text: # Code based on https://github.com/keithito/tacotron/blob/master/text/cleaners.py
  '''
  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 .korean import tokenize as ko_tokenize
  from .japanese import tokenize as jp_tokenize
  
  # Added to support LJ_speech
  from unidecode import unidecode
  from .en_numbers import normalize_numbers as en_normalize_numbers
  
  # Regular expression matching whitespace:
  _whitespace_re = re.compile(r'\s+')
  
  
  #def korean_cleaners(text):
  #    '''Pipeline for Korean text, including number and abbreviation expansion.'''
  #    text = ko_tokenize(text)
  #    return text
  
  
  def japanese_cleaners(text):
      '''Pipeline for Japanese text, including number and abbreviation expansion.'''
      text = jp_tokenize(text)
      return text
  
  
  
  # 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 en_normalize_numbers(text)
  
  
  def lowercase(text):
      return text.lower()
  
  
  def collapse_whitespace(text):
      return re.sub(_whitespace_re, ' ', text)
  
  def convert_to_ascii(text):
      '''Converts to ascii, existed in keithito but deleted in carpedm20'''
      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
  
  
  