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This commit is contained in:
Stardust·减
2023-10-28 10:36:21 +08:00
committed by GitHub
parent c1ba4c7b3a
commit 82ae8f6928
102 changed files with 175574 additions and 1062 deletions

View File

@@ -0,0 +1,28 @@
from .symbols import *
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
def cleaned_text_to_sequence(cleaned_text, tones, language):
"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
Args:
text: string to convert to a sequence
Returns:
List of integers corresponding to the symbols in the text
"""
phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
tone_start = language_tone_start_map[language]
tones = [i + tone_start for i in tones]
lang_id = language_id_map[language]
lang_ids = [lang_id for i in phones]
return phones, tones, lang_ids
def get_bert(norm_text, word2ph, language):
from .chinese_bert import get_bert_feature as zh_bert
from .english_bert_mock import get_bert_feature as en_bert
lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert}
bert = lang_bert_func_map[language](norm_text, word2ph)
return bert

View File

@@ -0,0 +1,199 @@
import os
import re
import cn2an
from pypinyin import lazy_pinyin, Style
from .symbols import punctuation
from .tone_sandhi import ToneSandhi
current_file_path = os.path.dirname(__file__)
pinyin_to_symbol_map = {
line.split("\t")[0]: line.strip().split("\t")[1]
for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
}
import jieba.posseg as psg
rep_map = {
"": ",",
"": ",",
"": ",",
"": ".",
"": "!",
"": "?",
"\n": ".",
"·": ",",
"": ",",
"...": "",
"$": ".",
"": "'",
"": "'",
"": "'",
"": "'",
"": "'",
"": "'",
"(": "'",
")": "'",
"": "'",
"": "'",
"": "'",
"": "'",
"[": "'",
"]": "'",
"": "-",
"": "-",
"~": "-",
"": "'",
"": "'",
}
tone_modifier = ToneSandhi()
def replace_punctuation(text):
text = text.replace("", "").replace("", "")
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
replaced_text = re.sub(
r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
)
return replaced_text
def g2p(text):
pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
phones, tones, word2ph = _g2p(sentences)
assert sum(word2ph) == len(phones)
assert len(word2ph) == len(text) # Sometimes it will crash,you can add a try-catch.
phones = ["_"] + phones + ["_"]
tones = [0] + tones + [0]
word2ph = [1] + word2ph + [1]
return phones, tones, word2ph
def _get_initials_finals(word):
initials = []
finals = []
orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
orig_finals = lazy_pinyin(
word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
)
for c, v in zip(orig_initials, orig_finals):
initials.append(c)
finals.append(v)
return initials, finals
def _g2p(segments):
phones_list = []
tones_list = []
word2ph = []
for seg in segments:
# Replace all English words in the sentence
seg = re.sub("[a-zA-Z]+", "", seg)
seg_cut = psg.lcut(seg)
initials = []
finals = []
seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
for word, pos in seg_cut:
if pos == "eng":
continue
sub_initials, sub_finals = _get_initials_finals(word)
sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
initials.append(sub_initials)
finals.append(sub_finals)
# assert len(sub_initials) == len(sub_finals) == len(word)
initials = sum(initials, [])
finals = sum(finals, [])
#
for c, v in zip(initials, finals):
raw_pinyin = c + v
# NOTE: post process for pypinyin outputs
# we discriminate i, ii and iii
if c == v:
assert c in punctuation
phone = [c]
tone = "0"
word2ph.append(1)
else:
v_without_tone = v[:-1]
tone = v[-1]
pinyin = c + v_without_tone
assert tone in "12345"
if c:
# 多音节
v_rep_map = {
"uei": "ui",
"iou": "iu",
"uen": "un",
}
if v_without_tone in v_rep_map.keys():
pinyin = c + v_rep_map[v_without_tone]
else:
# 单音节
pinyin_rep_map = {
"ing": "ying",
"i": "yi",
"in": "yin",
"u": "wu",
}
if pinyin in pinyin_rep_map.keys():
pinyin = pinyin_rep_map[pinyin]
else:
single_rep_map = {
"v": "yu",
"e": "e",
"i": "y",
"u": "w",
}
if pinyin[0] in single_rep_map.keys():
pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
phone = pinyin_to_symbol_map[pinyin].split(" ")
word2ph.append(len(phone))
phones_list += phone
tones_list += [int(tone)] * len(phone)
return phones_list, tones_list, word2ph
def text_normalize(text):
numbers = re.findall(r"\d+(?:\.?\d+)?", text)
for number in numbers:
text = text.replace(number, cn2an.an2cn(number), 1)
text = replace_punctuation(text)
return text
def get_bert_feature(text, word2ph):
from text import chinese_bert
return chinese_bert.get_bert_feature(text, word2ph)
if __name__ == "__main__":
from text.chinese_bert import get_bert_feature
text = "啊!但是《原神》是由,米哈\游自主, [研发]的一款全.新开放世界.冒险游戏"
text = text_normalize(text)
print(text)
phones, tones, word2ph = g2p(text)
bert = get_bert_feature(text, word2ph)
print(phones, tones, word2ph, bert.shape)
