Laughter Detector

This skill enables AI agents to detect laughter and humorous segments in audio or video files.

When to Use

• User wants to find funny moments in a video
• Detecting audience reactions (laughter, applause)
• Creating viral clips from humorous content
• Analyzing podcast or comedy content

Detection Methods

1. Keyword-Based Detection (Default)

Analyzes transcript for laughter-related keywords and phrases:

• laugh, laughter, haha, lmao, lol
• chuckle, giggle, snicker
• (laughing), (laughter)

2. Audio Feature Detection

Analyzes audio characteristics:

• High energy segments
• Repetitive patterns
• Voice characteristics

3. AI Model Detection

Uses trained laughter detection models:

• LaughterSegmentation model
• Custom trained models

Available Scripts

scripts/detect_laughter.py

Detect laughter segments in audio/video.

Usage:

python skills/laughter-detector/scripts/detect_laughter.py <video_path> [options]

Options:

• --method: Detection method (keywords, audio, ai) - default: keywords
• --transcript-path: Path to transcript SRT/VTT file (for keyword detection)
• --threshold: Detection threshold (0.0-1.0) - default: 0.5
• --min-duration: Minimum laughter segment duration (seconds) - default: 0.3
• --output, -o: Output JSON path (default: <video_path>_laughter.json)

Examples:

Detect laughter from transcript:

python skills/laughter-detector/scripts/detect_laughter.py video.mp4 --transcript-path video.srt

Detect with audio analysis:

python skills/laughter-detector/scripts/detect_laughter.py video.mp4 --method audio --threshold 0.4

scripts/detect_from_transcript.py

Detect laughter from transcript file only.

Usage:

python skills/laughter-detector/scripts/detect_from_transcript.py <transcript_path> [options]

Options:

• --keywords: Custom keywords (comma-separated)
• --output, -o: Output JSON path

Example:

python skills/laughter-detector/scripts/detect_from_transcript.py video.srt --keywords "laugh,laughter,haha"

Output Format

{
  "video_path": "video.mp4",
  "method": "keywords",
  "total_laughter_segments": 8,
  "laughter_segments": [
    {
      "segment_number": 1,
      "start_time": 12.5,
      "end_time": 15.2,
      "duration": 2.7,
      "confidence": 0.85,
      "text": "[laughter] That's hilarious!",
      "type": "explicit"
    },
    {
      "segment_number": 2,
      "start_time": 45.0,
      "end_time": 47.8,
      "duration": 2.8,
      "confidence": 0.92,
      "text": "(laughing) I can't believe it",
      "type": "explicit"
    }
  ],
  "total_laughter_duration": 15.5,
  "laughter_percentage": 12.5
}

Integration with Other Skills

After laughter detection, you can use these skills:

• highlight-scanner: Combine laughter with other signals
• video-trimmer: Create clips from laughter segments
• autocut-shorts: Full workflow for creating short clips

Common Workflow

1. User provides video file
2. Transcribe using video-transcriber
3. Detect laughter using this skill
4. Create short clips from funny moments

Tips

• Laughter segments are excellent for viral content
• Combine with scene detection for better cut points
• Longer laughter = higher viral potential
• Consider surrounding context (3-5 seconds before/after)
• Keyword detection is faster, AI model is more accurate

References

• Laughter detection research: Interspeech 2024 papers
• Audio feature extraction: Librosa documentation