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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """ This code is modified 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). | |
| """ | |
| # Regular expression matching whitespace: | |
| import re | |
| from unidecode import unidecode | |
| from .numbers import normalize_numbers | |
| _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 number and abbreviation expansion.""" | |
| text = convert_to_ascii(text) | |
| text = lowercase(text) | |
| text = expand_numbers(text) | |
| text = expand_abbreviations(text) | |
| text = collapse_whitespace(text) | |
| return text | |