Update app.py

app.py

@@ -173,9 +173,13 @@ def generate_audio(script_output, voice1, voice2, num_hosts, temperature, top_p,
             if len(code_list) < min_length:
                 code_list = code_list + [0] * (min_length - len(code_list))
             
-            # Use the correct method for audio generation based on your SNAC model
-            codes_tensor = torch.tensor(code_list).unsqueeze(0).to(device)
-            paragraph_audio = snac_model(codes_tensor)
+            # Convert to float tensor to match bias type
+            codes_tensor = torch.tensor(code_list, dtype=torch.float32).unsqueeze(0).to(device)
+            
+            # Generate audio
+            with torch.no_grad():
+                paragraph_audio = snac_model(codes_tensor)
+            
             paragraph_audio = paragraph_audio.cpu().numpy().flatten()
             
             silences = detect_silence(paragraph_audio)