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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 | |