Podcastify / melo /text /english.py
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import pickle
import os
import re
from g2p_en import G2p
from . import symbols
from .english_utils.abbreviations import expand_abbreviations
from .english_utils.time_norm import expand_time_english
from .english_utils.number_norm import normalize_numbers
from .japanese import distribute_phone
from transformers import AutoTokenizer
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):
text = text.lower()
text = expand_time_english(text)
text = normalize_numbers(text)
text = expand_abbreviations(text)
return text
model_id = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_id)
def g2p_old(text):
tokenized = tokenizer.tokenize(text)
# import pdb; pdb.set_trace()
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
def g2p(text, pad_start_end=True, tokenized=None):
if tokenized is None:
tokenized = tokenizer.tokenize(text)
# import pdb; pdb.set_trace()
phs = []
ph_groups = []
for t in tokenized:
if not t.startswith("#"):
ph_groups.append([t])
else:
ph_groups[-1].append(t.replace("#", ""))
phones = []
tones = []
word2ph = []
for group in ph_groups:
w = "".join(group)
phone_len = 0
word_len = len(group)
if w.upper() in eng_dict:
phns, tns = refine_syllables(eng_dict[w.upper()])
phones += phns
tones += tns
phone_len += len(phns)
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)
phone_len += 1
aaa = distribute_phone(phone_len, word_len)
word2ph += aaa
phones = [post_replace_ph(i) for i in phones]
if pad_start_end:
phones = ["_"] + phones + ["_"]
tones = [0] + tones + [0]
word2ph = [1] + word2ph + [1]
return phones, tones, word2ph
def get_bert_feature(text, word2ph, device=None):
from text import english_bert
return english_bert.get_bert_feature(text, word2ph, device=device)
if __name__ == "__main__":
# print(get_dict())
# print(eng_word_to_phoneme("hello"))
from text.english_bert import get_bert_feature
text = "In this paper, we propose 1 DSPGAN, a N-F-T GAN-based universal vocoder."
text = text_normalize(text)
phones, tones, word2ph = g2p(text)
import pdb; pdb.set_trace()
bert = get_bert_feature(text, word2ph)
print(phones, tones, word2ph, bert.shape)
# 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)