# # 示例用法
# text = "这是一个示例文本:,你好!这是一个测试...."
# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试

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import torch
import sys
from transformers import AutoTokenizer, AutoModelForMaskedLM
device = torch.device(
"cuda"
if torch.cuda.is_available()
else (
"mps"
if sys.platform == "darwin" and torch.backends.mps.is_available()
else "cpu"
)
)
tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(
device
)
def get_bert_feature(text, word2ph):
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device)
res = model(**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
assert len(word2ph) == len(text) + 2
word2phone = word2ph
phone_level_feature = []
for i in range(len(word2phone)):
repeat_feature = res[i].repeat(word2phone[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T
if __name__ == "__main__":
# feature = get_bert_feature('你好,我是说的道理。')
import torch
word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征
word2phone = [
1,
2,
1,
2,
2,
1,
2,
2,
1,
2,
2,
1,
2,
2,
2,
2,
2,
1,
1,
2,
2,
1,
2,
2,
2,
2,
1,
2,
2,
2,
2,
2,
1,
2,
2,
2,
2,
1,
]
# 计算总帧数
total_frames = sum(word2phone)
print(word_level_feature.shape)
print(word2phone)
phone_level_feature = []
for i in range(len(word2phone)):
print(word_level_feature[i].shape)
# 对每个词重复word2phone[i]次
repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
print(phone_level_feature.shape) # torch.Size([36, 1024])

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from . import chinese, cleaned_text_to_sequence
language_module_map = {"ZH": chinese}
def clean_text(text, language):
language_module = language_module_map[language]
norm_text = language_module.text_normalize(text)
phones, tones, word2ph = language_module.g2p(norm_text)
return norm_text, phones, tones, word2ph
def clean_text_bert(text, language):
language_module = language_module_map[language]
norm_text = language_module.text_normalize(text)
phones, tones, word2ph = language_module.g2p(norm_text)
bert = language_module.get_bert_feature(norm_text, word2ph)
return phones, tones, bert
def text_to_sequence(text, language):
norm_text, phones, tones, word2ph = clean_text(text, language)
return cleaned_text_to_sequence(phones, tones, language)
if __name__ == "__main__":
pass

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import pickle
import os
import re
from g2p_en import G2p
from text import symbols
current_file_path = os.path.dirname(__file__)
CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
_g2p = G2p()
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 post_replace_ph(ph):
rep_map = {
"": ",",
"": ",",
"": ",",
"": ".",
"": "!",
"": "?",
"\n": ".",
"·": ",",
"": ",",
"...": "",
"v": "V",
}
if ph in rep_map.keys():
ph = rep_map[ph]
if ph in symbols:
return ph
if ph not in symbols:
ph = "UNK"
return ph
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]
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 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()
cache_dict(g2p_dict, CACHE_PATH)
return g2p_dict
eng_dict = get_dict()
def refine_ph(phn):
