grad_tts_utils.py

# tokenizer

import re

import torch
from transformers import PreTrainedTokenizer

try:
    from unidecode import unidecode
except:
    print("unidecode is not installed")
    pass

try:
    import inflect
except:
    print("inflect is not installed")
    pass


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)

def intersperse(lst, item):
    # Adds blank symbol
    result = [item] * (len(lst) * 2 + 1)
    result[1::2] = lst
    return result


class 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):
        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)



_whitespace_re = re.compile(r'\s+')

_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):
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def transliteration_cleaners(text):
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def english_cleaners(text):
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = expand_numbers(text)
    text = expand_abbreviations(text)
    text = collapse_whitespace(text)
    return text






_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'
    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

""" from https://github.com/keithito/tacotron """


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

# Prepend "@" to ARPAbet symbols to ensure uniqueness:
_arpabet = ['@' + s for s in valid_symbols]

# Export all symbols:
symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet


_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}

_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')


def get_arpabet(word, dictionary):
    word_arpabet = dictionary.lookup(word)
    if word_arpabet is not None:
        return "{" + word_arpabet[0] + "}"
    else:
        return word


def text_to_sequence(text, cleaner_names=[english_cleaners], dictionary=None):
    '''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
      dictionary: arpabet class with arpabet dictionary

    Returns:
      List of integers corresponding to the symbols in the text
    '''
    sequence = []
    space = _symbols_to_sequence(' ')
    # Check for curly braces and treat their contents as ARPAbet:
    while len(text):
        m = _curly_re.match(text)
        if not m:
            clean_text = _clean_text(text, cleaner_names)
            if dictionary is not None:
                clean_text = [get_arpabet(w, dictionary) for w in clean_text.split(" ")]
                for i in range(len(clean_text)):
                    t = clean_text[i]
                    if t.startswith("{"):
                        sequence += _arpabet_to_sequence(t[1:-1])
                    else:
                        sequence += _symbols_to_sequence(t)
                    sequence += space
            else:
                sequence += _symbols_to_sequence(clean_text)
            break
        sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
        sequence += _arpabet_to_sequence(m.group(2))
        text = m.group(3)
  
    # remove trailing space
    if dictionary is not None:
        sequence = sequence[:-1] if sequence[-1] == space[0] else sequence
    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 cleaner in cleaner_names:
        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 != '_' and s != '~'


VOCAB_FILES_NAMES = {
    "dict_file": "merges.txt",
}

class GradTTSTokenizer(PreTrainedTokenizer):
    vocab_files_names = VOCAB_FILES_NAMES
    
    def __init__(self, dict_file, **kwargs):
        super().__init__(**kwargs)
        self.cmu = CMUDict(dict_file)
    
    def __call__(self, text):
        x = torch.LongTensor(intersperse(text_to_sequence(text, dictionary=self.cmu), len(symbols)))[None]
        x_lengths = torch.LongTensor([x.shape[-1]])
        return x.shape, x_lengths