Update app.py

app.py

@@ -19,8 +19,8 @@ from transformers.pipelines.audio_utils import ffmpeg_read
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
 # set up the diarization pipeline
-#diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.0", use_auth_token=HF_TOKEN)
-diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=HF_TOKEN)
+diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.0", use_auth_token=HF_TOKEN)
+#diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=HF_TOKEN)
 
 if torch.cuda.is_available():
  diarization_pipeline.to(torch.device("cuda"))
@@ -32,7 +32,7 @@ import gradio as gr
 def transcribe(audio_path):
     # Run diarization while we wait for Whisper JAX
     diarization = diarization_pipeline(audio_path)
-    segments = diarization.for_json()["content"]
+    #segments = diarization.for_json()["content"]
     # Segments = diarization.for_json()["content"]
     # Segments = str(diarization)
     transcription = "SAML Output"