Why it matters

Diarization turns transcripts into useful conversation records. Understanding enables meeting and call apps.

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The architecture

Speech embedding: extract voice fingerprint per speaker (x-vector, d-vector).

Clustering: group similar embeddings; each cluster = one speaker.

Diarization pipelineSegment audiointo short windowsVoice embeddingsper segmentCluster + assignspeaker labelspyannote.audio popular open-source library; commercial options often more accurate
Diarization stages.
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How it works end to end

Segmentation: split audio into short segments (0.5-2 sec).

Embedding: extract voice fingerprint.

Clustering: unsupervised — number of speakers detected or specified.

Alignment: assign each ASR word to nearest speaker.

Tools: pyannote.audio (open), AssemblyAI, Deepgram, Rev (commercial).