Fix package pathing issues

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -445,8 +445,9 @@ class TTS:
     def _init_models(
         self,
     ):
-        self.init_t2s_weights(self.configs.t2s_weights_path)
+        # Don't auto load the weights, load them independently not on instantiation
-        self.init_vits_weights(self.configs.vits_weights_path)
+        # self.init_t2s_weights(self.configs.t2s_weights_path)
+        # self.init_vits_weights(self.configs.vits_weights_path)
         self.init_bert_weights(self.configs.bert_base_path)
         self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path)
         # self.enable_half_precision(self.configs.is_half)
@@ -468,10 +469,11 @@ class TTS:
         if self.configs.is_half and str(self.configs.device) != "cpu":
             self.bert_model = self.bert_model.half()
 
-    def init_vits_weights(self, weights_path: str):
+    def init_vits_weights(self, weights_path: str, vocoder_path: str = None):
         self.configs.vits_weights_path = weights_path
         version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(weights_path)
         path_sovits = self.configs.default_configs[model_version]["vits_weights_path"]
+        print(if_lora_v3)
 
         if if_lora_v3 == True and os.path.exists(path_sovits) == False:
             info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重"%model_version)
@@ -502,9 +504,10 @@ class TTS:
 
         self.configs.update_version(model_version)
 
-        # print(f"model_version:{model_version}")
+        print(f"model_version:{model_version}")
         # print(f'hps["model"]["version"]:{hps["model"]["version"]}')
         if model_version not in {"v3", "v4"}:
+            print("Using v2 model")
             vits_model = SynthesizerTrn(
                 self.configs.filter_length // 2 + 1,
                 self.configs.segment_size // self.configs.hop_length,
@@ -513,6 +516,7 @@ class TTS:
             )
             self.configs.use_vocoder = False
         else:
+            print("Using v3 model")
             kwargs["version"]=model_version
             vits_model = SynthesizerTrnV3(
                 self.configs.filter_length // 2 + 1,
@@ -520,8 +524,9 @@ class TTS:
                 n_speakers=self.configs.n_speakers,
                 **kwargs,
             )
+            
             self.configs.use_vocoder = True
-            self.init_vocoder(model_version)
+            self.init_vocoder(model_version, vocoder_path)
             if "pretrained" not in weights_path and hasattr(vits_model, "enc_q"):
                 del vits_model.enc_q
 
@@ -570,7 +575,7 @@ class TTS:
         if self.configs.is_half and str(self.configs.device) != "cpu":
             self.t2s_model = self.t2s_model.half()
 
-    def init_vocoder(self, version: str):
+    def init_vocoder(self, version: str, vocoder_path: str = None):
         if version == "v3":
             if self.vocoder is not None and self.vocoder.__class__.__name__ == "BigVGAN":
                 return
@@ -578,7 +583,7 @@ class TTS:
                 self.vocoder.cpu()
                 del self.vocoder
                 self.empty_cache()
-                
+            
             self.vocoder = BigVGAN.from_pretrained(
                 "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
                 use_cuda_kernel=False,
@@ -611,8 +616,13 @@ class TTS:
                         gin_channels=0, is_bias=True
                     )
             self.vocoder.remove_weight_norm()
-            state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu")
+            print("loading vocoder",vocoder_path)
-            print("loading vocoder",self.vocoder.load_state_dict(state_dict_g))
+            try:
+                state_dict_g = torch.load(vocoder_path, map_location="cpu")
+                self.vocoder.load_state_dict(state_dict_g)
+            except:
+                state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu")
+                print("loading vocoder",self.vocoder.load_state_dict(state_dict_g))
 
             self.vocoder_configs["sr"] = 48000
             self.vocoder_configs["T_ref"] = 500
@@ -988,7 +998,7 @@ class TTS:
         seed = inputs.get("seed", -1)
         seed = -1 if seed in ["", None] else seed
         actual_seed = set_seed(seed)
-        parallel_infer = inputs.get("parallel_infer", True)
+        parallel_infer = inputs.get("parallel_infer", False)
         repetition_penalty = inputs.get("repetition_penalty", 1.35)
         sample_steps = inputs.get("sample_steps", 32)
         super_sampling = inputs.get("super_sampling", False)

GPT_SoVITS/f5_tts/model/backbones/dit.py

@@ -25,7 +25,7 @@ from GPT_SoVITS.f5_tts.model.modules import (
     get_pos_embed_indices,
 )
 
-from module.commons import sequence_mask
+from GPT_SoVITS.module.commons import sequence_mask
 
 
 class TextEmbedding(nn.Module):

GPT_SoVITS/f5_tts/model/backbones/mmdit.py

@@ -14,7 +14,7 @@ from torch import nn
 
 from x_transformers.x_transformers import RotaryEmbedding
 
-from f5_tts.model.modules import (
+from GPT_SoVITS.f5_tts.model.modules import (
     TimestepEmbedding,
     ConvPositionEmbedding,
     MMDiTBlock,

GPT_SoVITS/f5_tts/model/backbones/unett.py

@@ -17,7 +17,7 @@ import torch.nn.functional as F
 from x_transformers import RMSNorm
 from x_transformers.x_transformers import RotaryEmbedding
 
-from f5_tts.model.modules import (
+from GPT_SoVITS.f5_tts.model.modules import (
     TimestepEmbedding,
     ConvNeXtV2Block,
     ConvPositionEmbedding,

GPT_SoVITS/module/models.py

@@ -10,7 +10,7 @@ from torch.nn import functional as F
 from GPT_SoVITS.module import commons
 from GPT_SoVITS.module import modules
 from GPT_SoVITS.module import attentions
-from f5_tts.model import DiT
+from GPT_SoVITS.f5_tts.model import DiT
 from torch.nn import Conv1d, ConvTranspose1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from GPT_SoVITS.module.commons import init_weights, get_padding

GPT_SoVITS/text/english.py

@@ -9,7 +9,7 @@ from GPT_SoVITS.text.symbols import punctuation
 from GPT_SoVITS.text.symbols2 import symbols
 
 from builtins import str as unicode
-from text.en_normalization.expend import normalize
+from GPT_SoVITS.text.en_normalization.expend import normalize
 from nltk.tokenize import TweetTokenizer
 
 word_tokenize = TweetTokenizer().tokenize