speaker-clustering
bybenchflow-ai· 57 skills
Choose and implement clustering algorithms for grouping speaker embeddings after VAD and embedding extraction. Compare Hierarchical clustering (auto-tunes speaker count), KMeans (fast, requires known count), and Agglomerative clustering (fixed clusters). Use Hierarchical clustering when speaker count is unknown, KMeans when count is known, and always normalize embeddings before clustering.
How to install speaker-clustering?
benchflow-ai/skillsbench/speaker-clustering
$npx -y skills add benchflow-ai/skillsbench --skill speaker-clusteringInstalls into the current project.
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Run `npx skills use "https://github.com/benchflow-ai/skillsbench" --skill "benchflow-ai/skillsbench/speaker-clustering"` 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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