tone = 0
if re.search(r"\d$", phn):
tone = int(phn[-1]) + 1
phn = phn[:-1]
return phn.lower(), tone
def refine_syllables(syllables):
tones = []
phonemes = []
for phn_list in syllables:
for i in range(len(phn_list)):
phn = phn_list[i]
phn, tone = refine_ph(phn)
phonemes.append(phn)
tones.append(tone)
return phonemes, tones
def text_normalize(text):
# todo: eng text normalize
return text
def g2p(text):
phones = []
tones = []
words = re.split(r"([,;.\-\?\!\s+])", text)
for w in words:
if w.upper() in eng_dict:
phns, tns = refine_syllables(eng_dict[w.upper()])
phones += phns
tones += tns
else:
phone_list = list(filter(lambda p: p != " ", _g2p(w)))
for ph in phone_list:
if ph in arpa:
ph, tn = refine_ph(ph)
phones.append(ph)
tones.append(tn)
else:
phones.append(ph)
tones.append(0)
# todo: implement word2ph
word2ph = [1 for i in phones]
phones = [post_replace_ph(i) for i in phones]
return phones, tones, word2ph
if __name__ == "__main__":
# print(get_dict())
# print(eng_word_to_phoneme("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)

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import torch
def get_bert_feature(norm_text, word2ph):
return torch.zeros(1024, sum(word2ph))

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# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
import re
import sys
import pyopenjtalk
from . import symbols
# Regular expression matching Japanese without punctuation marks:
_japanese_characters = re.compile(
r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
)
# Regular expression matching non-Japanese characters or punctuation marks:
_japanese_marks = re.compile(
r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
)
# List of (symbol, Japanese) pairs for marks:
_symbols_to_japanese = [(re.compile("%s" % x[0]), x[1]) for x in [("", "パーセント")]]
# List of (consonant, sokuon) pairs:
_real_sokuon = [
(re.compile("%s" % x[0]), x[1])
for x in [
(r"Q([↑↓]*[kg])", r"k#\1"),
(r"Q([↑↓]*[tdjʧ])", r"t#\1"),
(r"Q([↑↓]*[sʃ])", r"s\1"),
(r"Q([↑↓]*[pb])", r"p#\1"),
]
]
# List of (consonant, hatsuon) pairs:
_real_hatsuon = [
(re.compile("%s" % x[0]), x[1])
for x in [
(r"N([↑↓]*[pbm])", r"m\1"),
(r"N([↑↓]*[ʧʥj])", r"n^\1"),
(r"N([↑↓]*[tdn])", r"n\1"),
(r"N([↑↓]*[kg])", r"ŋ\1"),
]
]
def post_replace_ph(ph):
rep_map = {
"": ",",
"": ",",
"": ",",
"": ".",
"": "!",
"": "?",
"\n": ".",
"·": ",",
"": ",",
"...": "",
"v": "V",
}
if ph in rep_map.keys():
ph = rep_map[ph]
if ph in symbols:
return ph
if ph not in symbols:
ph = "UNK"
return ph
def symbols_to_japanese(text):
for regex, replacement in _symbols_to_japanese:
text = re.sub(regex, replacement, text)
return text
def preprocess_jap(text):
"""Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html"""
text = symbols_to_japanese(text)
sentences = re.split(_japanese_marks, text)
marks = re.findall(_japanese_marks, text)
text = []
for i, sentence in enumerate(sentences):
if re.match(_japanese_characters, sentence):
p = pyopenjtalk.g2p(sentence)
text += p.split(" ")
if i < len(marks):
text += [marks[i].replace(" ", "")]
return text
def text_normalize(text):
