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Scopes bringing cross-video speaker identification and a per-person knowledge base into the catalog, using ListeningBuddy's diarization + voiceprint clustering and PeopleBuddy's per-person dossier generation as a reference design (not code to port — different stack end to end). Deliberately leaves compute placement, cross-platform identity scope, and the access/privacy model as open questions for a follow-up decision before Phase 1 implementation. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QHTx7ivcwi7W6oFg8d7Stx
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Summary
Scopes bringing cross-video speaker identification and a per-person knowledge base into the catalog. The catalog today treats
participants/transcript_textspeaker labels as opaque, source-supplied strings with no identity that persists across videos or platforms (Fireflies and Zoom each diarize independently; nothing here does).Uses two internal tools' already-working implementation of this pattern as a reference design — not code to port, since neither matches this stack:
pyannote.audiodiarizes each recording and produces a per-speaker voice embedding; a clustering pass groups embeddings across the entire corpus, surfaces unnamed clusters with sample clips, and a human names each cluster once — the name applies retroactively to every member. New recordings get auto-suggested/confirmed against known voiceprints by cosine similarity.This ADR is Proposed (exploration) — it records the problem and the reference design without committing to an implementation. It explicitly leaves open, as decisions for maintainers before any Phase 1 code:
Proposes a new event-sourced
Speakeraggregate (following the ADR-008 pattern) withparticipantskept as a synced legacy field, the same precedent ADR-077 §1 set fordestination_outcomes.Test plan
N/A — documentation only (ADR), no code changes.
docs/adr/README.mdregenerated viascripts/gen-adr-index.shand diff reviewed to confirm only the new entry was added.