Update app.py

app.py

@@ -28,6 +28,7 @@ speaker_model = EncoderClassifier.from_hparams(
     savedir=os.path.join("/tmp", spk_model_name),
 )
 signal, fs =torchaudio.load('default_ratan_tata_voice.wav')
+print(signal, fs)
 # Ensure to detach and clone before converting to tensor if needed
 speaker_embeddings = speaker_model.encode_batch(signal)  # Directly passing signal as a tensor, no need to wrap in torch.tensor
 speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)  # Normalize the embeddings