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asr_diarization/pipeline.py

@@ -5,7 +5,6 @@ import torchaudio
 import noisereduce as nr
 from pyannote.audio import Pipeline
 from transformers import pipeline as hf_pipeline
-from transformers import WhisperForConditionalGeneration, WhisperProcessor
 import tempfile
 
 from pyannote.core import Annotation, Segment
@@ -26,7 +25,7 @@ class ASR_Diarization:
         # Load ASR model with timestamps
         self.asr_pipeline = hf_pipeline(
             "automatic-speech-recognition",
-            model=WhisperForConditionalGeneration.from_pretrained(asr_model),
+            model=asr_model,
             device=0 if self.device == "cuda" else -1,
             return_timestamps=True
         )