108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
# Copyright (c) 2017 Keith Ito
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" from https://github.com/keithito/tacotron
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Modified to add puncturation removal
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"""
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'''
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Cleaners are transformations that run over the input text at both training and eval time.
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Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
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hyperparameter. Some cleaners are English-specific. You'll typically want to use:
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1. "english_cleaners" for English text
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2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
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the Unidecode library (https://pypi.python.org/pypi/Unidecode)
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3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
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the symbols in symbols.py to match your data).
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'''
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import re
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from unidecode import unidecode
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from .numbers import normalize_numbers
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# Regular expression matching whitespace:
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_whitespace_re = re.compile(r'\s+')
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# List of (regular expression, replacement) pairs for abbreviations:
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_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
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('mrs', 'misess'),
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('mr', 'mister'),
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('dr', 'doctor'),
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('st', 'saint'),
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('co', 'company'),
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('jr', 'junior'),
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('maj', 'major'),
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('gen', 'general'),
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('drs', 'doctors'),
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('rev', 'reverend'),
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('lt', 'lieutenant'),
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('hon', 'honorable'),
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('sgt', 'sergeant'),
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('capt', 'captain'),
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('esq', 'esquire'),
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('ltd', 'limited'),
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('col', 'colonel'),
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('ft', 'fort'),
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]]
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def expand_abbreviations(text):
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for regex, replacement in _abbreviations:
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text = re.sub(regex, replacement, text)
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return text
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def expand_numbers(text):
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return normalize_numbers(text)
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def lowercase(text):
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return text.lower()
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def collapse_whitespace(text):
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return re.sub(_whitespace_re, ' ', text)
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def convert_to_ascii(text):
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return unidecode(text)
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def remove_punctuation(text, table):
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text = text.translate(table)
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text = re.sub(r'&', " and ", text)
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text = re.sub(r'\+', " plus ", text)
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return text
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def basic_cleaners(text):
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'''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
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text = lowercase(text)
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text = collapse_whitespace(text)
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return text
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def transliteration_cleaners(text):
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'''Pipeline for non-English text that transliterates to ASCII.'''
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text = convert_to_ascii(text)
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text = lowercase(text)
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text = collapse_whitespace(text)
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return text
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def english_cleaners(text, table=None):
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'''Pipeline for English text, including number and abbreviation expansion.'''
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text = convert_to_ascii(text)
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text = lowercase(text)
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text = expand_numbers(text)
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text = expand_abbreviations(text)
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if table is not None:
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text = remove_punctuation(text, table)
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text = collapse_whitespace(text)
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return text
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