Phase 3: Speaker diarization and full transcription pipeline

- Implement DiarizeService with pyannote.audio speaker detection
- Build PipelineService combining transcribe → diarize → merge with
  overlap-based speaker assignment per segment
- Add pipeline.start and diarize.start IPC handlers
- Add run_pipeline Tauri command for full pipeline execution
- Wire frontend to use pipeline: speakers auto-created with colors,
  segments assigned to detected speakers
- Build SpeakerManager with rename support (double-click or edit button)
- Add speaker color coding throughout transcript display
- Add pyannote.audio dependency
- Tests: 24 Python (including merge logic), 6 Rust, 0 Svelte errors

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

python/voice_to_notes/ipc/handlers.py

@@ -64,6 +64,59 @@ def make_transcribe_handler() -> HandlerFunc:
     return handler
 
 
+def make_diarize_handler() -> HandlerFunc:
+    """Create a diarization handler with a persistent DiarizeService."""
+    from voice_to_notes.services.diarize import DiarizeService, diarization_to_payload
+
+    service = DiarizeService()
+
+    def handler(msg: IPCMessage) -> IPCMessage:
+        payload = msg.payload
+        result = service.diarize(
+            request_id=msg.id,
+            file_path=payload["file"],
+            num_speakers=payload.get("num_speakers"),
+            min_speakers=payload.get("min_speakers"),
+            max_speakers=payload.get("max_speakers"),
+        )
+        return IPCMessage(
+            id=msg.id,
+            type="diarize.result",
+            payload=diarization_to_payload(result),
+        )
+
+    return handler
+
+
+def make_pipeline_handler() -> HandlerFunc:
+    """Create a full pipeline handler (transcribe + diarize + merge)."""
+    from voice_to_notes.services.pipeline import PipelineService, pipeline_result_to_payload
+
+    service = PipelineService()
+
+    def handler(msg: IPCMessage) -> IPCMessage:
+        payload = msg.payload
+        result = service.run(
+            request_id=msg.id,
+            file_path=payload["file"],
+            model_name=payload.get("model", "base"),
+            device=payload.get("device", "cpu"),
+            compute_type=payload.get("compute_type", "int8"),
+            language=payload.get("language"),
+            num_speakers=payload.get("num_speakers"),
+            min_speakers=payload.get("min_speakers"),
+            max_speakers=payload.get("max_speakers"),
+            skip_diarization=payload.get("skip_diarization", False),
+        )
+        return IPCMessage(
+            id=msg.id,
+            type="pipeline.result",
+            payload=pipeline_result_to_payload(result),
+        )
+
+    return handler
+
+
 def hardware_detect_handler(msg: IPCMessage) -> IPCMessage:
     """Detect hardware capabilities and return recommendations."""
     from voice_to_notes.hardware.detect import detect_hardware