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Add text preprocessing utilities for TTS pipeline #1639

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95 changes: 95 additions & 0 deletions examples/pipeline_tacotron2/text/numbers.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,95 @@
# *****************************************************************************
# 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
"""

import inflect
import re


_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]+')


def _remove_commas(m: re.Match) -> str:
return m.group(1).replace(',', '')


def _expand_decimal_point(m: re.Match) -> str:
return m.group(1).replace('.', ' point ')


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'


def _expand_ordinal(m: re.Match) -> str:
return _inflect.number_to_words(m.group(0))


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='')


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
22 changes: 22 additions & 0 deletions examples/pipeline_tacotron2/text/test_text.py
Original file line number Diff line number Diff line change
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import unittest

from parameterized import parameterized

from .text_preprocessing import text_to_sequence


class TestTextPreprocessor(unittest.TestCase):

@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):

assert (text_to_sequence(sent) == seq)
85 changes: 85 additions & 0 deletions examples/pipeline_tacotron2/text/text_preprocessing.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,85 @@
# *****************************************************************************
# 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
"""

from typing import List
import re

from unidecode import unidecode

from .numbers import normalize_numbers


# 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'),
]]

_pad = '_'
_punctuation = '!\'(),.:;? '
_special = '-'
_letters = 'abcdefghijklmnopqrstuvwxyz'

symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters)
_symbol_to_id = {s: i for i, s in enumerate(symbols)}


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.

Args:
sent (str): The input sentence to convert to a sequence.

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)
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Could you test the functionality of the method? I tried with some toy string but it didn't work:

sent = 'dr lee`
sent = unidecode(sent)  # convert to ascii
sent = sent.lower()  # lower case
for regex, replacement in _abbreviations:  # expand abbreviations
    sent = re.sub(regex, replacement, sent)
print(sent)

Then I got the same dr lee , not doctor lee.

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For the current version, you'll have to go with dr. lee to get docter lee.

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BTW, I've also added some tests at examples/pipeline_tacotron2/text/test_text.py.

sent = re.sub(_whitespace_re, ' ', sent) # collapse whitespace

return [_symbol_to_id[s] for s in sent if s in _symbol_to_id]
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So the preprocessing will ignore the characters that are not in symbol_to_id. Where is the preprocessing used in tacotron?

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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.

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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.

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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.

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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 :)