What this skill does

# percept-speaker-id Speaker identification and management for multi-person conversations. ## What it does Tracks who said what in conversations. Maps anonymous speaker labels (SPEAKER_0, SPEAKER_1) to real names, maintains speaker profiles, and gates voice command authorization. ## When to use - User asks "who said that?" or wants speaker-attributed transcripts - User wants to configure which people can trigger voice commands - Agent needs to know who is speaking in a multi-person conversation ## Requirements - **percept-listen** skill installed and running - **Omi pendant** (provides `is_user` flag for primary speaker) ## How it works 1. Omi sends transcript segments with speaker labels (SPEAKER_0, SPEAKER_1, etc.) 2. Percept resolves labels to names using the speakers registry 3. `is_user` flag from Omi identifies the pendant wearer as the primary speaker 4. Speaker profiles track first/last seen timestamps and authorization status ## Speaker registry Located at `percept/data/speakers.json`: ```json { "SPEAKER_00": { "name": "David", "is_owner": true, "approved": true }, "SPEAKER_01": { "name": "Rob", "is_owner": false, "approved": true } } ``` Manage via Percept dashboard (port 8960) → Settings → Speakers. ## Authorization levels - **Owner** (`is_owner: true`): Full command access, always authorized - **Approved** (`approved: true`): Can trigger wake word commands - **Unknown**: Logged only, commands not executed ## Future: Voice embeddings Planned: pyannote speaker diarization with 192-dim voice embeddings for automatic speaker recognition via cosine similarity. Currently speaker mapping is manual. ## Links - **GitHub:** https://github.com/GetPercept/percept