Refactor: split style_bert_vits2.nlp.english package
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tsukumi
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style_bert_vits2/nlp/english/g2p.py
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240
style_bert_vits2/nlp/english/g2p.py
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import re
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from g2p_en import G2p
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from style_bert_vits2.constants import Languages
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from style_bert_vits2.nlp import bert_models
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from style_bert_vits2.nlp.english.cmudict import get_dict
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from style_bert_vits2.nlp.symbols import PUNCTUATIONS, SYMBOLS
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def g2p(text: str) -> tuple[list[str], list[int], list[int]]:
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ARPA = {
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"AH0",
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"S",
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"AH1",
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"EY2",
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"AE2",
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"EH0",
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"OW2",
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"UH0",
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"NG",
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"B",
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"G",
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"AY0",
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"M",
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"AA0",
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"F",
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"AO0",
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"ER2",
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"UH1",
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"IY1",
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"AH2",
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"DH",
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"IY0",
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"EY1",
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"IH0",
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"K",
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"N",
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"W",
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"IY2",
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"T",
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"AA1",
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"ER1",
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"EH2",
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"OY0",
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"UH2",
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"UW1",
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"Z",
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"AW2",
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"AW1",
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"V",
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"UW2",
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"AA2",
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"ER",
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"AW0",
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"UW0",
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"R",
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"OW1",
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"EH1",
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"ZH",
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"AE0",
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"IH2",
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"IH",
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"Y",
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"JH",
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"P",
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"AY1",
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"EY0",
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"OY2",
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"TH",
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"HH",
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"D",
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"ER0",
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"CH",
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"AO1",
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"AE1",
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"AO2",
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"OY1",
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"AY2",
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"IH1",
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"OW0",
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"L",
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"SH",
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}
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_g2p = G2p()
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phones = []
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tones = []
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phone_len = []
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# tokens = [tokenizer.tokenize(i) for i in words]
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words = __text_to_words(text)
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eng_dict = get_dict()
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for word in words:
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temp_phones, temp_tones = [], []
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if len(word) > 1:
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if "'" in word:
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word = ["".join(word)]
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for w in word:
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if w in PUNCTUATIONS:
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temp_phones.append(w)
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temp_tones.append(0)
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continue
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if w.upper() in eng_dict:
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phns, tns = __refine_syllables(eng_dict[w.upper()])
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temp_phones += [__post_replace_ph(i) for i in phns]
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temp_tones += tns
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# w2ph.append(len(phns))
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else:
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phone_list = list(filter(lambda p: p != " ", _g2p(w))) # type: ignore
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phns = []
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tns = []
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for ph in phone_list:
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if ph in ARPA:
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ph, tn = __refine_ph(ph)
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phns.append(ph)
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tns.append(tn)
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else:
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phns.append(ph)
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tns.append(0)
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temp_phones += [__post_replace_ph(i) for i in phns]
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temp_tones += tns
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phones += temp_phones
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tones += temp_tones
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phone_len.append(len(temp_phones))
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# phones = [post_replace_ph(i) for i in phones]
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word2ph = []
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for token, pl in zip(words, phone_len):
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word_len = len(token)
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aaa = __distribute_phone(pl, word_len)
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word2ph += aaa
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phones = ["_"] + phones + ["_"]
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tones = [0] + tones + [0]
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word2ph = [1] + word2ph + [1]
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assert len(phones) == len(tones), text
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assert len(phones) == sum(word2ph), text
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return phones, tones, word2ph
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def __post_replace_ph(ph: str) -> str:
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REPLACE_MAP = {
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"：": ",",
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"；": ",",
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"，": ",",
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"。": ".",
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"！": "!",
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"？": "?",
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"\n": ".",
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"·": ",",
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"、": ",",
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"…": "...",
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"···": "...",
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"・・・": "...",
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"v": "V",
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}
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if ph in REPLACE_MAP.keys():
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ph = REPLACE_MAP[ph]
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if ph in SYMBOLS:
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return ph
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if ph not in SYMBOLS:
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ph = "UNK"
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return ph
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def __refine_ph(phn: str) -> tuple[str, int]:
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tone = 0
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if re.search(r"\d$", phn):
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tone = int(phn[-1]) + 1
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phn = phn[:-1]
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else:
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tone = 3
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return phn.lower(), tone
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def __refine_syllables(syllables: list[list[str]]) -> tuple[list[str], list[int]]:
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tones = []
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phonemes = []
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for phn_list in syllables:
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for i in range(len(phn_list)):
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phn = phn_list[i]
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phn, tone = __refine_ph(phn)
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phonemes.append(phn)
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tones.append(tone)
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return phonemes, tones
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def __distribute_phone(n_phone: int, n_word: int) -> list[int]:
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phones_per_word = [0] * n_word
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for task in range(n_phone):
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min_tasks = min(phones_per_word)
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min_index = phones_per_word.index(min_tasks)
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phones_per_word[min_index] += 1
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return phones_per_word
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def __text_to_words(text: str) -> list[list[str]]:
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tokenizer = bert_models.load_tokenizer(Languages.EN)
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tokens = tokenizer.tokenize(text)
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words = []
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for idx, t in enumerate(tokens):
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if t.startswith("▁"):
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words.append([t[1:]])
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else:
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if t in PUNCTUATIONS:
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if idx == len(tokens) - 1:
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words.append([f"{t}"])
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else:
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if (
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not tokens[idx + 1].startswith("▁")
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and tokens[idx + 1] not in PUNCTUATIONS
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):
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if idx == 0:
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words.append([])
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words[-1].append(f"{t}")
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else:
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words.append([f"{t}"])
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else:
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if idx == 0:
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words.append([])
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words[-1].append(f"{t}")
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return words
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if __name__ == "__main__":
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# print(get_dict())
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# print(eng_word_to_phoneme("hello"))
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print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
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# all_phones = set()
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# eng_dict = get_dict()
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# for k, syllables in eng_dict.items():
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# for group in syllables:
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# for ph in group:
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# all_phones.add(ph)
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# print(all_phones)
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