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| """ from https://github.com/keithito/tacotron | |
| Cleaners are transformations that run over the input text at both training and eval time. | |
| Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" | |
| hyperparameter. Some cleaners are English-specific. You'll typically want to use: | |
| 1. "english_cleaners" for English text | |
| 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using | |
| the Unidecode library (https://pypi.python.org/pypi/Unidecode) | |
| 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update | |
| the symbols in symbols.py to match your data). | |
| """ | |
| import logging | |
| import re | |
| import phonemizer | |
| import piper_phonemize | |
| from unidecode import unidecode | |
| # To avoid excessive logging we set the log level of the phonemizer package to Critical | |
| critical_logger = logging.getLogger("phonemizer") | |
| critical_logger.setLevel(logging.CRITICAL) | |
| # Intializing the phonemizer globally significantly reduces the speed | |
| # now the phonemizer is not initialising at every call | |
| # Might be less flexible, but it is much-much faster | |
| global_phonemizer = phonemizer.backend.EspeakBackend( | |
| language="en-us", | |
| preserve_punctuation=True, | |
| with_stress=True, | |
| language_switch="remove-flags", | |
| logger=critical_logger, | |
| ) | |
| # Regular expression matching whitespace: | |
| _whitespace_re = re.compile(r"\s+") | |
| # List of (regular expression, replacement) pairs for abbreviations: | |
| _abbreviations = [ | |
| (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) | |
| for x in [ | |
| ("mrs", "misess"), | |
| ("mr", "mister"), | |
| ("dr", "doctor"), | |
| ("st", "saint"), | |
| ("co", "company"), | |
| ("jr", "junior"), | |
| ("maj", "major"), | |
| ("gen", "general"), | |
| ("drs", "doctors"), | |
| ("rev", "reverend"), | |
| ("lt", "lieutenant"), | |
| ("hon", "honorable"), | |
| ("sgt", "sergeant"), | |
| ("capt", "captain"), | |
| ("esq", "esquire"), | |
| ("ltd", "limited"), | |
| ("col", "colonel"), | |
| ("ft", "fort"), | |
| ] | |
| ] | |
| def expand_abbreviations(text): | |
| for regex, replacement in _abbreviations: | |
| text = re.sub(regex, replacement, text) | |
| return text | |
| def lowercase(text): | |
| return text.lower() | |
| def collapse_whitespace(text): | |
| return re.sub(_whitespace_re, " ", text) | |
| def convert_to_ascii(text): | |
| return unidecode(text) | |
| def basic_cleaners(text): | |
| """Basic pipeline that lowercases and collapses whitespace without transliteration.""" | |
| text = lowercase(text) | |
| text = collapse_whitespace(text) | |
| return text | |
| def transliteration_cleaners(text): | |
| """Pipeline for non-English text that transliterates to ASCII.""" | |
| text = convert_to_ascii(text) | |
| text = lowercase(text) | |
| text = collapse_whitespace(text) | |
| return text | |
| def english_cleaners2(text): | |
| """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" | |
| text = convert_to_ascii(text) | |
| text = lowercase(text) | |
| text = expand_abbreviations(text) | |
| phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0] | |
| phonemes = collapse_whitespace(phonemes) | |
| return phonemes | |
| def english_cleaners_piper(text): | |
| """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" | |
| text = convert_to_ascii(text) | |
| text = lowercase(text) | |
| text = expand_abbreviations(text) | |
| phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0]) | |
| phonemes = collapse_whitespace(phonemes) | |
| return phonemes | |