# todo: jap text normalize
return text
def g2p(norm_text):
phones = preprocess_jap(norm_text)
phones = [post_replace_ph(i) for i in phones]
# todo: implement tones and word2ph
tones = [0 for i in phones]
word2ph = [1 for i in phones]
return phones, tones, word2ph
if __name__ == "__main__":
for line in open("../../../Downloads/transcript_utf8.txt").readlines():
text = line.split(":")[1]
phones, tones, word2ph = g2p(text)
for p in phones:
if p == "z":
print(text, phones)
sys.exit(0)

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a AA a
ai AA ai
an AA an
ang AA ang
ao AA ao
ba b a
bai b ai
ban b an
bang b ang
bao b ao
bei b ei
ben b en
beng b eng
bi b i
bian b ian
biao b iao
bie b ie
bin b in
bing b ing
bo b o
bu b u
ca c a
cai c ai
can c an
cang c ang
cao c ao
ce c e
cei c ei
cen c en
ceng c eng
cha ch a
chai ch ai
chan ch an
chang ch ang
chao ch ao
che ch e
chen ch en
cheng ch eng
chi ch ir
chong ch ong
chou ch ou
chu ch u
chua ch ua
chuai ch uai
chuan ch uan
chuang ch uang
chui ch ui
chun ch un
chuo ch uo
ci c i0
cong c ong
cou c ou
cu c u
cuan c uan
cui c ui
cun c un
cuo c uo
da d a
dai d ai
dan d an
dang d ang
dao d ao
de d e
dei d ei
den d en
deng d eng
di d i
dia d ia
dian d ian
diao d iao
die d ie
ding d ing
diu d iu
dong d ong
dou d ou
du d u
duan d uan
dui d ui
dun d un
duo d uo
e EE e
ei EE ei
en EE en
eng EE eng
er EE er
fa f a
fan f an
fang f ang
fei f ei
fen f en
feng f eng
fo f o
fou f ou
fu f u
ga g a
gai g ai
gan g an
gang g ang
gao g ao
ge g e
gei g ei
gen g en
geng g eng
gong g ong
gou g ou
gu g u
gua g ua
guai g uai
guan g uan
guang g uang
gui g ui
gun g un
guo g uo
ha h a
hai h ai
han h an
hang h ang
hao h ao
he h e
hei h ei
hen h en
heng h eng
hong h ong
hou h ou
hu h u
hua h ua
huai h uai
huan h uan
huang h uang
hui h ui
hun h un
huo h uo
ji j i
jia j ia
jian j ian
jiang j iang
jiao j iao
jie j ie
jin j in
jing j ing
jiong j iong
jiu j iu
ju j v
jv j v
juan j van
jvan j van
jue j ve
jve j ve
jun j vn
jvn j vn
ka k a
kai k ai
kan k an
kang k ang
kao k ao
ke k e
kei k ei
ken k en
keng k eng
kong k ong
kou k ou
ku k u
kua k ua
kuai k uai
kuan k uan
kuang k uang
kui k ui
kun k un
kuo k uo
la l a
lai l ai
lan l an
lang l ang
lao l ao
le l e
lei l ei
leng l eng
li l i
lia l ia
lian l ian
liang l iang
liao l iao
lie l ie
lin l in
ling l ing
liu l iu
lo l o
long l ong
lou l ou
lu l u
luan l uan
lun l un
luo l uo
lv l v
lve l ve
ma m a
mai m ai
man m an
mang m ang
mao m ao
me m e
mei m ei
men m en
meng m eng
mi m i
mian m ian
miao m iao
mie m ie
min m in
ming m ing
miu m iu
mo m o
mou m ou
mu m u
na n a
nai n ai
nan n an
nang n ang
nao n ao
ne n e
nei n ei
nen n en
neng n eng
ni n i
nian n ian
niang n iang
niao n iao
nie n ie
nin n in
ning n ing
niu n iu
nong n ong
nou n ou
nu n u
nuan n uan
nun n un
nuo n uo
nv n v
nve n ve
o OO o
ou OO ou
pa p a
pai p ai
pan p an
pang p ang
pao p ao
pei p ei
pen p en
peng p eng
pi p i
pian p ian
piao p iao
pie p ie
pin p in
ping p ing
po p o
pou p ou
pu p u
qi q i
qia q ia
qian q ian
qiang q iang
qiao q iao
qie q ie
qin q in
qing q ing
qiong q iong
qiu q iu
