fastspeech2.patch

diff --git a/text_preprocess_for_inference.py b/text_preprocess_for_inference.py
index ccca511..2191ebb 100644
--- a/text_preprocess_for_inference.py
+++ b/text_preprocess_for_inference.py
@@ -3,6 +3,8 @@ TTS Preprocessing
 Developed by Arun Kumar A(CS20S013) - November 2022
 Code Changes by Utkarsh - 2023
 '''
+import locale
+locale.setlocale(locale.LC_ALL, 'C.UTF-8')
 import os
 import re
 import json
@@ -40,14 +42,14 @@ def add_to_dictionary(dict_to_add, dict_file):
             df_temp = pd.read_csv(temp_dict_file, delimiter=" ", header=None, dtype=str)
             if len(df_temp) > len(df_orig):
                 os.rename(temp_dict_file, dict_file)
-                print(f"{len(dict_to_add)} new words appended to Dictionary: {dict_file}")
+                # print(f"{len(dict_to_add)} new words appended to Dictionary: {dict_file}")
         except:
             print(traceback.format_exc())
     else:
         # create a new dictionary
         with open(dict_file, "a") as f:
             f.write(append_string)
-        print(f"New Dictionary: {dict_file} created with {len(dict_to_add)} words")
+        # print(f"New Dictionary: {dict_file} created with {len(dict_to_add)} words")
 
 
 class TextCleaner:
@@ -104,7 +106,7 @@ class Phonifier:
             except Exception as e:
                 print(traceback.format_exc())
 
-        print("Phone dictionary loaded for the following languages:", list(self.phone_dictionary.keys()))
+        # print("Phone dictionary loaded for the following languages:", list(self.phone_dictionary.keys()))
 
         self.g2p = G2p()
         print('Loading G2P model... Done!')
@@ -315,7 +317,7 @@ class Phonifier:
                     #print('INSIDE IF CONDITION OF ADDING WORDS')
         else:
             non_dict_words = words
-        print(f"word not in dict: {non_dict_words}")
+        # print(f"word not in dict: {non_dict_words}")
 
         if len(non_dict_words) > 0:
             # unified parser has to be run for the non dictionary words
@@ -335,7 +337,7 @@ class Phonifier:
                     phn_out_dict[non_dict_words[i]] = self.en_g2p(non_dict_words[i])
                 # Create a string representation of the dictionary
                 data_str = "\n".join([f"{key}\t{value}" for key, value in phn_out_dict.items()])
-                print(f"data_str: {data_str}")
+                # print(f"data_str: {data_str}")
                 with open(out_dict_file, "w") as f:
                     f.write(data_str)
             else:
@@ -358,7 +360,7 @@ class Phonifier:
                     for original_word, formatted_word in zip(non_dict_words, replaced_output_list):
                         line = f"{original_word}\t{formatted_word}\n"
                         file.write(line)
-                        print(line, end='') 
+                        # print(line, end='') 
                   
 
             try:
@@ -415,8 +417,8 @@ class Phonifier:
             non_dict_words = words
 
         if len(non_dict_words) > 0:
-            print(len(non_dict_words))
-            print(non_dict_words)
+            # print(len(non_dict_words))
+            # print(non_dict_words)
             # unified parser has to be run for the non dictionary words
             os.makedirs("tmp", exist_ok=True)
             timestamp = str(time.time())
@@ -434,7 +436,7 @@ class Phonifier:
                     phn_out_dict[non_dict_words[i]] = self.en_g2p(non_dict_words[i])
                 # Create a string representation of the dictionary
                 data_str = "\n".join([f"{key}\t{value}" for key, value in phn_out_dict.items()])
-                print(f"data_str: {data_str}")
+                # print(f"data_str: {data_str}")
                 with open(out_dict_file, "w") as f:
                     f.write(data_str)
             else:
@@ -454,12 +456,12 @@ class Phonifier:
                     for original_word, formatted_word in zip(non_dict_words, replaced_output_list):
                         line = f"{original_word}\t{formatted_word}\n"
                         file.write(line)
-                        print(line, end='') 
+                        # print(line, end='') 
         
             try:
                 df = pd.read_csv(out_dict_file, delimiter="\t", header=None, dtype=str)
                 new_dict = df.dropna().set_index(0).to_dict('dict')[1]
-                print(new_dict)
+                # print(new_dict)
                 if language not in self.phone_dictionary:
                     self.phone_dictionary[language] = new_dict
                 else:
@@ -656,7 +658,7 @@ class TextNormalizer:
                     text = re.sub(str(digit), ' '+num_to_word(digit, self.keydict[language])+' ', text)
             return self.__post_cleaning(text)
         else:
-            print(f"No num-to-char for the given language {language}.")
+            # print(f"No num-to-char for the given language {language}.")
             return self.__post_cleaning(text)
 
     def num2text_list(self, text, language):
@@ -671,7 +673,7 @@ class TextNormalizer:
                 output_text.append(line)
             return self.__post_cleaning_list(output_text)
         else:
-            print(f"No num-to-char for the given language {language}.")
+            # print(f"No num-to-char for the given language {language}.")
             return self.__post_cleaning_list(text)
 
     def normalize(self, text, language):
@@ -758,9 +760,9 @@ class TTSDurAlignPreprocessor:
 
     def preprocess(self, text, language, gender):
         # text = text.strip()
-        print(text)
+        # print(text)
         text = self.text_cleaner.clean(text)
-        print("cleaned text", text)
+        # print("cleaned text", text)
         # text = self.text_normalizer.insert_space(text)
         text = self.text_normalizer.num2text(text, language)
         # print(text)
@@ -769,9 +771,9 @@ class TTSDurAlignPreprocessor:
         phrasified_text = TextPhrasifier.phrasify(text)
         #print("phrased",phrasified_text)
         phonified_text = self.phonifier.phonify(phrasified_text, language, gender)
-        print("phonetext",phonified_text)
+        # print("phonetext",phonified_text)
         phonified_text = self.post_processor.textProcesor(phonified_text)
-        print(phonified_text)
+        # print(phonified_text)
         return phonified_text, phrasified_text
 
 class TTSDurAlignPreprocessor_VTT:
@@ -854,9 +856,9 @@ class TTSPreprocessor:
         text = self.text_normalizer.normalize(text, language)
         phrasified_text = TextPhrasifier.phrasify(text)
         phonified_text = self.phonifier.phonify(phrasified_text, language, gender)
-        print(phonified_text)
+        # print(phonified_text)
         phonified_text = self.post_processor.textProcesorForEnglish(phonified_text)
-        print(phonified_text)
+        # print(phonified_text)
         return phonified_text, phrasified_text
 
 class TTSPreprocessor_VTT: