修复了一些bug,优化了一些代码

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -1,5 +1,7 @@
 
 import os, sys
+
+from tqdm import tqdm
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 
@@ -12,9 +14,9 @@ from text import cleaned_text_to_sequence
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
 
-# from tools.i18n.i18n import I18nAuto
+from tools.i18n.i18n import I18nAuto
 
-# i18n = I18nAuto()
+i18n = I18nAuto()
 
 def get_first(text:str) -> str:
     pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
@@ -51,9 +53,11 @@ class TextPreprocessor:
         self.device = device
         
     def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]:
+        print(i18n("############ 切分文本 ############"))
         texts = self.pre_seg_text(text, lang, text_split_method)
         result = []
-        for text in texts:
+        print(i18n("############ 提取文本Bert特征 ############"))
+        for text in tqdm(texts):
             phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang)
             res={
                 "phones": phones,
@@ -67,14 +71,16 @@ class TextPreprocessor:
         text = text.strip("\n")
         if (text[0] not in splits and len(get_first(text)) < 4): 
             text = "。" + text if lang != "en" else "." + text
-        # print(i18n("实际输入的目标文本:"), text)
+        print(i18n("实际输入的目标文本:"))
+        print(text)
         
         seg_method = get_seg_method(text_split_method)
         text = seg_method(text)
         
         while "\n\n" in text:
             text = text.replace("\n\n", "\n")
-        # print(i18n("实际输入的目标文本(切句后):"), text)
+        print(i18n("实际输入的目标文本(切句后):"))
+        print(text)
         _texts = text.split("\n")
         _texts = merge_short_text_in_array(_texts, 5)
         texts = []
@@ -105,7 +111,7 @@ class TextPreprocessor:
         textlist=[]
         langlist=[]
         if language in ["auto", "zh", "ja"]:
-            # LangSegment.setfilters(["zh","ja","en","ko"])
+            LangSegment.setfilters(["zh","ja","en","ko"])
             for tmp in LangSegment.getTexts(text):
                 if tmp["lang"] == "ko":
                     langlist.append("zh")
@@ -116,7 +122,7 @@ class TextPreprocessor:
                     langlist.append(language if language!="auto" else tmp["lang"])
                 textlist.append(tmp["text"])
         elif language == "en":
-            # LangSegment.setfilters(["en"])
+            LangSegment.setfilters(["en"])
             formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
             while "  " in formattext:
                 formattext = formattext.replace("  ", " ")
@@ -153,7 +159,7 @@ class TextPreprocessor:
         # phones = sum(phones_list, [])
         norm_text = ''.join(norm_text_list)
 
-        return phones, bert_feature, norm_text
+        return phones_list, bert_feature, norm_text
 
 
     def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor: