Update app.py

app.py

@@ -20,16 +20,16 @@ speaker_model = EncoderClassifier.from_hparams(
     savedir=os.path.join("/tmp", "speechbrain/spkrec-xvect-voxceleb")
 )
 
-# Load a sample from the dataset for speaker embedding
-try:
-    dataset = load_dataset("mozilla-foundation/common_voice_17_0", "hi", split="validated", trust_remote_code=True)
-    dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
-    sample = dataset[0]
-    speaker_embedding = create_speaker_embedding(sample['audio']['array'])
-except Exception as e:
-    print(f"Error loading dataset: {e}")
-    # Use a random speaker embedding as fallback
-    speaker_embedding = torch.randn(1, 512)
+# # Load a sample from the dataset for speaker embedding
+# try:
+#     dataset = load_dataset("mozilla-foundation/common_voice_17_0", "hi", split="validated", trust_remote_code=True)
+#     dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
+#     sample = dataset[0]
+#     speaker_embedding = create_speaker_embedding(sample['audio']['array'])
+# except Exception as e:
+#     print(f"Error loading dataset: {e}")
+#     # Use a random speaker embedding as fallback
+#     speaker_embedding = torch.randn(1, 512)
 
 def create_speaker_embedding(waveform):
     with torch.no_grad():