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Inspecting compiled semantics
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Semantic radar
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Semantic radar needs more MAP space.
Voiceprinting already covers cloud ASR
• Local ASR: embeddings come inline from PyAnnote's native centroids (no extra GPU pass)
• Cloud ASR (no native embeddings): a dedicated extract_speaker_embeddings_task runs on the CPU queue — same voiceprint/fingerprint outcome, different path
LLM speaker ID is a suggestion, never a write
• Gender/age detection runs first so the LLM has that context
• Confidence-scored suggestions surface for manual approval only — repo-wide rule
Independent fan-out
• Indexing, summarization, and analytics all dispatch right after finalize
• None of them block the speaker-identity chain or each other