added model c hange

video_transcription/ai_transcriber_v2/diarizer.py

@@ -6,31 +6,28 @@
 
 import os
 import sys
+import tracker
+import torch
 
 def diarize_audio(audio_path, num_speakers=None, hf_token=None):
     """
     Performs speaker diarization using pyannote.audio.
-    
-    Args:
-        audio_path (str): Path to audio file.
-        num_speakers (int, optional): Number of speakers if known.
-        hf_token (str): HuggingFace Auth Token.
-        
-    Returns:
-        list: List of segments with speaker labels [(start, end, speaker), ...].
     """
     try:
         from pyannote.audio import Pipeline
     except ImportError:
-        print("Error: pyannote.audio not installed. Diarization skipped.")
+        tracker.logger.error("Error: pyannote.audio not installed. Diarization skipped.")
         return None
 
     if not hf_token:
-        print("Error: HuggingFace Token (HF_TOKEN) not found. Diarization skipped.")
+        tracker.logger.error("Error: HuggingFace Token (HF_TOKEN) not found. Diarization skipped.")
         return None
 
-    print(f"Loading Diarization Pipeline (pyannote/speaker-diarization-3.1)...")
+    tracker.logger.info(f"Loading Diarization Pipeline (pyannote/speaker-diarization-3.1)...")
     try:
+        # Fix for PyTorch 2.6+ weights_only issue
+        torch.serialization.add_safe_globals([torch.torch_version.TorchVersion])
+        
         # Note: 'use_auth_token' was deprecated in favor of 'token' in recent versions
         pipeline = Pipeline.from_pretrained(
             "pyannote/speaker-diarization-3.1",
@@ -38,11 +35,10 @@ def diarize_audio(audio_path, num_speakers=None, hf_token=None):
         )
         
         # Move to GPU if available
-        import torch
         if torch.cuda.is_available():
             pipeline.to(torch.device("cuda"))
         
-        print(f"Diarizing {audio_path}...")
+        tracker.logger.info(f"Diarizing {audio_path}...")
         diarization = pipeline(audio_path, num_speakers=num_speakers)
         
         results = []
@@ -56,7 +52,7 @@ def diarize_audio(audio_path, num_speakers=None, hf_token=None):
         return results
 
     except Exception as e:
-        print(f"Error during diarization: {e}")
+        tracker.logger.error(f"Error during diarization: {e}")
         return None
 
 def merge_diarization_with_transcript(transcript_segments, diarization_segments):