From 25a2bbd3b9ba1d0bb83fb6d81e9b3a4dbe7be745 Mon Sep 17 00:00:00 2001
From: KamioRinn <snowsdream@live.com>
Date: Sun, 21 Jul 2024 01:40:29 +0800
Subject: [PATCH] Replace deprecated function

---
 GPT_SoVITS/module/attentions.py  |  3 ++-
 GPT_SoVITS/module/models.py      |  3 ++-
 GPT_SoVITS/module/models_onnx.py |  3 ++-
 GPT_SoVITS/module/modules.py     | 15 ++++++++-------
 GPT_SoVITS/module/mrte_model.py  |  3 ++-
 5 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/GPT_SoVITS/module/attentions.py b/GPT_SoVITS/module/attentions.py
index a2e9e515..31ca0f9d 100644
--- a/GPT_SoVITS/module/attentions.py
+++ b/GPT_SoVITS/module/attentions.py
@@ -433,7 +433,8 @@ class FFN(nn.Module):
 
 
 import torch.nn as nn
-from torch.nn.utils import remove_weight_norm, weight_norm
+from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm
 
 
 class Depthwise_Separable_Conv1D(nn.Module):
diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py
index 58a21eee..7586b8c3 100644
--- a/GPT_SoVITS/module/models.py
+++ b/GPT_SoVITS/module/models.py
@@ -9,7 +9,8 @@ from module import modules
 from module import attentions
 
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
 from module.mrte_model import MRTE
 from module.quantize import ResidualVectorQuantizer
diff --git a/GPT_SoVITS/module/models_onnx.py b/GPT_SoVITS/module/models_onnx.py
index 232fd74d..48201121 100644
--- a/GPT_SoVITS/module/models_onnx.py
+++ b/GPT_SoVITS/module/models_onnx.py
@@ -9,7 +9,8 @@ from module import modules
 from module import attentions_onnx as attentions
 
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
 from module.mrte_model import MRTE
 from module.quantize import ResidualVectorQuantizer
diff --git a/GPT_SoVITS/module/modules.py b/GPT_SoVITS/module/modules.py
index f4447455..91b28632 100644
--- a/GPT_SoVITS/module/modules.py
+++ b/GPT_SoVITS/module/modules.py
@@ -5,7 +5,8 @@ from torch import nn
 from torch.nn import functional as F
 
 from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
+from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
 
 from module import commons
 from module.commons import init_weights, get_padding
@@ -159,7 +160,7 @@ class WN(torch.nn.Module):
             cond_layer = torch.nn.Conv1d(
                 gin_channels, 2 * hidden_channels * n_layers, 1
             )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+            self.cond_layer = weight_norm(cond_layer, name="weight")
 
         for i in range(n_layers):
             dilation = dilation_rate**i
@@ -171,7 +172,7 @@ class WN(torch.nn.Module):
                 dilation=dilation,
                 padding=padding,
             )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            in_layer = weight_norm(in_layer, name="weight")
             self.in_layers.append(in_layer)
 
             # last one is not necessary
@@ -181,7 +182,7 @@ class WN(torch.nn.Module):
                 res_skip_channels = hidden_channels
 
             res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            res_skip_layer = weight_norm(res_skip_layer, name="weight")
             self.res_skip_layers.append(res_skip_layer)
 
     def forward(self, x, x_mask, g=None, **kwargs):
@@ -213,11 +214,11 @@ class WN(torch.nn.Module):
 
     def remove_weight_norm(self):
         if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
+            remove_weight_norm(self.cond_layer)
         for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
+            remove_weight_norm(l)
         for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
+            remove_weight_norm(l)
 
 
 class ResBlock1(torch.nn.Module):
diff --git a/GPT_SoVITS/module/mrte_model.py b/GPT_SoVITS/module/mrte_model.py
index b0cd242c..14aeb46a 100644
--- a/GPT_SoVITS/module/mrte_model.py
+++ b/GPT_SoVITS/module/mrte_model.py
@@ -2,7 +2,8 @@
 
 import torch
 from torch import nn
-from torch.nn.utils import remove_weight_norm, weight_norm
+from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
 from module.attentions import MultiHeadAttention