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asr_diarization/inference.py

@@ -2,8 +2,7 @@ import os
 from .pipeline import ASR_Diarization
 
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
-HF_TOKEN1 = os.environ.get("HF_TOKEN1", None)
-pipe = ASR_Diarization(HF_TOKEN, HF_TOKEN1)
+pipe = ASR_Diarization(HF_TOKEN)
 
 def inference(inputs):
     return pipe(inputs)

asr_diarization/pipeline.py

@@ -13,11 +13,10 @@ from jiwer import wer, Compose, ToLowerCase, RemovePunctuation, RemoveMultipleSp
 
 
 class ASR_Diarization:
-    def __init__(self, HF_TOKEN, HF_TOKEN1,
+    def __init__(self, HF_TOKEN,
                  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
@@ -29,7 +28,6 @@ class ASR_Diarization:
             model=asr_model,
             device=0 if self.device == "cuda" else -1,
             return_timestamps=True,
-            token=HF_TOKEN1
         )
 
     def run_diarization(self, audio_path):