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""" from https://github.com/keithito/tacotron """
import re
from unicodedata import normalize
from text.cleaners import collapse_whitespace
from text.symbols import lang_to_dict, lang_to_dict_inverse
def text_to_sequence(raw_text, lang):
'''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
lang: language of the input text
Returns:
List of integers corresponding to the symbols in the text
'''
_symbol_to_id = lang_to_dict(lang)
text = collapse_whitespace(raw_text)
if lang == 'ko_KR':
text = normalize('NFKD', text)
sequence = [_symbol_to_id[symbol] for symbol in text]
tone = [0 for i in sequence]
elif lang == 'en_US':
_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
sequence = []
while len(text):
m = _curly_re.match(text)
if m is not None:
ar = m.group(1)
sequence += [_symbol_to_id[symbol] for symbol in ar]
ar = m.group(2)
sequence += [_symbol_to_id[symbol] for symbol in ar.split()]
text = m.group(3)
else:
sequence += [_symbol_to_id[symbol] for symbol in text]
break
tone = [0 for i in sequence]
else:
raise RuntimeError('Wrong type of lang')
assert len(sequence) == len(tone)
return sequence, tone
def sequence_to_text(sequence, lang):
'''Converts a sequence of IDs back to a string'''
_id_to_symbol = lang_to_dict_inverse(lang)
result = ''
for symbol_id in sequence:
s = _id_to_symbol[symbol_id]
result += s
return result
def _clean_text(text, cleaner_names):
for name in cleaner_names:
cleaner = getattr(cleaners, name)
if not cleaner:
raise Exception('Unknown cleaner: %s' % name)
text = cleaner(text)
return text
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