mirror of https://github.com/coqui-ai/TTS.git
142 lines
4.3 KiB
Python
142 lines
4.3 KiB
Python
# -*- coding: utf-8 -*-
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import re
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import phonemizer
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from phonemizer.phonemize import phonemize
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from utils.text import cleaners
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from utils.text.symbols import symbols, phonemes, _punctuations
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# Mappings from symbol to numeric ID and vice versa:
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_symbol_to_id = {s: i for i, s in enumerate(symbols)}
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_id_to_symbol = {i: s for i, s in enumerate(symbols)}
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_phonemes_to_id = {s: i for i, s in enumerate(phonemes)}
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_id_to_phonemes = {i: s for i, s in enumerate(phonemes)}
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# Regular expression matching text enclosed in curly braces:
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_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
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# Regular expression matchinf punctuations, ignoring empty space
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pat = r'['+_punctuations[:-1]+']+'
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def text2phone(text, language):
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'''
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Convert graphemes to phonemes.
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'''
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seperator = phonemizer.separator.Separator(' |', '', '|')
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#try:
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punctuations = re.findall(pat, text)
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ph = phonemize(text, separator=seperator, strip=False, njobs=1, backend='espeak', language=language)
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# Replace \n with matching punctuations.
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if len(punctuations) > 0:
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for punct in punctuations[:-1]:
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ph = ph.replace(' \n', punct+'| ', 1)
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try:
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ph = ph[:-1] + punctuations[-1]
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except:
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print(text)
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return ph
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def phoneme_to_sequence(text, cleaner_names, language):
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'''
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TODO: This ignores punctuations
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'''
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sequence = []
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clean_text = _clean_text(text, cleaner_names)
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phonemes = text2phone(clean_text, language)
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# print(phonemes.replace('|', ''))
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if phonemes is None:
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print("!! After phoneme conversion the result is None. -- {} ".format(clean_text))
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for phoneme in phonemes.split('|'):
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# print(word, ' -- ', phonemes_text)
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sequence += _phoneme_to_sequence(phoneme)
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# Aeepnd EOS char
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sequence.append(_phonemes_to_id['~'])
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return sequence
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def sequence_to_phoneme(sequence):
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'''Converts a sequence of IDs back to a string'''
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result = ''
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for symbol_id in sequence:
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if symbol_id in _id_to_phonemes:
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s = _id_to_phonemes[symbol_id]
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print(s)
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result += s
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return result.replace('}{', ' ')
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def text_to_sequence(text, cleaner_names):
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'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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The text can optionally have ARPAbet sequences enclosed in curly braces embedded
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in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
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Args:
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text: string to convert to a sequence
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cleaner_names: names of the cleaner functions to run the text through
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Returns:
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List of integers corresponding to the symbols in the text
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'''
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sequence = []
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# Check for curly braces and treat their contents as ARPAbet:
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while len(text):
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m = _curly_re.match(text)
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if not m:
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sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
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break
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sequence += _symbols_to_sequence(
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_clean_text(m.group(1), cleaner_names))
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sequence += _arpabet_to_sequence(m.group(2))
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text = m.group(3)
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# Append EOS token
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sequence.append(_symbol_to_id['~'])
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return sequence
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def sequence_to_text(sequence):
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'''Converts a sequence of IDs back to a string'''
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result = ''
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for symbol_id in sequence:
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if symbol_id in _id_to_symbol:
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s = _id_to_symbol[symbol_id]
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# Enclose ARPAbet back in curly braces:
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if len(s) > 1 and s[0] == '@':
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s = '{%s}' % s[1:]
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result += s
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return result.replace('}{', ' ')
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def _clean_text(text, cleaner_names):
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for name in cleaner_names:
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cleaner = getattr(cleaners, name)
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if not cleaner:
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raise Exception('Unknown cleaner: %s' % name)
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text = cleaner(text)
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return text
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def _symbols_to_sequence(symbols):
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return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
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def _phoneme_to_sequence(phonemes):
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return [_phonemes_to_id[s] for s in list(phonemes) if _should_keep_phoneme(s)]
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def _arpabet_to_sequence(text):
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return _symbols_to_sequence(['@' + s for s in text.split()])
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def _should_keep_symbol(s):
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return s in _symbol_to_id and s is not '_' and s is not '~'
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def _should_keep_phoneme(p):
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return p in _phonemes_to_id and p is not '_' and p is not '~'
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