qu q v
qv q v
quan q van
qvan q van
que q ve
qve q ve
qun q vn
qvn q vn
ran r an
rang r ang
rao r ao
re r e
ren r en
reng r eng
ri r ir
rong r ong
rou r ou
ru r u
rua r ua
ruan r uan
rui r ui
run r un
ruo r uo
sa s a
sai s ai
san s an
sang s ang
sao s ao
se s e
sen s en
seng s eng
sha sh a
shai sh ai
shan sh an
shang sh ang
shao sh ao
she sh e
shei sh ei
shen sh en
sheng sh eng
shi sh ir
shou sh ou
shu sh u
shua sh ua
shuai sh uai
shuan sh uan
shuang sh uang
shui sh ui
shun sh un
shuo sh uo
si s i0
song s ong
sou s ou
su s u
suan s uan
sui s ui
sun s un
suo s uo
ta t a
tai t ai
tan t an
tang t ang
tao t ao
te t e
tei t ei
teng t eng
ti t i
tian t ian
tiao t iao
tie t ie
ting t ing
tong t ong
tou t ou
tu t u
tuan t uan
tui t ui
tun t un
tuo t uo
wa w a
wai w ai
wan w an
wang w ang
wei w ei
wen w en
weng w eng
wo w o
wu w u
xi x i
xia x ia
xian x ian
xiang x iang
xiao x iao
xie x ie
xin x in
xing x ing
xiong x iong
xiu x iu
xu x v
xv x v
xuan x van
xvan x van
xue x ve
xve x ve
xun x vn
xvn x vn
ya y a
yan y En
yang y ang
yao y ao
ye y E
yi y i
yin y in
ying y ing
yo y o
yong y ong
you y ou
yu y v
yv y v
yuan y van
yvan y van
yue y ve
yve y ve
yun y vn
yvn y vn
za z a
zai z ai
zan z an
zang z ang
zao z ao
ze z e
zei z ei
zen z en
zeng z eng
zha zh a
zhai zh ai
zhan zh an
zhang zh ang
zhao zh ao
zhe zh e
zhei zh ei
zhen zh en
zheng zh eng
zhi zh ir
zhong zh ong
zhou zh ou
zhu zh u
zhua zh ua
zhuai zh uai
zhuan zh uan
zhuang zh uang
zhui zh ui
zhun zh un
zhuo zh uo
zi z i0
zong z ong
zou z ou
zu z u
zuan z uan
zui z ui
zun z un
zuo z uo

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@@ -0,0 +1,183 @@
punctuation = ["!", "?", "", ",", ".", "'", "-"]
pu_symbols = punctuation + ["SP", "UNK"]
pad = "_"
# chinese
zh_symbols = [
"E",
"En",
"a",
"ai",
"an",
"ang",
"ao",
"b",
"c",
"ch",
"d",
"e",
"ei",
"en",
"eng",
"er",
"f",
"g",
"h",
"i",
"i0",
"ia",
"ian",
"iang",
"iao",
"ie",
"in",
"ing",
"iong",
"ir",
"iu",
"j",
"k",
"l",
"m",
"n",
"o",
"ong",
"ou",
"p",
"q",
"r",
"s",
"sh",
"t",
"u",
"ua",
"uai",
"uan",
"uang",
"ui",
"un",
"uo",
"v",
"van",
"ve",
"vn",
"w",
"x",
"y",
"z",
"zh",
"AA",
"EE",
"OO",
]
num_zh_tones = 6
# japanese
ja_symbols = [
"I",
"N",
"U",
"a",
"b",
"by",
"ch",
"cl",
"d",
"dy",
"e",
"f",
"g",
"gy",
"h",
"hy",
"i",
"j",
"k",
"ky",
"m",
"my",
"n",
"ny",
"o",
"p",
"py",
"r",
"ry",
"s",
"sh",
"t",
"ts",
"u",
"V",
"w",
"y",
"z",
]
num_ja_tones = 1
# English
en_symbols = [
"aa",
"ae",
"ah",
"ao",
"aw",
"ay",
"b",
"ch",
"d",
"dh",
"eh",
"er",
"ey",
"f",
"g",
"hh",
"ih",
"iy",
"jh",
"k",
"l",
"m",
"n",
"ng",
"ow",
"oy",
"p",
"r",
"s",
"sh",
"t",
"th",
"uh",
"uw",
"V",
"w",
"y",
"z",
"zh",
]
num_en_tones = 4
# combine all symbols
normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
symbols = [pad] + normal_symbols + pu_symbols
sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
# combine all tones
num_tones = num_zh_tones + num_ja_tones + num_en_tones
# language maps
language_id_map = {"ZH": 0, "JA": 1, "EN": 2}
num_languages = len(language_id_map.keys())
language_tone_start_map = {
"ZH": 0,
"JA": num_zh_tones,
