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asr_diarization/pipeline.py

@@ -40,7 +40,9 @@ class ASR_Diarization:
             self.embedding_model = None
             print(f"[ERROR] Failed to load ECAPA: {e}")
 
-        self.diar_pipeline = Pipeline.from_pretrained(diar_model, use_auth_token=HF_TOKEN)
+        self.diar_pipeline = Pipeline.from_pretrained(diar_model, use_auth_token=None)
+diar_model="pyannote/speaker-diarization-3.1"
+
         device_index = 0 if torch.cuda.is_available() else -1
         self.asr_pipeline = hf_pipeline(
             "automatic-speech-recognition",

requirements.txt

@@ -1,8 +1,10 @@
-torch
-torchaudio
-pyannote.audio
-transformers
+torch>=2.3.0
+torchaudio>=2.3.0
+pyannote.audio==3.1.1
+transformers>=4.41.0
+huggingface_hub>=0.24.0
 noisereduce
 scikit-learn
 jiwer
 librosa
+speechbrain