Detect laughter and humorous segments in audio or video files.
npx skills add https://github.com/akrindev/trimer-clip --skill laughter-detector
Please help me install this Agent Skill. Skill: laughter-detector Source: https://github.com/akrindev/trimer-clip/tree/main/skills/laughter-detector Command: npx skills add https://github.com/akrindev/trimer-clip --skill laughter-detector
## What problem does it solve? Detect laughter and humorous segments in audio or video to identify funny moments and audience reactions, enabling faster content creation and analysis.
## Core Features & Use Cases
### Quick Start Run the laughter-detector on a video file to extract laughter segments using the preferred detection method.
High-intent search queries and answers about installing and using this skill.
To detect laughter in video audio, you can use a laughter-detector to identify humorous segments and audience reactions by applying audio feature detection, AI models, or keyword-based transcript analysis.
Yes, you can use keyword-based detection from transcripts to locate laughs in podcasts, enabling faster content analysis by scanning long-form conversational content for specific laughter indicators.
Generating viral clips from comedy videos is best achieved by detecting laughter and humorous segments, then using those identified timestamps within a video clipping pipeline to assemble the final clips.
Yes, laughter detection works with both audio feature analysis and AI models, providing robust coverage across media formats to accurately identify audience reactions and funny moments.
You can analyze audience sentiment across transcripts and audio for content creation by detecting laughter and humorous segments, which helps locate funny moments and gauge audience response.
You do not need a video clipping pipeline to detect humor, but integrating laughter detection with a clipping pipeline is necessary to assemble viral clips and export the final video segments.
jscraikExtracts transcripts, downloads media, and retrieves subtitles from video and audio sources using yt-dlp and ffmpeg.
abdu6666Analyze video transcript files to identify viral-worthy segments for short clips.
akrindevAnalyze video sentiment and emotion to produce sentiment scores, peaks, and timelines.
Lawliet2004Classify emotions in speech audio into categories like angry, happy, or sad.
