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asr_diarization/pipeline.py

@@ -4,9 +4,8 @@ import torch
 import torchaudio
 import noisereduce as nr
 from pyannote.audio import Pipeline
-from transformers import pipeline as hf_pipeline
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, pipeline as hf_pipeline
 import tempfile
-
 from pyannote.core import Annotation, Segment
 from pyannote.metrics.diarization import DiarizationErrorRate
 from jiwer import wer, Compose, ToLowerCase, RemovePunctuation, RemoveMultipleSpaces, Strip
@@ -28,7 +27,6 @@ class ASR_Diarization:
             model=asr_model,
             device=0 if self.device == "cuda" else -1,
             return_timestamps=True,
-            use_auth_token = HF_TOKEN,
         )
 
     def run_diarization(self, audio_path):