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Add text preprocessing utilities for TTS pipeline #1639
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# ***************************************************************************** | ||
# Copyright (c) 2017 Keith Ito | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in | ||
# all copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN | ||
# THE SOFTWARE. | ||
# | ||
# ***************************************************************************** | ||
""" | ||
Modified from https://github.com/keithito/tacotron | ||
""" | ||
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import inflect | ||
import re | ||
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_inflect = inflect.engine() | ||
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') | ||
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') | ||
_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') | ||
_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') | ||
_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') | ||
_number_re = re.compile(r'[0-9]+') | ||
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def _remove_commas(m: re.Match) -> str: | ||
return m.group(1).replace(',', '') | ||
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def _expand_decimal_point(m: re.Match) -> str: | ||
return m.group(1).replace('.', ' point ') | ||
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def _expand_dollars(m: re.Match) -> str: | ||
match = m.group(1) | ||
parts = match.split('.') | ||
if len(parts) > 2: | ||
return match + ' dollars' # Unexpected format | ||
dollars = int(parts[0]) if parts[0] else 0 | ||
cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 | ||
if dollars and cents: | ||
dollar_unit = 'dollar' if dollars == 1 else 'dollars' | ||
cent_unit = 'cent' if cents == 1 else 'cents' | ||
return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) | ||
elif dollars: | ||
dollar_unit = 'dollar' if dollars == 1 else 'dollars' | ||
return '%s %s' % (dollars, dollar_unit) | ||
elif cents: | ||
cent_unit = 'cent' if cents == 1 else 'cents' | ||
return '%s %s' % (cents, cent_unit) | ||
else: | ||
return 'zero dollars' | ||
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def _expand_ordinal(m: re.Match) -> str: | ||
return _inflect.number_to_words(m.group(0)) | ||
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def _expand_number(m: re.Match) -> str: | ||
num = int(m.group(0)) | ||
if num > 1000 and num < 3000: | ||
if num == 2000: | ||
return 'two thousand' | ||
elif num > 2000 and num < 2010: | ||
return 'two thousand ' + _inflect.number_to_words(num % 100) | ||
elif num % 100 == 0: | ||
return _inflect.number_to_words(num // 100) + ' hundred' | ||
else: | ||
return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') | ||
else: | ||
return _inflect.number_to_words(num, andword='') | ||
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def normalize_numbers(text: str) -> str: | ||
text = re.sub(_comma_number_re, _remove_commas, text) | ||
text = re.sub(_pounds_re, r'\1 pounds', text) | ||
text = re.sub(_dollars_re, _expand_dollars, text) | ||
text = re.sub(_decimal_number_re, _expand_decimal_point, text) | ||
text = re.sub(_ordinal_re, _expand_ordinal, text) | ||
text = re.sub(_number_re, _expand_number, text) | ||
return text |
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import unittest | ||
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from parameterized import parameterized | ||
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from .text_preprocessing import text_to_sequence | ||
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class TestTextPreprocessor(unittest.TestCase): | ||
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@parameterized.expand( | ||
[ | ||
["dr. Strange?", [15, 26, 14, 31, 26, 29, 11, 30, 31, 29, 12, 25, 18, 16, 10]], | ||
["ML, is fun.", [24, 23, 6, 11, 20, 30, 11, 17, 32, 25, 7]], | ||
["I love torchaudio!", [20, 11, 23, 26, 33, 16, 11, 31, 26, 29, 14, 19, 12, 32, 15, 20, 26, 2]], | ||
# 'one thousand dollars, twenty cents' | ||
["$1,000.20", [26, 25, 16, 11, 31, 19, 26, 32, 30, 12, 25, 15, 11, 15, 26, 23, 23, | ||
12, 29, 30, 6, 11, 31, 34, 16, 25, 31, 36, 11, 14, 16, 25, 31, 30]], | ||
] | ||
) | ||
def test_text_to_sequence(self, sent, seq): | ||
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assert (text_to_sequence(sent) == seq) |
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# ***************************************************************************** | ||
# Copyright (c) 2017 Keith Ito | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in | ||
# all copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN | ||
# THE SOFTWARE. | ||
# | ||
# ***************************************************************************** | ||
""" | ||
Modified from https://github.com/keithito/tacotron | ||
""" | ||
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from typing import List | ||
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: | ||
_whitespace_re = re.compile(r'\s+') | ||
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# 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'), | ||
]] | ||
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_pad = '_' | ||
_punctuation = '!\'(),.:;? ' | ||
_special = '-' | ||
_letters = 'abcdefghijklmnopqrstuvwxyz' | ||
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symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) | ||
_symbol_to_id = {s: i for i, s in enumerate(symbols)} | ||
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def text_to_sequence(sent: str) -> List[int]: | ||
r'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. | ||
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Args: | ||
sent (str): The input sentence to convert to a sequence. | ||
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Returns: | ||
List of integers corresponding to the symbols in the sentence. | ||
''' | ||
sent = unidecode(sent) # convert to ascii | ||
sent = sent.lower() # lower case | ||
sent = normalize_numbers(sent) # expand numbers | ||
for regex, replacement in _abbreviations: # expand abbreviations | ||
sent = re.sub(regex, replacement, sent) | ||
sent = re.sub(_whitespace_re, ' ', sent) # collapse whitespace | ||
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return [_symbol_to_id[s] for s in sent if s in _symbol_to_id] | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. So the preprocessing will ignore the characters that are not in There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Yes, it will ignore characters that are not in it. You can only encode a finite set of symbols. This preprocessing will be used to preprocess the input text before sending into Tacotron2. You want to encode each character into a number. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I see. I guess it's only used in tacotron2 since other TTS model focus on the vocoder. If so, we can just put it here, otherwise we can put it as a general TTS function. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Yes, for now it is only for Tacotron2. But I doubt this will ever go into the core library as this is a text preprocessing function and torchaudio is an audio/signal processing library. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. It can be a general function if it's a standard processing method in TTS. Since it's Tacotron specific let's keep it here :) |
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Could you test the functionality of the method? I tried with some toy string but it didn't work:
Then I got the same
dr lee
, notdoctor lee
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For the current version, you'll have to go with
dr. lee
to getdocter lee
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BTW, I've also added some tests at
examples/pipeline_tacotron2/text/test_text.py
.