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asr_diarization/pipeline.py

@@ -17,6 +17,7 @@ class ASR_Diarization:
                  diar_model="pyannote/speaker-diarization-3.1",
                  asr_model="Capstone04/TrainedWhisper"):
         self.HF_TOKEN = HF_TOKEN
+        self.HF_TOKEN1 = HF_TOKEN1
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
 
         # Load diarization model
@@ -27,7 +28,8 @@ class ASR_Diarization:
             "automatic-speech-recognition",
             model=asr_model,
             device=0 if self.device == "cuda" else -1,
-            return_timestamps=True
+            return_timestamps=True,
+            token=HF_TOKEN1
         )
 
     def run_diarization(self, audio_path):