platform: windows, python, cli
- tags: video-editing, subtitles, whisper, transcription, srt, text-based-editing, video, linux, macos, docker

## Member repositories
- mli/autocut (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:05.022012+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:34:39.687175+00:00, confidence not recorded.
  - readme: https://github.com/mli/autocut (fetched 2026-08-28T04:10:05.022012+00:00, sha 0d2d114a3f0b)
- Data as of 2026-08-30T08:39:29.467469+00:00.