make grapheme to phoneme an independent part

g2p/__init__.py

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+# coding: utf-8
+
+"""Text processing frontend
+
+All frontend module should have the following functions:
+
+- text_to_sequence(text, p)
+- sequence_to_text(sequence)
+
+and the property:
+
+- n_vocab
+
+"""
+from g2p import en
+
+# optinoal Japanese frontend
+try:
+    from g2p import jp
+except ImportError:
+    jp = None
+
+try:
+    from g2p import ko
+except ImportError:
+    ko = None
+
+# if you are going to use the frontend, you need to modify _characters in symbol.py:
+# _characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!\'(),-.:;? ' + '¡¿ñáéíóúÁÉÍÓÚÑ'
+try:
+    from g2p import es
+except ImportError:
+    es = None

g2p/en/__init__.py

@@ -0,0 +1,38 @@
+# coding: utf-8
+
+from g2p.text.symbols import symbols
+from g2p import text
+from g2p.text import sequence_to_text
+
+import nltk
+from random import random
+
+n_vocab = len(symbols)
+
+_arpabet = nltk.corpus.cmudict.dict()
+
+
+def _maybe_get_arpabet(word, p):
+    try:
+        phonemes = _arpabet[word][0]
+        phonemes = " ".join(phonemes)
+    except KeyError:
+        return word
+
+    return '{%s}' % phonemes if random() < p else word
+
+
+def mix_pronunciation(text, p):
+    text = ' '.join(_maybe_get_arpabet(word, p) for word in text.split(' '))
+    return text
+
+
+def text_to_sequence(text, p=0.0):
+    if p >= 0:
+        text = mix_pronunciation(text, p)
+    from g2p.text import text_to_sequence
+    text = text_to_sequence(text, ["english_cleaners"])
+    return text
+
+
+

g2p/es/__init__.py

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+# coding: utf-8
+from g2p.text.symbols import symbols
+from g2p.text import sequence_to_text
+
+import nltk
+from random import random
+
+n_vocab = len(symbols)
+
+
+def text_to_sequence(text, p=0.0):
+    from g2p.text import text_to_sequence
+    text = text_to_sequence(text, ["basic_cleaners"])
+    return text
+
+
+

g2p/jp/__init__.py

@@ -0,0 +1,79 @@
+# coding: utf-8
+
+
+import MeCab
+import jaconv
+from random import random
+
+n_vocab = 0xffff
+
+_eos = 1
+_pad = 0
+_tagger = None
+
+
+def _yomi(mecab_result):
+    tokens = []
+    yomis = []
+    for line in mecab_result.split("\n")[:-1]:
+        s = line.split("\t")
+        if len(s) == 1:
+            break
+        token, rest = s
+        rest = rest.split(",")
+        tokens.append(token)
+        yomi = rest[7] if len(rest) > 7 else None
+        yomi = None if yomi == "*" else yomi
+        yomis.append(yomi)
+
+    return tokens, yomis
+
+
+def _mix_pronunciation(tokens, yomis, p):
+    return "".join(
+        yomis[idx] if yomis[idx] is not None and random() < p else tokens[idx]
+        for idx in range(len(tokens)))
+
+
+def mix_pronunciation(text, p):
+    global _tagger
+    if _tagger is None:
+        _tagger = MeCab.Tagger("")
+    tokens, yomis = _yomi(_tagger.parse(text))
+    return _mix_pronunciation(tokens, yomis, p)
+
+
+def add_punctuation(text):
+    last = text[-1]
+    if last not in [".", ",", "、", "。", "！", "？", "!", "?"]:
+        text = text + "。"
+    return text
+
+
+def normalize_delimitor(text):
+    text = text.replace(",", "、")
+    text = text.replace(".", "。")
+    text = text.replace("，", "、")
+    text = text.replace("．", "。")
+    return text
+
+
+def text_to_sequence(text, p=0.0):
+    for c in [" ", "　", "「", "」", "『", "』", "・", "【", "】",
+              "（", "）", "(", ")"]:
+        text = text.replace(c, "")
+    text = text.replace("!", "！")
+    text = text.replace("?", "？")
+
+    text = normalize_delimitor(text)
+    text = jaconv.normalize(text)
+    if p > 0:
+        text = mix_pronunciation(text, p)
+    text = jaconv.hira2kata(text)
+    text = add_punctuation(text)
+
+    return [ord(c) for c in text] + [_eos]  # EOS
+
+
+def sequence_to_text(seq):
+    return "".join(chr(n) for n in seq)

g2p/ko/__init__.py

@@ -0,0 +1,17 @@
+# coding: utf-8
+
+
+from random import random
+
+n_vocab = 0xffff
+
+_eos = 1
+_pad = 0
+_tagger = None
+
+
+def text_to_sequence(text, p=0.0):
+    return [ord(c) for c in text] + [_eos]  # EOS
+
+def sequence_to_text(seq):
+    return "".join(chr(n) for n in seq)

g2p/text/__init__.py

@@ -0,0 +1,75 @@
+import re
+from g2p.text import cleaners
+from g2p.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 '~'

g2p/text/cleaners.py

@@ -0,0 +1,97 @@
+'''
+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 add_punctuation(text):
+    if len(text) == 0:
+        return text
+    if text[-1] not in '!,.:;?':
+        text = text + '.'  # without this decoder is confused when to output EOS
+    return 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 = add_punctuation(text)
+    text = lowercase(text)
+    text = expand_numbers(text)
+    text = expand_abbreviations(text)
+    text = collapse_whitespace(text)
+    return text

g2p/text/cmudict.py

@@ -0,0 +1,61 @@
+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)

g2p/text/numbers.py

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+# -*- 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

g2p/text/symbols.py

@@ -0,0 +1,17 @@
+'''
+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 .cmudict import valid_symbols
+
+_pad = '_'
+_eos = '~'
+_characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!\'(),-.:;? '
+
+# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
+_arpabet = ['@' + s for s in valid_symbols]
+
+# Export all symbols:
+symbols = [_pad, _eos] + list(_characters) + _arpabet