initial upload attempt

initial upload attempt

system/tts_engines/rvc/train/process/model_blender.py

@@ -0,0 +1,63 @@
+import os
+import torch
+from collections import OrderedDict
+
+
+def extract(ckpt):
+    a = ckpt["model"]
+    opt = OrderedDict()
+    opt["weight"] = {}
+    for key in a.keys():
+        if "enc_q" in key:
+            continue
+        opt["weight"][key] = a[key]
+    return opt
+
+
+def model_blender(name, path1, path2, ratio):
+    try:
+        message = f"Model {path1} and {path2} are merged with alpha {ratio}."
+        ckpt1 = torch.load(path1, map_location="cpu")
+        ckpt2 = torch.load(path2, map_location="cpu")
+        cfg = ckpt1["config"]
+        cfg_f0 = ckpt1["f0"]
+        cfg_version = ckpt1["version"]
+
+        if "model" in ckpt1:
+            ckpt1 = extract(ckpt1)
+        else:
+            ckpt1 = ckpt1["weight"]
+        if "model" in ckpt2:
+            ckpt2 = extract(ckpt2)
+        else:
+            ckpt2 = ckpt2["weight"]
+
+        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
+            return "Fail to merge the models. The model architectures are not the same."
+
+        opt = OrderedDict()
+        opt["weight"] = {}
+        for key in ckpt1.keys():
+            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
+                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
+                opt["weight"][key] = (
+                    ratio * (ckpt1[key][:min_shape0].float())
+                    + (1 - ratio) * (ckpt2[key][:min_shape0].float())
+                ).half()
+            else:
+                opt["weight"][key] = (
+                    ratio * (ckpt1[key].float()) + (1 - ratio) * (ckpt2[key].float())
+                ).half()
+
+        opt["config"] = cfg
+        opt["sr"] = message
+        opt["f0"] = cfg_f0
+        opt["version"] = cfg_version
+        opt["info"] = message
+
+        torch.save(opt, os.path.join("logs", "%s.pth" % name))
+        print(message)
+        return message, os.path.join("logs", "%s.pth" % name)
+    except Exception as error:
+        print(error)
+        return error