Update app.py

app.py

@@ -221,22 +221,22 @@ def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence,
         )
 
 # Process generated tensor
-    generated = generated[:, ref_audio_len:, :]
-    generated_mel_spec = rearrange(generated, "1 n d -> 1 d n")
+        generated = generated[:, ref_audio_len:, :]
+        generated_mel_spec = rearrange(generated, "1 n d -> 1 d n")
 
 # Convert to appropriate dtype and device
-    generated_mel_spec = generated_mel_spec.to(dtype=torch.float16, device=vocos.device)  # Ensure device matches vocos
-    generated_wave = vocos.decode(generated_mel_spec)
+        generated_mel_spec = generated_mel_spec.to(dtype=torch.float16, device=vocos.device)  # Ensure device matches vocos
+        generated_wave = vocos.decode(generated_mel_spec)
 
 # Adjust wave RMS if needed
-    if rms < target_rms:
-    generated_wave = generated_wave * rms / target_rms
+        if rms < target_rms:
+            generated_wave = generated_wave * rms / target_rms
 
 # Convert to numpy
-       generated_wave = generated_wave.squeeze().cpu().numpy()
+        generated_wave = generated_wave.squeeze().cpu().numpy()
 
 # Append to list
-    generated_waves.append(generated_wave)spectrograms.append(generated_mel_spec[0].cpu().numpy())
+        generated_waves.append(generated_wave)spectrograms.append(generated_mel_spec[0].cpu().numpy())
 # Ensure generated_mel_spec is in a compatible dtype (e.g., float32) before passing it to numpy
 #        generated_mel_spec = generated_mel_spec.to(dtype=torch.float32)  # Convert to float32 if it's in bfloat16