Merge pull request #964 from IAHispano/formatter/main

chore(format): run black on main

Author: blaisewf

rvc/lib/predictors/FCPE.py

@@ -728,7 +728,9 @@ def __init__(self, model_path, device=None, dtype=torch.float32):
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
         self.device = device
-        ckpt = torch.load(model_path, map_location=torch.device(self.device), weights_only=True)
+        ckpt = torch.load(
+            model_path, map_location=torch.device(self.device), weights_only=True
+        )
         self.args = DotDict(ckpt["config"])
         self.dtype = dtype
         model = FCPE(

rvc/train/train.py

@@ -469,23 +469,31 @@ def run(
                 print(f"Loaded pretrained (G) '{pretrainG}'")
             if hasattr(net_g, "module"):
                 net_g.module.load_state_dict(
-                    torch.load(pretrainG, map_location="cpu", weights_only=True)["model"]
+                    torch.load(pretrainG, map_location="cpu", weights_only=True)[
+                        "model"
+                    ]
                 )
             else:
                 net_g.load_state_dict(
-                    torch.load(pretrainG, map_location="cpu", weights_only=True)["model"]
+                    torch.load(pretrainG, map_location="cpu", weights_only=True)[
+                        "model"
+                    ]
                 )
 
         if pretrainD != "" and pretrainD != "None":
             if rank == 0:
                 print(f"Loaded pretrained (D) '{pretrainD}'")
             if hasattr(net_d, "module"):
                 net_d.module.load_state_dict(
-                    torch.load(pretrainD, map_location="cpu", weights_only=True)["model"]
+                    torch.load(pretrainD, map_location="cpu", weights_only=True)[
+                        "model"
+                    ]
                 )
             else:
                 net_d.load_state_dict(
-                    torch.load(pretrainD, map_location="cpu", weights_only=True)["model"]
+                    torch.load(pretrainD, map_location="cpu", weights_only=True)[
+                        "model"
+                    ]
                 )
 
     # Initialize schedulers