"EN": num_zh_tones + num_ja_tones,
}
if __name__ == "__main__":
a = set(zh_symbols)
b = set(en_symbols)
print(sorted(a & b))

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@@ -0,0 +1,769 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
from typing import Tuple
import jieba
from pypinyin import lazy_pinyin
from pypinyin import Style
class ToneSandhi:
def __init__(self):
self.must_neural_tone_words = {
"麻烦",
"麻利",
"鸳鸯",
"高粱",
"骨头",
"骆驼",
"马虎",
"首饰",
"馒头",
"馄饨",
"风筝",
"难为",
"队伍",
"阔气",
"闺女",
"门道",
"锄头",
"铺盖",
"铃铛",
"铁匠",
"钥匙",
"里脊",
"里头",
"部分",
"那么",
"道士",
"造化",
"迷糊",
"连累",
"这么",
"这个",
"运气",
"过去",
"软和",
"转悠",
"踏实",
"跳蚤",
"跟头",
"趔趄",
"财主",
"豆腐",
"讲究",
"记性",
"记号",
"认识",
"规矩",
"见识",
"裁缝",
"补丁",
"衣裳",
"衣服",
"衙门",
"街坊",
"行李",
"行当",
"蛤蟆",
"蘑菇",
"薄荷",
"葫芦",
"葡萄",
"萝卜",
"荸荠",
"苗条",
"苗头",
"苍蝇",
"芝麻",
"舒服",
"舒坦",
"舌头",
"自在",
"膏药",
"脾气",
"脑袋",
"脊梁",
"能耐",
"胳膊",
"胭脂",
"胡萝",
"胡琴",
"胡同",
"聪明",
"耽误",
"耽搁",
"耷拉",
"耳朵",
"老爷",
"老实",
"老婆",
"老头",
"老太",
"翻腾",
"罗嗦",
"罐头",
"编辑",
"结实",
"红火",
"累赘",
"糨糊",
"糊涂",
"精神",
"粮食",
"簸箕",
"篱笆",
"算计",
"算盘",
"答应",
"笤帚",
"笑语",
"笑话",
"窟窿",
"窝囊",
"窗户",
"稳当",
"稀罕",
"称呼",
"秧歌",
"秀气",
"秀才",
"福气",
"祖宗",
"砚台",
"码头",
"石榴",
"石头",
"石匠",
"知识",
"眼睛",
"眯缝",
"眨巴",
"眉毛",
"相声",
"盘算",
"白净",
"痢疾",
"痛快",
"疟疾",
"疙瘩",
"疏忽",
"畜生",
"生意",
"甘蔗",
"琵琶",
"琢磨",
"琉璃",
"玻璃",
"玫瑰",
"玄乎",
"狐狸",
"状元",
"特务",
"牲口",
"牙碜",
"牌楼",
"爽快",
"爱人",
"热闹",
"烧饼",
"烟筒",
"烂糊",
"点心",
"炊帚",
"灯笼",
"火候",
"漂亮",
"滑溜",
"溜达",
"温和",
"清楚",
"消息",
"浪头",
"活泼",
"比方",
"正经",
"欺负",
"模糊",
"槟榔",
"棺材",
"棒槌",
"棉花",
"核桃",
"栅栏",
"柴火",
"架势",
"枕头",
"枇杷",
"机灵",
"本事",
"木头",
"木匠",
"朋友",
"月饼",
"月亮",
"暖和",
"明白",
"时候",
"新鲜",
"故事",
"收拾",
"收成",
"提防",
"挖苦",
"挑剔",
"指甲",
"指头",
"拾掇",
"拳头",
"拨弄",
"招牌",
"招呼",
"抬举",
"护士",
"折腾",
"扫帚",
"打量",
"打算",
"打点",
"打扮",
"打听",
"打发",
"扎实",
"扁担",
"戒指",
"懒得",
"意识",
"意思",
"情形",
"悟性",
"怪物",
"思量",
"怎么",
"念头",
"念叨",
"快活",
"忙活",
"志气",
"心思",
"得罪",
"张罗",
"弟兄",
"开通",
"应酬",
"庄稼",
"干事",
"帮手",
"帐篷",
"希罕",
"师父",
"师傅",
"巴结",
"巴掌",
"差事",
"工夫",
"岁数",
"屁股",
"尾巴",
"少爷",
"小气",
"小伙",
"将就",
"对头",
"对付",
"寡妇",
"家伙",
"客气",
"实在",
"官司",
"学问",
"学生",
"字号",
"嫁妆",
"媳妇",
"媒人",
"婆家",
"娘家",
"委屈",
"姑娘",
"姐夫",
"妯娌",
"妥当",
"妖精",
"奴才",
"女婿",
"头发",
"太阳",
"大爷",
"大方",
"大意",
"大夫",
"多少",
"多么",
"外甥",
"壮实",
"地道",
"地方",
"在乎",
"困难",
"嘴巴",
"嘱咐",
"嘟囔",
"嘀咕",
"喜欢",
"喇嘛",
"喇叭",
"商量",
"唾沫",
"哑巴",
"哈欠",
"哆嗦",
"咳嗽",
"和尚",
"告诉",
"告示",
"含糊",
"吓唬",
"后头",
"名字",
"名堂",
"合同",
"吆喝",
"叫唤",
"口袋",
"厚道",
"厉害",
"千斤",
"包袱",
"包涵",
"匀称",
"勤快",
"动静",
"动弹",
"功夫",
"力气",
"前头",
"刺猬",
"刺激",
"别扭",
"利落",
"利索",
"利害",
"分析",
"出息",
"凑合",
"凉快",
"冷战",
"冤枉",
"冒失",
"养活",
"关系",
"先生",
"兄弟",
"便宜",
"使唤",
"佩服",
"作坊",
"体面",
"位置",
"似的",
"伙计",
"休息",
"什么",
"人家",
"亲戚",
"亲家",
"交情",
"云彩",
"事情",
"买卖",
"主意",
"丫头",
"丧气",
"两口",
"东西",
"东家",
"世故",
"不由",
"不在",
"下水",
"下巴",
"上头",
"上司",
"丈夫",
"丈人",
"一辈",
"那个",
"菩萨",
"父亲",
"母亲",
"咕噜",
"邋遢",
"费用",
"冤家",
"甜头",
"介绍",
"荒唐",
"大人",
"泥鳅",
"幸福",
"熟悉",
"计划",
"扑腾",
"蜡烛",
"姥爷",
"照顾",
"喉咙",
"吉他",
"弄堂",
"蚂蚱",
"凤凰",
"拖沓",
"寒碜",
"糟蹋",
"倒腾",
"报复",
"逻辑",
"盘缠",
"喽啰",
"牢骚",
"咖喱",
"扫把",
"惦记",
}
self.must_not_neural_tone_words = {
"男子",
"女子",
"分子",
"原子",
"量子",
"莲子",
"石子",
"瓜子",
"电子",
"人人",
"虎虎",
}
self.punc = ":,;。?!“”‘’':,;.?!"
