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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 | |
# 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 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='en-us', 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='en-us', backend='espeak', strip=True, preserve_punctuation=True, with_stress=True) | |
phonemes = collapse_whitespace(phonemes) | |
return phonemes | |