Pause Detector

Detects pauses and low-energy segments using local dynamic thresholds on pre-computed energy data. Unlike global threshold methods, this compares energy to surrounding context, avoiding false positives when speaker volume varies.

Use Cases

• Detecting natural pauses in lectures
• Finding board-writing silences
• Identifying breaks between sections

Usage

python3 /root/.claude/skills/pause-detector/scripts/detect_pauses.py \
    --energies /path/to/energies.json \
    --output /path/to/pauses.json

Parameters

• --energies: Path to energy JSON file (from energy-calculator)
• --output: Path to output JSON file
• --start-time: Start analyzing from this second (default: 0)
• --threshold-ratio: Ratio of local average for low energy (default: 0.5)
• --min-duration: Minimum pause duration in seconds (default: 2)
• --window-size: Local average window size (default: 30)

Output Format

{
  "method": "local_dynamic_threshold",
  "segments": [
    {"start": 610, "end": 613, "duration": 3},
    {"start": 720, "end": 724, "duration": 4}
  ],
  "total_segments": 11,
  "total_duration_seconds": 28,
  "parameters": {
    "threshold_ratio": 0.5,
    "window_size": 30,
    "min_duration": 2
  }
}

How It Works

1. Load pre-computed energy data
2. Calculate local average using sliding window
3. Mark seconds where energy < local_avg × threshold_ratio
4. Group consecutive low-energy seconds into segments
5. Filter segments by minimum duration

Dependencies

• Python 3.11+
• numpy
• scipy

Example

# Detect pauses after opening (start at 221s)
python3 /root/.claude/skills/pause-detector/scripts/detect_pauses.py \
    --energies energies.json \
    --start-time 221 \
    --output pauses.json

# Result: Found 11 pauses totaling 28 seconds

Parameters Tuning

Notes

• Requires energy data from energy-calculator skill
• Use --start-time to skip detected opening
• Local threshold adapts to varying speaker volume