Refactor mock speaker recognition client and improve testing structure

- Replaced direct import of mock client with a structured import from the new testing module.
- Introduced a dedicated `mock_speaker_client.py` to provide a mock implementation for speaker recognition, facilitating testing without heavy dependencies.
- Added an `__init__.py` file in the testing directory to organize testing utilities and mocks.

Author: AnkushMalaker

backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py

@@ -39,16 +39,8 @@ def __init__(self, service_url: Optional[str] = None):
         # Check if we should use mock client (for testing)
         if os.getenv("USE_MOCK_SPEAKER_CLIENT") == "true":
             try:
-                # Import mock client from tests directory
-                import sys
-                from pathlib import Path
-
-                # Add tests directory to Python path
-                tests_dir = Path(__file__).resolve().parents[5] / "tests"
-                if str(tests_dir) not in sys.path:
-                    sys.path.insert(0, str(tests_dir))
-
-                from mocks.mock_speaker_client import MockSpeakerRecognitionClient
+                # Import mock client from testing module
+                from advanced_omi_backend.testing.mock_speaker_client import MockSpeakerRecognitionClient
 
                 self._mock_client = MockSpeakerRecognitionClient()
                 self.enabled = True

backends/advanced/src/advanced_omi_backend/testing/__init__.py

@@ -0,0 +1 @@
+"""Testing utilities and mocks for Chronicle backend."""

backends/advanced/src/advanced_omi_backend/testing/mock_speaker_client.py

@@ -0,0 +1,160 @@
+"""Mock speaker recognition client for testing without heavy ML dependencies."""
+
+import logging
+from typing import Dict, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class MockSpeakerRecognitionClient:
+    """
+    Mock speaker recognition client that returns pre-computed segments.
+
+    Used in test environments to avoid running resource-intensive speaker
+    recognition service. Segments are based on test_data.py expectations.
+    """
+
+    # Map audio filenames to mock segment data
+    # Segments follow the structure expected by the backend:
+    # {
+    #   "start": float,          # Start time in seconds
+    #   "end": float,            # End time in seconds
+    #   "text": str,             # Transcript text for this segment
+    #   "speaker": int,          # Speaker label (0, 1, 2, etc.)
+    #   "identified_as": str,    # Speaker name or "Unknown"
+    #   "confidence": float      # Optional confidence score
+    # }
+
+    MOCK_SEGMENTS = {
+        "DIY_Experts_Glass_Blowing_16khz_mono_1min.wav": [
+            {
+                "start": 0.0,
+                "end": 10.08,
+                "speaker": 0,
+                "identified_as": "Unknown",
+                "text": "The pumpkin that'll last for forever. Finally. Does it count? Today, we're taking a glass blowing class.",
+                "confidence": 0.95
+            },
+            {
+                "start": 10.28,
+                "end": 20.255,
+                "speaker": 0,
+                "identified_as": "Unknown",
+                "text": "I'm sweating already. We've worked with a lot of materials before, but we've only scratched the surface",
+                "confidence": 0.93
+            },
+            {
+                "start": 20.455,
+                "end": 21.895,
+                "speaker": 1,
+                "identified_as": "Unknown",
+                "text": "when it comes to glass",
+                "confidence": 0.91
+            },
+            {
+                "start": 22.095,
+                "end": 23.615,
+                "speaker": 0,
+                "identified_as": "Unknown",
+                "text": "and that's because",
+                "confidence": 0.94
+            },
+            {
+                "start": 23.815,
+                "end": 28.135,
+                "speaker": 1,
+                "identified_as": "Unknown",
+                "text": "a little intimidating. We've got about 400 pounds",
+                "confidence": 0.92
+            },
+            {
+                "start": 28.335,
+                "end": 43.08,
+                "speaker": 0,
+                "identified_as": "Unknown",
+                "text": "of liquid glass in this furnace right here. Nick's gonna really help us out. Nick, I'm excited and nervous. Me too.",
+                "confidence": 0.96
+            },
+            {
+                "start": 43.28,
+                "end": 44.48,
+                "speaker": 1,
+                "identified_as": "Unknown",
+                "text": "So we're gonna",
+                "confidence": 0.90
+            },
+            {
+                "start": 44.68,
+                "end": 46.76,
+                "speaker": 0,
+                "identified_as": "Unknown",
+                "text": "make what's called a trumpet",
+                "confidence": 0.95
+            },
+            {
+                "start": 46.96,
+                "end": 50.24,
+                "speaker": 0,
+                "identified_as": "Unknown",
+                "text": "flower. We're using gravity as a tool.",
+                "confidence": 0.93
+            }
+        ]
+    }
+
+    def __init__(self):
+        """Initialize mock client."""
+        logger.info("🎤 Mock speaker recognition client initialized")
+
+    async def diarize_identify_match(
+        self,
+        conversation_id: str,
+        backend_token: str,
+        transcript_data: Dict,
+        user_id: Optional[str] = None
+    ) -> Dict:
+        """
+        Return pre-computed mock segments for known test audio files.
+
+        Args:
+            conversation_id: Not used in mock (audio filename derived from transcript)
+            backend_token: Not used in mock
+            transcript_data: Dict with 'text' and 'words' - used to identify audio file
+            user_id: Not used in mock
+
+        Returns:
+            Dictionary with 'segments' array matching speaker service format
+        """
+        logger.info(f"🎤 Mock speaker client processing conversation: {conversation_id[:12]}...")
+
+        # Try to identify which test audio this is from the transcript
+        transcript_text = transcript_data.get("text", "").lower()
+
+        # Match by transcript content
+        if "glass blowing" in transcript_text or "glass" in transcript_text:
+            filename = "DIY_Experts_Glass_Blowing_16khz_mono_1min.wav"
+            if filename in self.MOCK_SEGMENTS:
+                segments = self.MOCK_SEGMENTS[filename]
+                logger.info(f"🎤 Mock returning {len(segments)} segments for DIY Glass Blowing audio")
+                return {"segments": segments}
+
+        # Fallback: Create single generic segment
+        logger.warning(f"🎤 Mock: No pre-computed segments found, creating generic segment")
+
+        # Get duration from words if available
+        words = transcript_data.get("words", [])
+        if words:
+            duration = words[-1].get("end", 60.0)
+        else:
+            duration = 60.0
+
+        return {
+            "segments": [{
+                "start": 0.0,
+                "end": duration,
+                "speaker": 0,
+                "identified_as": "Unknown",
+                "text": transcript_data.get("text", ""),
+                "confidence": 0.85
+            }]
+        }