Update GPT_SoVITS/app_vc.py

GPT_SoVITS/app_vc.py

@@ -54,10 +54,10 @@ def get_pretrain_model_path(env_name, log_file, def_path):
 device = 'cpu'
 
 gpt_path = get_pretrain_model_path('gpt_path', "./gweight.txt", 
-                                   "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
+                                   "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt")
 
 sovits_path = get_pretrain_model_path('sovits_path', "./sweight.txt",
-                                      "GPT_SoVITS/pretrained_models/s2G488k.pth")
+                                      "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth")
 
 cnhubert_base_path = get_pretrain_model_path("cnhubert_base_path", '', "GPT_SoVITS/pretrained_models/chinese-hubert-base")
 
@@ -145,11 +145,17 @@ def change_sovits_weights(sovits_path):
     hps = dict_s2["config"]
     hps = DictToAttrRecursive(hps)
     hps.model.semantic_frame_rate = "25hz"
+    if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+        hps.model.version = "v1"
+    else:
+        hps.model.version = "v2"
+    print("sovits版本:",hps.model.version)
+    model_params_dict = vars(hps.model)
     vq_model = SynthesizerTrn(
         hps.data.filter_length // 2 + 1,
         hps.train.segment_size // hps.data.hop_length,
         n_speakers=hps.data.n_speakers,
-        **hps.model
+        **model_params_dict
     )
     if ("pretrained" not in sovits_path):
         del vq_model.enc_q
@@ -158,9 +164,7 @@ def change_sovits_weights(sovits_path):
     else:
         vq_model = vq_model.to(device)
     vq_model.eval()
-    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
-    with open("./sweight.txt", "w", encoding="utf-8") as f:
-        f.write(sovits_path)
+    vq_model.load_state_dict(dict_s2["weight"], strict=False)
 
 
 change_sovits_weights(sovits_path)
@@ -179,8 +183,7 @@ def change_gpt_weights(gpt_path):
     t2s_model = t2s_model.to(device)
     t2s_model.eval()
     total = sum([param.nelement() for param in t2s_model.parameters()])
-    print("Number of parameter: %.2fM" % (total / 1e6))
-    with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
+    logger.info("Number of parameter: %.2fM" % (total / 1e6))
 
 
 change_gpt_weights(gpt_path)
@@ -293,8 +296,8 @@ def change_choices():
     return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
 
 
-pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
-pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+pretrained_sovits_name = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
+pretrained_gpt_name = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
 SoVITS_weight_root = "SoVITS_weights"
 GPT_weight_root = "GPT_weights"
 os.makedirs(SoVITS_weight_root, exist_ok=True)