Update app.py

app.py

@@ -19,6 +19,13 @@ speaker_model = EncoderClassifier.from_hparams(
     savedir=os.path.join("/tmp", "speechbrain/spkrec-xvect-voxceleb")
 )
 
+def create_speaker_embedding(waveform):
+    with torch.no_grad():
+        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
+        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
+        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
+    return speaker_embeddings
+
 # Load a sample from the dataset for speaker embedding
 try:
     dataset = load_dataset("Yassmen/TTS_English_Technical_data", split="train", trust_remote_code=True)
@@ -30,12 +37,6 @@ except Exception as e:
     # Use a random speaker embedding as fallback
     speaker_embedding = torch.randn(1, 512)
 
-def create_speaker_embedding(waveform):
-    with torch.no_grad():
-        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
-        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
-        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
-    return speaker_embeddings
 
 def text_to_speech(text):
     # Clean up text