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What's inside
- Skill name
chunk-process- Description
- None
- License
- Apache-2.0
- Instructions
- 84 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- missing YAML frontmatter
- name: missing
- description: missing
Files (5)
- SKILL.md
- batch_transcribe.py
- mlx_transcribe.py
- process_chunks.py
- smart_chunk.py
SKILL.md (first lines)
# Chunk Process Skill
Smart video chunking and MLX-accelerated transcription for long-form content.
## Problem Solved
- Raw footage too long for single Gemini upload (~47 min = 5GB+)
- Need word-level timestamps for precise cutting
- Fixed-length chunks break mid-sentence
## Smart Chunking
Instead of fixed 5-minute segments, `smart_chunk.py` finds natural break points:
```bash
python skills/chunk-process/smart_chunk.py raw_footage.mp4 -o chunks/
```
**How it works:**
1. Detect silence regions (>500ms gaps)
2. Target 2.5-3.5 minute chunks
3. Split at natural pauses, not mid-sentence
4. Output: `chunk_001.mp4`, `chunk_002.mp4`, ...
**Options:**
- `--min-chunk 150`: Minimum chunk length (seconds)
- `--max-chunk 210`: Maximum chunk length (seconds)
- `--silence-thresh -40`: Silence detection threshold (dB)
## MLX Transcription
`mlx_transcribe.py` uses MLX-accelerated Qwen3-ASR for fast transcription on Mac:
```bash
# Single file
python skills/chunk-process/mlx_transcribe.py audio.wav -o transcript.json
# Batch process chunks
python skills/chunk-process/mlx_transcribe.py chunks/ --batch --word-timestamps
```
**Features:**
- 3-5x faster than CPU on Apple Silicon
- Auto language detection (Chinese/English/Mixed)
- Word-level timestamps (~30ms precision)
- Outputs: `transcript.json` with segments + words
## Combined Pipeline
```bash
# 1. Smart chunk the video
python skills/chunk-process/smart_chunk.py raw.mp4 -o chunks/
# 2. Transcribe all chunks
python skills/chunk-process/mlx_transcribe.py chunks/ --batch --word-timestamps
# Output: chunks/transcript.json (merged from all chunks)
```
## Output Format
```json
{
"language": "English",
"segments": [
{"text": "First sentence.", "start": 0.0, "end": 2.5},
{"text": "Second sentence.", "start": 2.5, "end": 5.0}
],
"words": [
{"text": "First", "start": 0.0, "end": 0.3},
{"text": "sentence", "start": 0.3, "end": 0.8}
],
"full_text": "First sentence. Second sentence..."
}
```
## Why Smart Chunking?
| Fixed Chunks | Smart Chunks |
|--------------|--------------|
| Breaks mid-word | Breaks at pauses |
| 5 min arbitrary | 2.5-3.5 min natural |
| Hard cuts | Clean transitions |
| Timestamp gaps | Continuous timeline |From Nuva-Lab/vibecut, Apache-2.0.
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Audio processing utilities - noise reduction, normalization, enhancement
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transcribe-audio
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Signals
- Stars
- 6
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- Jan 31, 2026 (9 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/Nuva-Lab/vibecut
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from Nuva-Lab
- 0.467score
align-captions
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Generate karaoke-style word-level timestamps by aligning script text to audio using Qwen3-ForcedAligner + jieba for Chinese word segmentation.
SkillDFreshness: D★ 6Claude Code +9 - 0.461score
audio-process
Nuva-Lab
Audio processing utilities - noise reduction, normalization, enhancement
SkillDFreshness: D★ 6Claude Code +9 - 0.461score
extract-clip
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Extract a video segment using FFmpeg with precise start/end times
SkillDFreshness: D★ 6Claude Code +9 - 0.461score
find-golden-segments
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Find naturally clean, coherent video segments worth keeping (selection over repair)
SkillDFreshness: D★ 6Claude Code +9 - 0.461score
transcribe-audio
Nuva-Lab
ASR with ~30ms timestamp precision using Qwen3-ASR + ForcedAligner
SkillDFreshness: D★ 6Claude Code +9 - 0.461score
transcribe-clip
Nuva-Lab
Transcribe a video clip using Gemini to get timestamped segments for captions
SkillDFreshness: D★ 6Claude Code +9
Cite this page
chunk-process skill. Agent Mart. https://agentmart.dev/skills/nuva-lab-chunk-process (last updated 2026-10-24)