Add design doc for speaker identification with Resemblyzer

Voice-based speaker ID triggered by YAMNet speech detection.
Cosine similarity matching against SQLite enrollment DB.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

docs/plans/2026-02-01-speaker-identification-design.md

@@ -0,0 +1,137 @@
+# Speaker Identification: Resemblyzer on Pi 5 CPU
+
+Add voice-based speaker identification to headmic. Runs on CPU alongside YAMNet sound classification — only computes embeddings when speech is detected.
+
+## Model
+
+**Resemblyzer** — GE2E speaker encoder, 256-dim embeddings.
+
+| Spec | Value |
+|------|-------|
+| Library | `resemblyzer` (PyTorch-based) |
+| Embedding | 256-dim float32 |
+| Input | Float32 audio at 16kHz |
+| Inference | ~50-100ms on Pi 5 CPU |
+| Threshold | 0.75 cosine similarity |
+| Trigger | Only when YAMNet detects speech |
+
+## Architecture
+
+```
+sound_classifier_loop (every 0.5s)
+  |
+  +-> YAMNet classifies audio
+  |
+  +-> If category == "speech":
+        +-> Resemblyzer computes 256-dim embedding
+              +-> Cosine similarity against enrolled voices (SQLite)
+                    +-> state.recognized_speaker + confidence
+```
+
+No new threads. Speaker ID runs inside the existing classifier thread.
+
+## Files
+
+| File | Action | Purpose |
+|------|--------|---------|
+| `speaker_id.py` | New | SpeakerRecognizer: Resemblyzer encoder, SQLite DB, cosine matching |
+| `headmic.py` | Modify | Integrate speaker ID into classifier loop, new endpoints, enrollment LED |
+| `sound_id.py` | Modify | Return float32 audio alongside classification for speaker ID |
+
+## speaker_id.py — SpeakerRecognizer Class
+
+```python
+class SpeakerRecognizer:
+    def __init__(self, db_path="voices.db"):
+        # Load Resemblyzer voice encoder
+        # Init SQLite DB
+        # Load embedding cache into memory
+
+    def identify(self, audio_float32):
+        # Compute 256-dim embedding
+        # Cosine similarity against DB
+        # Return (name, confidence) or (None, 0.0)
+
+    def enroll(self, name, audio_float32):
+        # Compute embedding, store in DB
+
+    def list_speakers(self):
+        # Return enrolled names with counts
+
+    def delete_speaker(self, name):
+        # Remove all embeddings for a name
+```
+
+### SQLite Schema
+
+```sql
+CREATE TABLE voices (
+    id INTEGER PRIMARY KEY,
+    name TEXT NOT NULL,
+    embedding BLOB NOT NULL,
+    enrolled_at REAL NOT NULL,
+    source TEXT
+);
+CREATE INDEX IF NOT EXISTS idx_voices_name ON voices(name);
+```
+
+### Matching
+
+- Cosine similarity via dot product (Resemblyzer embeddings are L2-normalized)
+- Threshold: 0.75 for positive match
+- Compare against all stored embeddings, group by name, take best score per name
+
+## headmic.py Changes
+
+### Classifier Thread Update
+
+In `sound_classifier_loop()`, after YAMNet classification:
+
+```python
+if speaker_recognizer and result["category"] == "speech":
+    name, confidence = speaker_recognizer.identify(audio_float32)
+    state.recognized_speaker = name
+    state.speaker_confidence = confidence
+```
+
+### New API Endpoints
+
+| Endpoint | Method | Purpose |
+|----------|--------|---------|
+| `/speakers/enroll` | POST | Multipart: `name` + `audio` file |
+| `/speakers/enroll-from-mic` | POST | Record from live mic (5s, VAD stop) |
+| `/speakers` | GET | List enrolled speakers |
+| `/speakers/{name}` | DELETE | Remove a speaker |
+
+### Updated Endpoints
+
+- `GET /sounds` — adds `recognized_speaker`, `speaker_confidence`
+- `GET /status` — adds `recognized_speaker`
+- `GET /health` — adds `speaker_recognition_enabled`
+
+### Enroll-from-Mic Recording
+
+When `/speakers/enroll-from-mic?name=X` is called:
+1. Set enrollment flag + buffer
+2. Listener loop fills enrollment buffer for 5 seconds (VAD-based stop)
+3. Compute embedding from collected audio
+4. Store in DB
+
+### LED States
+
+| State | Color | Animation |
+|-------|-------|-----------|
+| Wake word | White flash | `wakeup()` |
+| Listening | Cyan (0x00FFFF) | `think()` spin |
+| Processing | Purple (0x9400D3) | `spin()` |
+| **Enrolling** | **Orange (0xFF8C00)** | **`think()` spin** |
+| Idle | Off | `off()` |
+
+## Dependencies
+
+- `resemblyzer` — speaker embeddings (pulls PyTorch)
+- `torch` — required by Resemblyzer (~200MB)
+
+## Graceful Degradation
+
+If Resemblyzer/PyTorch not installed, `speaker_recognizer = None`. All existing functionality unchanged. Speaker endpoints return 503.