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0330-125210-improve_ENG_inference
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import pickle
import os
import re
import wordsegment
from g2p_en import G2p
from string import punctuation
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")
CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
arpa = {
"AH0",
"S",
"AH1",
"EY2",
"AE2",
"EH0",
"OW2",
"UH0",
"NG",
"B",
"G",
"AY0",
"M",
"AA0",
"F",
"AO0",
"ER2",
"UH1",
"IY1",
"AH2",
"DH",
"IY0",
"EY1",
"IH0",
"K",
"N",
"W",
"IY2",
"T",
"AA1",
"ER1",
"EH2",
"OY0",
"UH2",
"UW1",
"Z",
"AW2",
"AW1",
"V",
"UW2",
"AA2",
"ER",
"AW0",
"UW0",
"R",
"OW1",
"EH1",
"ZH",
"AE0",
"IH2",
"IH",
"Y",
"JH",
"P",
"AY1",
"EY0",
"OY2",
"TH",
"HH",
"D",
"ER0",
"CH",
"AO1",
"AE1",
"AO2",
"OY1",
"AY2",
"IH1",
"OW0",
"L",
"SH",
}
def replace_phs(phs):
rep_map = {"'": "-"}
phs_new = []
for ph in phs:
if ph in symbols:
phs_new.append(ph)
elif ph in rep_map.keys():
phs_new.append(rep_map[ph])
else:
print("ph not in symbols: ", ph)
return phs_new
def read_dict():
g2p_dict = {}
start_line = 49
with open(CMU_DICT_PATH) as f:
line = f.readline()
line_index = 1
while line:
if line_index >= start_line:
line = line.strip()
word_split = line.split(" ")
word = word_split[0].lower()
syllable_split = word_split[1].split(" - ")
g2p_dict[word] = []
for syllable in syllable_split:
phone_split = syllable.split(" ")
g2p_dict[word].append(phone_split)
line_index = line_index + 1
line = f.readline()
return g2p_dict
def read_dict_new():
g2p_dict = {}
with open(CMU_DICT_PATH) as f:
line = f.readline()
line_index = 1
while line:
if line_index >= 57:
line = line.strip()
word_split = line.split(" ")
word = word_split[0].lower()
g2p_dict[word] = [word_split[1].split(" ")]
line_index = line_index + 1
line = f.readline()
with open(CMU_DICT_FAST_PATH) as f:
line = f.readline()
line_index = 1
while line:
if line_index >= 0:
line = line.strip()
word_split = line.split(" ")
word = word_split[0].lower()
if word not in g2p_dict:
g2p_dict[word] = [word_split[1:]]
line_index = line_index + 1
line = f.readline()
return g2p_dict
def hot_reload_hot(g2p_dict):
with open(CMU_DICT_HOT_PATH) as f:
line = f.readline()
line_index = 1
while line:
if line_index >= 0:
line = line.strip()
word_split = line.split(" ")
word = word_split[0].lower()
# 自定义发音词直接覆盖字典
g2p_dict[word] = [word_split[1:]]
line_index = line_index + 1
line = f.readline()
return g2p_dict
def cache_dict(g2p_dict, file_path):
with open(file_path, "wb") as pickle_file:
pickle.dump(g2p_dict, pickle_file)
def get_dict():
if os.path.exists(CACHE_PATH):
with open(CACHE_PATH, "rb") as pickle_file:
g2p_dict = pickle.load(pickle_file)
else:
g2p_dict = read_dict_new()
cache_dict(g2p_dict, CACHE_PATH)
g2p_dict = hot_reload_hot(g2p_dict)
return g2p_dict
def get_namedict():
if os.path.exists(NAMECACHE_PATH):
with open(NAMECACHE_PATH, "rb") as pickle_file:
name_dict = pickle.load(pickle_file)
else:
name_dict = {}
return name_dict
def text_normalize(text):
# todo: eng text normalize
# 适配中文及 g2p_en 标点
rep_map = {
"[;::,;]": ",",
'["’]': "'",
"。": ".",
"!": "!",
"?": "?",
}
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
class en_G2p(G2p):
def __init__(self):
super().__init__()
# 分词初始化
wordsegment.load()
# 扩展过时字典, 添加姓名字典
self.cmu = get_dict()
self.namedict = get_namedict()
# 剔除读音错误的几个缩写
for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
del self.cmu[word.lower()]
# 修正多音字
self.homograph2features["read"] = (['R', 'IY1', 'D'], ['R', 'EH1', 'D'], 'VBP')
self.homograph2features["complex"] = (['K', 'AH0', 'M', 'P', 'L', 'EH1', 'K', 'S'], ['K', 'AA1', 'M', 'P', 'L', 'EH0', 'K', 'S'], 'JJ')
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
# pos1比pos长仅出现在read
elif len(pos) < len(pos1) and pos == pos1[:len(pos)]:
pron = pron1
else:
pron = pron2
else:
# 递归查找预测
pron = self.qryword(o_word)
prons.extend(pron)
prons.extend([" "])
return prons[:-1]
def qryword(self, o_word):
word = o_word.lower()
# 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]
# 单词仅首字母大写时查找姓名字典
if o_word.istitle() and word in self.namedict:
return self.namedict[word][0]
# oov 长度小于等于 3 直接读字母
if len(word) <= 3:
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):
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 self.predict(word)
# 可以分词的递归处理
return [phone for comp in comps for phone in self.qryword(comp)]
_g2p = en_G2p()
def g2p(text):
# g2p_en 整段推理,剔除不存在的arpa返回
phone_list = _g2p(text)
phones = [ph if ph != "<unk>" else "UNK" for ph in phone_list if ph not in [" ", "<pad>", "UW", "</s>", "<s>"]]
return replace_phs(phones)
if __name__ == "__main__":
print(g2p("hello"))
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.")))