Update app_multi.py

app_multi.py

@@ -90,26 +90,25 @@ for model_name in multi_cfg.get('models'):
         open(path.join('model', model_name, 'config.json'), 'r')
     )
 
-    # Load RVC checkpoint
-    cpt = torch.load(
-        path.join('model', model_name, model_info['model']),
-        map_location='cpu'
-    )
-    
-    print(cpt.keys())
-    
-    # If your model checkpoint is not too large, this line can be useful for understanding the structure of your model.
-    # If the output is too large to be useful, you might want to remove or comment it out.
-    for key, value in cpt.items():
-        print(f"{key}: {type(value)}")  # This will print out the type of each item in the cpt dictionary.
-
-    # Check if the 'config' key exists before trying to access it
-    if 'config' in cpt:
-        tgt_sr = cpt['config'][-1]
-        cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0]  # n_spk
-    else:
-        print(f"Warning: Model {model_name} does not have a 'config' key. Skipping this model.")
-        continue
+# Load RVC checkpoint
+cpt = torch.load(
+    path.join('model', model_name, model_info['model']),
+    map_location='cpu'
+)
+
+print(cpt.keys())
+
+# Adding the loop to iterate over keys and values in the checkpoint
+for key, value in cpt.items():
+    print(f"{key}: {type(value)}")  # This will print out the type of each item in the cpt dictionary.
+
+# Check if the 'config' key exists before trying to access it
+if 'config' in cpt:
+    tgt_sr = cpt['config'][-1]
+    cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0]  # n_spk
+else:
+    print(f"Warning: Model {model_name} does not have a 'config' key. Skipping this model.")
+    continue
 
     if_f0 = cpt.get('f0', 1)
     # Check the dimension of the 'enc_p.emb_phone.weight' tensor