# the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
# e.g.
# word: "家里"
# pos: "s"
# finals: ['ia1', 'i3']
def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
# reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
for j, item in enumerate(word):
if (
j - 1 >= 0
and item == word[j - 1]
and pos[0] in {"n", "v", "a"}
and word not in self.must_not_neural_tone_words
):
finals[j] = finals[j][:-1] + "5"
ge_idx = word.find("")
if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
finals[-1] = finals[-1][:-1] + "5"
elif len(word) >= 1 and word[-1] in "的地得":
finals[-1] = finals[-1][:-1] + "5"
# e.g. 走了, 看着, 去过
# elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
# finals[-1] = finals[-1][:-1] + "5"
elif (
len(word) > 1
and word[-1] in "们子"
and pos in {"r", "n"}
and word not in self.must_not_neural_tone_words
):
finals[-1] = finals[-1][:-1] + "5"
# e.g. 桌上, 地下, 家里
elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
finals[-1] = finals[-1][:-1] + "5"
# e.g. 上来, 下去
elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
finals[-1] = finals[-1][:-1] + "5"
# 个做量词
elif (
ge_idx >= 1
and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
) or word == "":
finals[ge_idx] = finals[ge_idx][:-1] + "5"
else:
if (
word in self.must_neural_tone_words
or word[-2:] in self.must_neural_tone_words
):
finals[-1] = finals[-1][:-1] + "5"
word_list = self._split_word(word)
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
for i, word in enumerate(word_list):
# conventional neural in Chinese
if (
word in self.must_neural_tone_words
or word[-2:] in self.must_neural_tone_words
):
finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
finals = sum(finals_list, [])
return finals
def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
# e.g. 看不懂
if len(word) == 3 and word[1] == "":
finals[1] = finals[1][:-1] + "5"
else:
for i, char in enumerate(word):
# "不" before tone4 should be bu2, e.g. 不怕
if char == "" and i + 1 < len(word) and finals[i + 1][-1] == "4":
finals[i] = finals[i][:-1] + "2"
return finals
def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
# "一" in number sequences, e.g. 一零零, 二一零
if word.find("") != -1 and all(
[item.isnumeric() for item in word if item != ""]
):
return finals
# "一" between reduplication words shold be yi5, e.g. 看一看
elif len(word) == 3 and word[1] == "" and word[0] == word[-1]:
finals[1] = finals[1][:-1] + "5"
# when "一" is ordinal word, it should be yi1
elif word.startswith("第一"):
finals[1] = finals[1][:-1] + "1"
else:
for i, char in enumerate(word):
if char == "" and i + 1 < len(word):
# "一" before tone4 should be yi2, e.g. 一段
if finals[i + 1][-1] == "4":
finals[i] = finals[i][:-1] + "2"
# "一" before non-tone4 should be yi4, e.g. 一天
else:
# "一" 后面如果是标点,还读一声
if word[i + 1] not in self.punc:
finals[i] = finals[i][:-1] + "4"
return finals
def _split_word(self, word: str) -> List[str]:
word_list = jieba.cut_for_search(word)
word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
first_subword = word_list[0]
first_begin_idx = word.find(first_subword)
if first_begin_idx == 0:
second_subword = word[len(first_subword) :]
new_word_list = [first_subword, second_subword]
else:
second_subword = word[: -len(first_subword)]
new_word_list = [second_subword, first_subword]
return new_word_list
def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
if len(word) == 2 and self._all_tone_three(finals):
finals[0] = finals[0][:-1] + "2"
elif len(word) == 3:
word_list = self._split_word(word)
if self._all_tone_three(finals):
# disyllabic + monosyllabic, e.g. 蒙古/包
if len(word_list[0]) == 2:
finals[0] = finals[0][:-1] + "2"
finals[1] = finals[1][:-1] + "2"
# monosyllabic + disyllabic, e.g. 纸/老虎
elif len(word_list[0]) == 1:
finals[1] = finals[1][:-1] + "2"
else:
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
if len(finals_list) == 2:
for i, sub in enumerate(finals_list):
# e.g. 所有/人
if self._all_tone_three(sub) and len(sub) == 2:
finals_list[i][0] = finals_list[i][0][:-1] + "2"
# e.g. 好/喜欢
elif (
i == 1
and not self._all_tone_three(sub)
and finals_list[i][0][-1] == "3"
and finals_list[0][-1][-1] == "3"
):
finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
finals = sum(finals_list, [])
# split idiom into two words who's length is 2
elif len(word) == 4:
finals_list = [finals[:2], finals[2:]]
finals = []
for sub in finals_list:
if self._all_tone_three(sub):
sub[0] = sub[0][:-1] + "2"
finals += sub
return finals
def _all_tone_three(self, finals: List[str]) -> bool:
return all(x[-1] == "3" for x in finals)
# merge "不" and the word behind it
# if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
new_seg = []
last_word = ""
for word, pos in seg:
if last_word == "":
word = last_word + word
if word != "":
new_seg.append((word, pos))
last_word = word[:]
if last_word == "":
new_seg.append((last_word, "d"))
last_word = ""
