multimodal-fusion
bybenchflow-ai· 57 skills
Combine visual features (face detection, lip movement analysis) with audio features to improve speaker diarization accuracy in video files. Use OpenCV for face detection and lip movement tracking, then fuse visual cues with audio-based speaker embeddings. Essential when processing video files with multiple visible speakers or when audio-only diarization needs visual validation.
How to install multimodal-fusion?
benchflow-ai/skillsbench/multimodal-fusion
$npx -y skills add benchflow-ai/skillsbench --skill multimodal-fusionInstalls into the current project.
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Run `npx skills use "https://github.com/benchflow-ai/skillsbench" --skill "benchflow-ai/skillsbench/multimodal-fusion"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/benchflow-ai/skillsbench" that are relevant to the current task. Run `npx skills add "https://github.com/benchflow-ai/skillsbench"` and select the relevant skills, then follow their instructions.
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