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name: segmentation-diarization description: "HMM and classifier-based segmentation, silence removal, speaker diarization, music thumbnailing, and Audacity annotation splitting for pyAudioAnalysis." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Segmentation and diarization

Use this sub-skill when the task lives in time-aligned segmentation, speaker clustering, silence pruning, thumbnailing, or annotation slicing.

Route here

• Train or reuse HMM segmentation from labeled WAVs or annotated folders.
• Run mid-term classifier-based segmentation and folder-level evaluation.
• Remove silence from a signal and inspect the kept spans.
• Run speaker diarization and score cluster purity.
• Find music thumbnails from self-similarity.
• Split Audacity-style annotations into per-label WAV clips.

Route elsewhere

• Clip-level classifier or regressor training -> classification-regression.
• Raw short- or mid-term feature extraction -> feature-extraction.
• CLI flag catalogs, media conversion, or legacy wrapper plumbing -> cli-and-io.

Covered APIs

• train_hmm_from_file
• train_hmm_from_directory
• hmm_segmentation
• mid_term_file_classification
• evaluate_segmentation_classification_dir
• silence_removal
• speaker_diarization
• speaker_diarization_evaluation
• music_thumbnailing
• annotation2files
• annotation2folders
• folderAnnotation2folders

Read next

• API reference
• Workflows
• Data formats
• Troubleshooting
• Smoke script

Operating notes

• Prefer WAV inputs and tab-separated .segments sidecars with seconds as the time unit.
• Keep plotting flags off in headless or batch runs.
• HMM model files are the pickle stream written by the HMM trainers.
• Silence-removal and thumbnail CLI wrappers emit derived audio files; use the API directly when you want no file side effects.
• The legacy audioAnalysis.py and audacityAnnotation2WAVs.py entry points are summarized in the references; this skill tree uses bundled package imports and the smoke script instead.