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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 re | |
| from unidecode import unidecode | |
| #from phonemizer import phonemize | |
| #from phonemizer.backend import EspeakBackend | |
| #backend = EspeakBackend("vi", preserve_punctuation=True, with_stress=True) | |
| # 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 [ | |
| ("1", "một"), | |
| ("2", "hai"), | |
| ("3", "ba"), | |
| ("4", "bốn"), | |
| ("5", "năm"), | |
| ("6", "sáu"), | |
| ("7", "bảy"), | |
| ("8", "tám"), | |
| ("9", "chín"), | |
| ("10", "mười") | |
| ] | |
| ] | |
| def expand_abbreviations(text): | |
| for regex, replacement in _abbreviations: | |
| text = re.sub(regex, replacement, text) | |
| return text | |
| def expand_numbers(text): | |
| return normalize_numbers(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_cleaners(text): | |
| """Pipeline for English text, including abbreviation expansion.""" | |
| text = convert_to_ascii(text) | |
| text = lowercase(text) | |
| text = expand_abbreviations(text) | |
| phonemes = phonemize(text, language="vi", backend="espeak", strip=True) | |
| phonemes = collapse_whitespace(phonemes) | |
| return phonemes | |
| 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 = phonemize( | |
| text, | |
| language="vi", | |
| backend="espeak", | |
| strip=True, | |
| preserve_punctuation=True, | |
| with_stress=True, | |
| ) | |
| phonemes = collapse_whitespace(phonemes) | |
| return phonemes | |
| def english_cleaners3(text): | |
| """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" | |
| text = convert_to_ascii(text) | |
| text = lowercase(text) | |
| text = expand_abbreviations(text) | |
| phonemes = backend.phonemize([text], strip=True)[0] | |
| phonemes = collapse_whitespace(phonemes) | |
| return phonemes | |