.

GPT_SoVITS/BigVGAN/bigvgan.py

@@ -14,7 +14,7 @@ import torch.nn as nn
 from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
 from torch.nn import Conv1d, ConvTranspose1d
 from torch.nn.utils import remove_weight_norm
-from torch.nn.utils.parametrizations weight_norm
+from torch.nn.utils import weight_norm
 
 from . import activations
 from .alias_free_activation.torch.act import Activation1d as TorchActivation1d

GPT_SoVITS/BigVGAN/utils0.py

@@ -5,7 +5,7 @@ import glob
 import os
 
 import torch
-from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils import weight_norm
 
 
 def init_weights(m, mean=0.0, std=0.01):

GPT_SoVITS/inference_webui.py

@@ -345,11 +345,10 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
             n_speakers=hps.data.n_speakers,
             **hps.model,
         ).eval()
+
     if "pretrained" not in sovits_path:
-        try:
+        if hasattr(vq_model, "enc_q"):
             del vq_model.enc_q
-        finally:
-            pass
 
     if is_lora is False:
         console.print(f">> loading sovits_{model_version}", vq_model.load_state_dict(dict_s2["weight"], strict=False))
@@ -358,7 +357,8 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
         console.print(f">> loading sovits_{model_version}spretrained_G")
         dict_pretrain = torch.load(path_sovits)["weight"]
         console.print(f">> loading sovits_{model_version}_lora{model_version}")
-        state_dict = dict_pretrain.update(dict_s2["weight"])
+        dict_pretrain.update(dict_s2["weight"])
+        state_dict = dict_pretrain
         lora_rank = dict_s2["lora_rank"]
         lora_config = LoraConfig(
             target_modules=["to_k", "to_q", "to_v", "to_out.0"],

GPT_SoVITS/module/models.py

@@ -8,7 +8,7 @@ from torch.cuda.amp import autocast
 from torch.nn import Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
 from torch.nn.utils import remove_weight_norm, spectral_norm
-from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils import weight_norm
 
 from GPT_SoVITS.f5_tts.model import DiT
 from GPT_SoVITS.text import symbols as symbols_v1

GPT_SoVITS/module/models_onnx.py

@@ -6,7 +6,7 @@ from torch import nn
 from torch.nn import Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
 from torch.nn.utils import remove_weight_norm, spectral_norm
-from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils import weight_norm
 
 from GPT_SoVITS.f5_tts.model import DiT
 from GPT_SoVITS.text import symbols as symbols_v1

GPT_SoVITS/module/modules.py

@@ -7,7 +7,7 @@ from torch import nn
 from torch.nn import Conv1d
 from torch.nn import functional as F
 from torch.nn.utils import remove_weight_norm
-from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils import weight_norm
 
 from . import commons
 from .commons import get_padding, init_weights

GPT_SoVITS/module/mrte_model.py

@@ -3,7 +3,7 @@
 import torch
 from torch import nn
 from torch.nn.utils import remove_weight_norm
-from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils import weight_norm
 
 from .attentions import MultiHeadAttention
 

GPT_SoVITS/process_ckpt.py

@@ -30,7 +30,7 @@ def save_ckpt(ckpt, name, epoch, steps, hps, lora_rank=None):
             if "enc_q" in key:
                 continue
             opt["weight"][key] = ckpt[key].half()
-        opt["config"] = hps.__dict__
+        opt["config"] = hps.to_dict()
         opt["info"] = f"{epoch}epoch_{steps}iteration"
         if lora_rank:
             opt["lora_rank"] = lora_rank
@@ -51,8 +51,8 @@ def inspect_version(
     dict_s2 = torch.load(f, map_location="cpu", mmap=True)
     hps = dict_s2["config"]
     version: str | None = None
-    if "version" in hps:
-        version = hps.version
+    if "version" in hps.keys():
+        version = hps["version"]
     is_lora = "lora_rank" in dict_s2.keys()
 
     if version is not None:

GPT_SoVITS/s2_train.py

@@ -93,7 +93,7 @@ def run(rank, n_gpus, hps):
             diagnose=True,
             format="{time:YY-MM-DD HH:mm:ss}\t{name}\t{level}\t{message}",
         )
-        console.print(hps.__dict__)
+        console.print(hps.to_dict())
         writer: SummaryWriter | None = SummaryWriter(log_dir=hps.s2_ckpt_dir)
         writer_eval: SummaryWriter | None = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
     else:

GPT_SoVITS/s2_train_v3.py

@@ -88,7 +88,7 @@ def run(rank, n_gpus, hps):
             diagnose=True,
             format="{time:YY-MM-DD HH:mm:ss}\t{name}\t{level}\t{message}",
         )
-        console.print(hps.__dict__)
+        console.print(hps.to_dict())
         writer: SummaryWriter | None = SummaryWriter(log_dir=hps.s2_ckpt_dir)
         writer_eval: SummaryWriter | None = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
     else:

GPT_SoVITS/s2_train_v3_lora.py

@@ -97,7 +97,7 @@ def run(rank, n_gpus, hps):
             diagnose=True,
             format="{time:YY-MM-DD HH:mm:ss}\t{name}\t{level}\t{message}",
         )
-        console.print(hps.__dict__)
+        console.print(hps.to_dict())
         writer: SummaryWriter | None = SummaryWriter(log_dir=hps.s2_ckpt_dir)
         writer_eval: SummaryWriter | None = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
     else:

GPT_SoVITS/utils.py

@@ -360,3 +360,13 @@ class HParams:
 
     def __repr__(self):
         return self.__dict__.__repr__()
+
+    def to_dict(self):
+        """Convert HParams to a plain dictionary recursively"""
+        result = {}
+        for k, v in self.__dict__.items():
+            if isinstance(v, HParams):
+                result[k] = v.to_dict()
+            else:
+                result[k] = v
+        return result

tools/AP_BWE/models/model.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn.utils import spectral_norm
-from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils import weight_norm
 
 
 # from utils import init_weights, get_padding