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():