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text/LICENSE

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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.

text/__init__.py

@@ -1,76 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-import re
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-# Regular expression matching text enclosed in curly braces:
-_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
-
-
-def text_to_sequence(text, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-
-    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
-    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
-
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-
-  # Check for curly braces and treat their contents as ARPAbet:
-  while len(text):
-    m = _curly_re.match(text)
-    if not m:
-      sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
-      break
-    sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
-    sequence += _arpabet_to_sequence(m.group(2))
-    text = m.group(3)
-
-  # Append EOS token
-  sequence.append(_symbol_to_id['~'])
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    if symbol_id in _id_to_symbol:
-      s = _id_to_symbol[symbol_id]
-      # Enclose ARPAbet back in curly braces:
-      if len(s) > 1 and s[0] == '@':
-        s = '{%s}' % s[1:]
-      result += s
-  return result.replace('}{', ' ')
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
-
-
-def _symbols_to_sequence(symbols):
-  return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
-
-
-def _arpabet_to_sequence(text):
-  return _symbols_to_sequence(['@' + s for s in text.split()])
-
-
-def _should_keep_symbol(s):
-  return s in _symbol_to_id and s is not '_' and s is not '~'

text/cleaners.py

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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 .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'),
-]]
-
-
-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

text/cmudict.py

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-""" from https://github.com/keithito/tacotron """
-
-import re
-
-
-valid_symbols = [
-  'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
-  'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
-  'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
-  'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
-  'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
-  'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
-  'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH'
-]
-
-_valid_symbol_set = set(valid_symbols)
-
-
-class CMUDict:
-  '''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict'''
-  def __init__(self, file_or_path, keep_ambiguous=True):
-    if isinstance(file_or_path, str):
-      with open(file_or_path, encoding='latin-1') as f:
-        entries = _parse_cmudict(f)
-    else:
-      entries = _parse_cmudict(file_or_path)
-    if not keep_ambiguous:
-      entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
-    self._entries = entries
-
-
-  def __len__(self):
-    return len(self._entries)
-
-
-  def lookup(self, word):
-    '''Returns list of ARPAbet pronunciations of the given word.'''
-    return self._entries.get(word.upper())
-
-
-
-_alt_re = re.compile(r'\([0-9]+\)')
-
-
-def _parse_cmudict(file):
-  cmudict = {}
-  for line in file:
-    if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
-      parts = line.split('  ')
-      word = re.sub(_alt_re, '', parts[0])
-      pronunciation = _get_pronunciation(parts[1])
-      if pronunciation:
-        if word in cmudict:
-          cmudict[word].append(pronunciation)
-        else:
-          cmudict[word] = [pronunciation]
-  return cmudict
-
-
-def _get_pronunciation(s):
-  parts = s.strip().split(' ')
-  for part in parts:
-    if part not in _valid_symbol_set:
-      return None
-  return ' '.join(parts)

text/numbers.py

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-""" from https://github.com/keithito/tacotron """
-# -*- coding: utf-8 -*-
-
-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):
-  return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-  return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-  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):
-  return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-  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):
-  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

text/symbols.py

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-""" from https://github.com/keithito/tacotron """
-
-'''
-Defines the set of symbols used in text input to the model.
-
-The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
-from text import cmudict
-
-_pad        = '_'
-_eos        = '~'
-_characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!\'(),-.:;? '
-
-# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
-_arpabet = ['@' + s for s in cmudict.valid_symbols]
-
-# Export all symbols:
-symbols = [_pad, _eos] + list(_characters) + _arpabet