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| """ from https://github.com/keithito/tacotron """ | |
| import re | |
| from text import cleaners | |
| from text.symbols import symbols | |
| import torch | |
| _symbol_to_id = {s: i for i, s in enumerate(symbols)} | |
| _id_to_symbol = {i: s for i, s in enumerate(symbols)} | |
| _curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)') | |
| def get_arpabet(word, dictionary): | |
| word_arpabet = dictionary.lookup(word) | |
| if word_arpabet is not None: | |
| return "{" + word_arpabet[0] + "}" | |
| else: | |
| return word | |
| def text_to_sequence(text, cleaner_names=["kazakh_cleaners"], dictionary=None): | |
| '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. | |
| The text can optionally have ARPAbet sequences enclosed in curly braces embedded | |
| in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street." | |
| Args: | |
| text: string to convert to a sequence | |
| cleaner_names: names of the cleaner functions to run the text through | |
| dictionary: arpabet class with arpabet dictionary | |
| Returns: | |
| List of integers corresponding to the symbols in the text | |
| ''' | |
| sequence = [] | |
| space = _symbols_to_sequence(' ') | |
| # Check for curly braces and treat their contents as ARPAbet: | |
| while len(text): | |
| m = _curly_re.match(text) | |
| if not m: | |
| clean_text = _clean_text(text, cleaner_names) | |
| #clean_text = text | |
| if dictionary is not None: | |
| clean_text = [get_arpabet(w, dictionary) for w in clean_text.split(" ")] | |
| for i in range(len(clean_text)): | |
| t = clean_text[i] | |
| if t.startswith("{"): | |
| sequence += _arpabet_to_sequence(t[1:-1]) | |
| else: | |
| sequence += _symbols_to_sequence(t) | |
| sequence += space | |
| else: | |
| sequence += _symbols_to_sequence(clean_text) | |
| break | |
| sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names)) | |
| sequence += _arpabet_to_sequence(m.group(2)) | |
| text = m.group(3) | |
| # remove trailing space | |
| if dictionary is not None: | |
| sequence = sequence[:-1] if sequence[-1] == space[0] else sequence | |
| return sequence | |
| def sequence_to_text(sequence): | |
| '''Converts a sequence of IDs back to a string''' | |
| result = '' | |
| for symbol_id in sequence: | |
| if symbol_id in _id_to_symbol: | |
| s = _id_to_symbol[symbol_id] | |
| # Enclose ARPAbet back in curly braces: | |
| if len(s) > 1 and s[0] == '@': | |
| s = '{%s}' % s[1:] | |
| result += s | |
| return result.replace('}{', ' ') | |
| def convert_text(string): | |
| text_norm = text_to_sequence(string.lower()) | |
| text_norm = torch.IntTensor(text_norm) | |
| text_len = torch.IntTensor([text_norm.size(0)]) | |
| text_padded = torch.LongTensor(1, len(text_norm)) | |
| text_padded.zero_() | |
| text_padded[0, :text_norm.size(0)] = text_norm | |
| return text_padded, text_len | |
| 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 | |
| def _symbols_to_sequence(symbols): | |
| return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] | |
| def _arpabet_to_sequence(text): | |
| return _symbols_to_sequence(['@' + s for s in text.split()]) | |
| def _should_keep_symbol(s): | |
| return s in _symbol_to_id and s != '_' and s != '~' | |