Adjust normalize and g2p logic

GPT_SoVITS/text/english.py

@@ -8,6 +8,13 @@
 
 from text import symbols
 
+import unicodedata
+from builtins import str as unicode
+from g2p_en.expand import normalize_numbers
+from nltk.tokenize import TweetTokenizer
+word_tokenize = TweetTokenizer().tokenize
+from nltk import pos_tag
+
 current_file_path = os.path.dirname(__file__)
 CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
 CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
@@ -188,9 +195,6 @@ def get_dict():
  return g2p_dict
 
 
-eng_dict = get_dict()
-
-
 def text_normalize(text):
  # todo: eng text normalize
  # 适配中文及 g2p_en 标点
@@ -204,6 +208,16 @@ def text_normalize(text):
  for p, r in rep_map.items():
  text = re.sub(p, r, text)
 
+ # 来自 g2p_en 文本格式化处理
+ # 增加大写兼容
+ text = unicode(text)
+ text = normalize_numbers(text)
+ text = ''.join(char for char in unicodedata.normalize('NFD', text)
+ if unicodedata.category(char) != 'Mn') # Strip accents
+ text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
+ text = re.sub(r"(?i)i\.e\.", "that is", text)
+ text = re.sub(r"(?i)e\.g\.", "for example", text)
+
  return text
 
 
@@ -220,30 +234,85 @@ def __init__(self):
  for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
  del self.cmu[word.lower()]
 
- # "A" 落单不读 "AH0" 读 "EY1"
- self.cmu['a'] = [['EY1']]
-
 
- def predict(self, word):
- # 小写 oov 长度小于等于 3 直接读字母
+ def __call__(self, text):
+ # tokenization
+ words = word_tokenize(text)
+ tokens = pos_tag(words) # tuples of (word, tag)
+
+ # steps
+ prons = []
+ for o_word, pos in tokens:
+ # 还原 g2p_en 小写操作逻辑
+ word = o_word.lower()
+
+ if re.search("[a-z]", word) is None:
+ pron = [word]
+ # 先把单字母推出去
+ elif len(word) == 1:
+ # 单读 A 发音修正, 这里需要原格式 o_word 判断大写
+ if o_word == "A":
+ pron = ['EY1']
+ else:
+ pron = self.cmu[word][0]
+ # g2p_en 原版多音字处理
+ elif word in self.homograph2features: # Check homograph
+ pron1, pron2, pos1 = self.homograph2features[word]
+ if pos.startswith(pos1):
+ pron = pron1
+ else:
+ pron = pron2
+ else:
+ # 递归查找预测
+ pron = self.qryword(word)
+
+ prons.extend(pron)
+ prons.extend([" "])
+
+ return prons[:-1]
+
+
+ def qryword(self, word):
+ # 查字典, 单字母除外
+ if len(word) > 1 and word in self.cmu: # lookup CMU dict
+ return self.cmu[word][0]
+
+ # oov 长度小于等于 3 直接读字母
  if (len(word) <= 3):
- return [phone for w in word for phone in self(w)]
+ phones = []
+ for w in word:
+ # 单读 A 发音修正, 此处不存在大写的情况
+ if w == "a":
+ phones.extend(['EY1'])
+ else:
+ phones.extend(self.cmu[w][0])
+ return phones
 
  # 尝试分离所有格
  if re.match(r"^([a-z]+)('s)$", word):
- phone = self(word[:-2])
- phone.extend(['Z'])
- return phone
+ phones = self.qryword(word[:-2])
+ # P T K F TH HH 无声辅音结尾 's 发 ['S']
+ if phones[-1] in ['P', 'T', 'K', 'F', 'TH', 'HH']:
+ phones.extend(['S'])
+ # S Z SH ZH CH JH 擦声结尾 's 发 ['IH1', 'Z'] 或 ['AH0', 'Z']
+ elif phones[-1] in ['S', 'Z', 'SH', 'ZH', 'CH', 'JH']:
+ phones.extend(['AH0', 'Z'])
+ # B D G DH V M N NG L R W Y 有声辅音结尾 's 发 ['Z']
+ # AH0 AH1 AH2 EY0 EY1 EY2 AE0 AE1 AE2 EH0 EH1 EH2 OW0 OW1 OW2 UH0 UH1 UH2 IY0 IY1 IY2 AA0 AA1 AA2 AO0 AO1 AO2
+ # ER ER0 ER1 ER2 UW0 UW1 UW2 AY0 AY1 AY2 AW0 AW1 AW2 OY0 OY1 OY2 IH IH0 IH1 IH2 元音结尾 's 发 ['Z']
+ else:
+ phones.extend(['Z'])
+ return phones
 
  # 尝试进行分词，应对复合词
  comps = wordsegment.segment(word.lower())
 
  # 无法分词的送回去预测
  if len(comps)==1:
- return super().predict(word)
+ return self.predict(word)
 
  # 可以分词的递归处理
- return [phone for comp in comps for phone in self(comp)]
+ return [phone for comp in comps for phone in self.qryword(comp)]
 
 
 _g2p = en_G2p()
@@ -258,12 +327,6 @@ def g2p(text):
 
 
 if __name__ == "__main__":
- # print(get_dict())
  print(g2p("hello"))
- print(g2p("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
- # all_phones = set()
- # for k, syllables in eng_dict.items():
- # for group in syllables:
- # for ph in group:
- # all_phones.add(ph)
- # print(all_phones)
+ print(g2p(text_normalize("e.g. I used openai's AI tool to draw a picture.")))
+ print(g2p(text_normalize("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")))