init ascend tts

ascend_910-gpt-sovits/GPT-SoVITS/GPT_SoVITS/AR/utils/initialize.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+"""Initialize modules for espnet2 neural networks."""
+
+import torch
+from typeguard import check_argument_types
+
+
+def initialize(model: torch.nn.Module, init: str):
+    """Initialize weights of a neural network module.
+
+    Parameters are initialized using the given method or distribution.
+
+    Custom initialization routines can be implemented into submodules
+    as function `espnet_initialization_fn` within the custom module.
+
+    Args:
+        model: Target.
+        init: Method of initialization.
+    """
+    assert check_argument_types()
+    print("init with", init)
+
+    # weight init
+    for p in model.parameters():
+        if p.dim() > 1:
+            if init == "xavier_uniform":
+                torch.nn.init.xavier_uniform_(p.data)
+            elif init == "xavier_normal":
+                torch.nn.init.xavier_normal_(p.data)
+            elif init == "kaiming_uniform":
+                torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
+            elif init == "kaiming_normal":
+                torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
+            else:
+                raise ValueError("Unknown initialization: " + init)
+    # bias init
+    for name, p in model.named_parameters():
+        if ".bias" in name and p.dim() == 1:
+            p.data.zero_()