Update voice_analysis.py

voice_analysis.py

@@ -22,12 +22,16 @@ def diarize_speakers(audio_path):
     pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token=hf_token)
     diarization = pipeline(audio_path)
     return diarization
-
+    
 def get_speaker_embeddings(audio_path, diarization, model_name="pyannote/embedding"):
     model = Model.from_pretrained(model_name, use_auth_token=os.environ.get("py_annote_hf_token"))
     waveform, sample_rate = torchaudio.load(audio_path)
     duration = waveform.shape[1] / sample_rate
     
+    # Convert stereo to mono if necessary
+    if waveform.shape[0] == 2:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    
     embeddings = []
     for turn, _, speaker in diarization.itertracks(yield_label=True):
         start_frame = int(turn.start * sample_rate)
@@ -35,8 +39,11 @@ def get_speaker_embeddings(audio_path, diarization, model_name="pyannote/embeddi
         segment = waveform[:, start_frame:end_frame]
         
         if segment.shape[1] > 0:
+            # Ensure the segment is on the correct device
+            segment = segment.to(model.device)
+            
             with torch.no_grad():
-                embedding = model(segment.to(model.device))
+                embedding = model(segment)
             embeddings.append({"time": turn.start, "duration": turn.duration, "embedding": embedding.cpu().numpy(), "speaker": speaker})
     
     # Ensure embeddings cover the entire duration