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rvc/lib/process/model_fusion.py

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+import torch
+from collections import OrderedDict
+
+
+def extract(ckpt):
+    model = ckpt["model"]
+    opt = OrderedDict()
+    opt["weight"] = {key: value for key, value in model.items() if "enc_q" not in key}
+    return opt
+
+
+def model_fusion(model_name, pth_path_1, pth_path_2):
+    ckpt1 = torch.load(pth_path_1, map_location="cpu")
+    ckpt2 = torch.load(pth_path_2, map_location="cpu")
+    if "model" in ckpt1:
+        ckpt1 = extract(ckpt1)
+    else:
+        ckpt1 = ckpt1["weight"]
+    if "model" in ckpt2:
+        ckpt2 = extract(ckpt2)
+    else:
+        ckpt2 = ckpt2["weight"]
+    if sorted(ckpt1.keys()) != sorted(ckpt2.keys()):
+        return "Fail to merge the models. The model architectures are not the same."
+    opt = OrderedDict(
+        weight={
+            key: 1 * value.float() + (1 - 1) * ckpt2[key].float()
+            for key, value in ckpt1.items()
+        }
+    )
+    opt["info"] = f"Model fusion of {pth_path_1} and {pth_path_2}"
+    torch.save(opt, f"logs/{model_name}.pth")
+    print(f"Model fusion of {pth_path_1} and {pth_path_2} is done.")

rvc/lib/process/model_information.py

@@ -0,0 +1,15 @@
+import torch
+
+def model_information(path):
+    model_data = torch.load(path, map_location="cpu")
+
+    print(f"Loaded model from {path}")
+
+    data = model_data
+
+    epochs = data.get("info", "None")
+    sr = data.get("sr", "None")
+    f0 = data.get("f0", "None")
+    version = data.get("version", "None")
+
+    return(f"Epochs: {epochs}\nSampling rate: {sr}\nPitch guidance: {f0}\nVersion: {version}")