return new_seg
# function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
# function 2: merge single "一" and the word behind it
# if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
# e.g.
# input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
# output seg: [['听一听', 'v']]
def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
new_seg = []
# function 1
for i, (word, pos) in enumerate(seg):
if (
i - 1 >= 0
and word == ""
and i + 1 < len(seg)
and seg[i - 1][0] == seg[i + 1][0]
and seg[i - 1][1] == "v"
):
new_seg[i - 1][0] = new_seg[i - 1][0] + "" + new_seg[i - 1][0]
else:
if (
i - 2 >= 0
and seg[i - 1][0] == ""
and seg[i - 2][0] == word
and pos == "v"
):
continue
else:
new_seg.append([word, pos])
seg = new_seg
new_seg = []
# function 2
for i, (word, pos) in enumerate(seg):
if new_seg and new_seg[-1][0] == "":
new_seg[-1][0] = new_seg[-1][0] + word
else:
new_seg.append([word, pos])
return new_seg
# the first and the second words are all_tone_three
def _merge_continuous_three_tones(
self, seg: List[Tuple[str, str]]
) -> List[Tuple[str, str]]:
new_seg = []
sub_finals_list = [
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
for (word, pos) in seg
]
assert len(sub_finals_list) == len(seg)
merge_last = [False] * len(seg)
for i, (word, pos) in enumerate(seg):
if (
i - 1 >= 0
and self._all_tone_three(sub_finals_list[i - 1])
and self._all_tone_three(sub_finals_list[i])
and not merge_last[i - 1]
):
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
if (
not self._is_reduplication(seg[i - 1][0])
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
):
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
merge_last[i] = True
else:
new_seg.append([word, pos])
else:
new_seg.append([word, pos])
return new_seg
def _is_reduplication(self, word: str) -> bool:
return len(word) == 2 and word[0] == word[1]
# the last char of first word and the first char of second word is tone_three
def _merge_continuous_three_tones_2(
self, seg: List[Tuple[str, str]]
) -> List[Tuple[str, str]]:
new_seg = []
sub_finals_list = [
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
for (word, pos) in seg
]
assert len(sub_finals_list) == len(seg)
merge_last = [False] * len(seg)
for i, (word, pos) in enumerate(seg):
if (
i - 1 >= 0
and sub_finals_list[i - 1][-1][-1] == "3"
and sub_finals_list[i][0][-1] == "3"
and not merge_last[i - 1]
):
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
if (
not self._is_reduplication(seg[i - 1][0])
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
):
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
merge_last[i] = True
else:
new_seg.append([word, pos])
else:
new_seg.append([word, pos])
return new_seg
def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
new_seg = []
for i, (word, pos) in enumerate(seg):
if i - 1 >= 0 and word == "" and seg[i - 1][0] != "#":
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
else:
new_seg.append([word, pos])
return new_seg
def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
new_seg = []
for i, (word, pos) in enumerate(seg):
if new_seg and word == new_seg[-1][0]:
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
else:
new_seg.append([word, pos])
return new_seg
def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
seg = self._merge_bu(seg)
try:
seg = self._merge_yi(seg)
except:
print("_merge_yi failed")
seg = self._merge_reduplication(seg)
seg = self._merge_continuous_three_tones(seg)
seg = self._merge_continuous_three_tones_2(seg)
seg = self._merge_er(seg)
return seg
def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
finals = self._bu_sandhi(word, finals)
finals = self._yi_sandhi(word, finals)
finals = self._neural_sandhi(word, pos, finals)
finals = self._three_sandhi(word, finals)
return finals