Update app.py

app.py

@@ -1,8 +1,8 @@
 import os
 import sys
 # to avoid the modified user.pth file
-cnhubert_base_path = "GPT_SoVITS\pretrained_models\chinese-hubert-base"
-bert_path = "GPT_SoVITS\pretrained_models\chinese-roberta-wwm-ext-large"
+cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
+bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
 os.environ["version"] = 'v2'
 now_dir = os.path.dirname(os.path.abspath(__file__))  # 当前脚本所在目录
 sys.path.insert(0, now_dir)
@@ -44,26 +44,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 #device = "cpu"
 is_half = False
 
-tokenizer = AutoTokenizer.from_pretrained(bert_path)
-bert_model=AutoModelForMaskedLM.from_pretrained(bert_path)
-if(is_half==True):bert_model=bert_model.half().to(device)
-else:bert_model=bert_model.to(device)
-# bert_model=bert_model.to(device)
-def get_bert_feature(text, word2ph): # Bert(不是HuBERT的特征计算)
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors="pt")
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)#####输入是long不用管精度问题，精度随bert_model
-        res = bert_model(**inputs, output_hidden_states=True)
-        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
-    assert len(word2ph) == len(text)
-    phone_level_feature = []
-    for i in range(len(word2ph)):
-        repeat_feature = res[i].repeat(word2ph[i], 1)
-        phone_level_feature.append(repeat_feature)
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    # if(is_half==True):phone_level_feature=phone_level_feature.half()
-    return phone_level_feature.T
+
 
 loaded_sovits_model = [] # [(path, dict, model)]
 loaded_gpt_model = []