seefreed Semantic Video Clipper
Overview
Segment a long video into clips by having AI analyze subtitle semantics and identify natural topic boundaries. The lightweight Python script handles parsing, timing calculation, and FFmpeg clipping based on AI-provided segment indices. Output clips and matching subtitles with filenames basename_<index>.* in same directory as the source video.
Workflow
Step 1: AI Analysis
- Read full subtitle content (.vtt or .srt)
- Understand semantic flow and topic transitions
- Identify natural split points that:
- Align with complete sentence endings
- Occur at topic shifts (new concepts, examples, recaps)
- Fit within duration constraints (typically 25-60 seconds)
- Return segment ranges as cue index pairs:
[(0, 12), (12, 25), (25, 40), ...]
Step 2: Python Script Execution
- Parse subtitles into Cue objects with timing
- Convert cue indices to time ranges
- Call FFmpeg in parallel to clip video segments
- Shift subtitle times so each segment starts at 00:00
- Save output files as
basename_1.mp4+basename_1.vtt,basename_2.*, etc.
Scripted execution
Script location: skills/semantic-video-clipper/scripts/clip_video.py
Option 1: From skill directory (recommended)
cd skills/semantic-video-clipper
python3 scripts/clip_video.py /path/video.mp4 /path/subtitles.vtt "0-12,12-25,25-40"
Option 2: From any location
python3 skills/semantic-video-clipper/scripts/clip_video.py /path/video.mp4 /path/subtitles.vtt "0-12,12-25,25-40"
The segments argument uses 0-based cue indices:
"0-12,12-25,25-40"- Three segments: cues 0-11, 12-24, 25-39
Optional flags:
--workers 4 # Number of parallel workers (default: 4)
--dry-run # Only print segment plan without clipping
Dependencies
ffmpeg(external - must be in PATH)
Notes:
- AI handles semantic analysis; Python script only handles parsing, timing, and FFmpeg clipping.
- Output files always land beside the source video, named with an underscore plus 1-based index.
- Parallel processing (
--workers) provides 3-4x speedup for multi-clip videos.