""" 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 import pyopenjtalk from jamo import h2j, j2hcj from pypinyin import lazy_pinyin, BOPOMOFO import jieba, cn2an # This is a list of Korean classifiers preceded by pure Korean numerals. _korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통' # Regular expression matching whitespace: _whitespace_re = re.compile(r'\s+') # Regular expression matching Japanese without punctuation marks: _japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') # Regular expression matching non-Japanese characters or punctuation marks: _japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') # 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'), ]] # List of (hangul, hangul divided) pairs: _hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [ ('ㄳ', 'ㄱㅅ'), ('ㄵ', 'ㄴㅈ'), ('ㄶ', 'ㄴㅎ'), ('ㄺ', 'ㄹㄱ'), ('ㄻ', 'ㄹㅁ'), ('ㄼ', 'ㄹㅂ'), ('ㄽ', 'ㄹㅅ'), ('ㄾ', 'ㄹㅌ'), ('ㄿ', 'ㄹㅍ'), ('ㅀ', 'ㄹㅎ'), ('ㅄ', 'ㅂㅅ'), ('ㅘ', 'ㅗㅏ'), ('ㅙ', 'ㅗㅐ'), ('ㅚ', 'ㅗㅣ'), ('ㅝ', 'ㅜㅓ'), ('ㅞ', 'ㅜㅔ'), ('ㅟ', 'ㅜㅣ'), ('ㅢ', 'ㅡㅣ'), ('ㅑ', 'ㅣㅏ'), ('ㅒ', 'ㅣㅐ'), ('ㅕ', 'ㅣㅓ'), ('ㅖ', 'ㅣㅔ'), ('ㅛ', 'ㅣㅗ'), ('ㅠ', 'ㅣㅜ') ]] # List of (Latin alphabet, hangul) pairs: _latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ ('a', '에이'), ('b', '비'), ('c', '시'), ('d', '디'), ('e', '이'), ('f', '에프'), ('g', '지'), ('h', '에이치'), ('i', '아이'), ('j', '제이'), ('k', '케이'), ('l', '엘'), ('m', '엠'), ('n', '엔'), ('o', '오'), ('p', '피'), ('q', '큐'), ('r', '아르'), ('s', '에스'), ('t', '티'), ('u', '유'), ('v', '브이'), ('w', '더블유'), ('x', '엑스'), ('y', '와이'), ('z', '제트') ]] # List of (Latin alphabet, bopomofo) pairs: _latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ ('a', 'ㄟˉ'), ('b', 'ㄅㄧˋ'), ('c', 'ㄙㄧˉ'), ('d', 'ㄉㄧˋ'), ('e', 'ㄧˋ'), ('f', 'ㄝˊㄈㄨˋ'), ('g', 'ㄐㄧˋ'), ('h', 'ㄝˇㄑㄩˋ'), ('i', 'ㄞˋ'), ('j', 'ㄐㄟˋ'), ('k', 'ㄎㄟˋ'), ('l', 'ㄝˊㄛˋ'), ('m', 'ㄝˊㄇㄨˋ'), ('n', 'ㄣˉ'), ('o', 'ㄡˉ'), ('p', 'ㄆㄧˉ'), ('q', 'ㄎㄧㄡˉ'), ('r', 'ㄚˋ'), ('s', 'ㄝˊㄙˋ'), ('t', 'ㄊㄧˋ'), ('u', 'ㄧㄡˉ'), ('v', 'ㄨㄧˉ'), ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'), ('x', 'ㄝˉㄎㄨˋㄙˋ'), ('y', 'ㄨㄞˋ'), ('z', 'ㄗㄟˋ') ]] # List of (bopomofo, romaji) pairs: _bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ ('ㄅㄛ', 'p⁼wo'), ('ㄆㄛ', 'pʰwo'), ('ㄇㄛ', 'mwo'), ('ㄈㄛ', 'fwo'), ('ㄅ', 'p⁼'), ('ㄆ', 'pʰ'), ('ㄇ', 'm'), ('ㄈ', 'f'), ('ㄉ', 't⁼'), ('ㄊ', 'tʰ'), ('ㄋ', 'n'), ('ㄌ', 'l'), ('ㄍ', 'k⁼'), ('ㄎ', 'kʰ'), ('ㄏ', 'h'), ('ㄐ', 'ʧ⁼'), ('ㄑ', 'ʧʰ'), ('ㄒ', 'ʃ'), ('ㄓ', 'ʦ`⁼'), ('ㄔ', 'ʦ`ʰ'), ('ㄕ', 's`'), ('ㄖ', 'ɹ`'), ('ㄗ', 'ʦ⁼'), ('ㄘ', 'ʦʰ'), ('ㄙ', 's'), ('ㄚ', 'a'), ('ㄛ', 'o'), ('ㄜ', 'ə'), ('ㄝ', 'e'), ('ㄞ', 'ai'), ('ㄟ', 'ei'), ('ㄠ', 'au'), ('ㄡ', 'ou'), ('ㄧㄢ', 'yeNN'), ('ㄢ', 'aNN'), ('ㄧㄣ', 'iNN'), ('ㄣ', 'əNN'), ('ㄤ', 'aNg'), ('ㄧㄥ', 'iNg'), ('ㄨㄥ', 'uNg'), ('ㄩㄥ', 'yuNg'), ('ㄥ', 'əNg'), ('ㄦ', 'əɻ'), ('ㄧ', 'i'), ('ㄨ', 'u'), ('ㄩ', 'ɥ'), ('ˉ', '→'), ('ˊ', '↑'), ('ˇ', '↓↑'), ('ˋ', '↓'), ('˙', ''), (',', ','), ('。', '.'), ('!', '!'), ('?', '?'), ('—', '-') ]] def expand_abbreviations(text): for regex, replacement in _abbreviations: text = re.sub(regex, replacement, text) return 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 japanese_to_romaji_with_accent(text): '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html''' sentences = re.split(_japanese_marks, text) marks = re.findall(_japanese_marks, text) text = '' for i, sentence in enumerate(sentences): if re.match(_japanese_characters, sentence): if text!='': text+=' ' labels = pyopenjtalk.extract_fullcontext(sentence) for n, label in enumerate(labels): phoneme = re.search(r'\-([^\+]*)\+', label).group(1) if phoneme not in ['sil','pau']: text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q') else: continue n_moras = int(re.search(r'/F:(\d+)_', label).group(1)) a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1)) a2 = int(re.search(r"\+(\d+)\+", label).group(1)) a3 = int(re.search(r"\+(\d+)/", label).group(1)) if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']: a2_next=-1 else: a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1)) # Accent phrase boundary if a3 == 1 and a2_next == 1: text += ' ' # Falling elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras: text += '↓' # Rising elif a2 == 1 and a2_next == 2: text += '↑' if i