254 lines
8.4 KiB
Python
254 lines
8.4 KiB
Python
import os
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import re
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import cn2an
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from pypinyin import lazy_pinyin, Style
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# from text.symbols import punctuation
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from .symbols import language_tone_start_map
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from .tone_sandhi import ToneSandhi
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from .english import g2p as g2p_en
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from transformers import AutoTokenizer
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punctuation = ["!", "?", "…", ",", ".", "'", "-"]
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current_file_path = os.path.dirname(__file__)
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pinyin_to_symbol_map = {
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line.split("\t")[0]: line.strip().split("\t")[1]
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for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
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}
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import jieba.posseg as psg
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rep_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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"”": "'",
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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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"《": "'",
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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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"～": "-",
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"~": "-",
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"「": "'",
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"」": "'",
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}
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tone_modifier = ToneSandhi()
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def replace_punctuation(text):
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text = text.replace("嗯", "恩").replace("呣", "母")
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pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
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replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
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replaced_text = re.sub(r"[^\u4e00-\u9fa5_a-zA-Z\s" + "".join(punctuation) + r"]+", "", replaced_text)
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replaced_text = re.sub(r"[\s]+", " ", replaced_text)
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return replaced_text
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def g2p(text, impl='v2'):
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pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
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sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
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if impl == 'v1':
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_func = _g2p
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elif impl == 'v2':
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_func = _g2p_v2
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else:
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raise NotImplementedError()
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phones, tones, word2ph = _func(sentences)
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assert sum(word2ph) == len(phones)
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# assert len(word2ph) == len(text) # Sometimes it will crash,you can add a try-catch.
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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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return phones, tones, word2ph
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def _get_initials_finals(word):
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initials = []
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finals = []
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orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
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orig_finals = lazy_pinyin(
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word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
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)
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for c, v in zip(orig_initials, orig_finals):
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initials.append(c)
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finals.append(v)
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return initials, finals
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model_id = 'bert-base-multilingual-uncased'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def _g2p(segments):
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phones_list = []
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tones_list = []
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word2ph = []
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for seg in segments:
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# Replace all English words in the sentence
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# seg = re.sub("[a-zA-Z]+", "", seg)
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seg_cut = psg.lcut(seg)
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initials = []
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finals = []
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seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
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for word, pos in seg_cut:
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if pos == "eng":
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initials.append(['EN_WORD'])
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finals.append([word])
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else:
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sub_initials, sub_finals = _get_initials_finals(word)
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sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
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initials.append(sub_initials)
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finals.append(sub_finals)
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# assert len(sub_initials) == len(sub_finals) == len(word)
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initials = sum(initials, [])
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finals = sum(finals, [])
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#
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for c, v in zip(initials, finals):
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if c == 'EN_WORD':
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tokenized_en = tokenizer.tokenize(v)
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phones_en, tones_en, word2ph_en = g2p_en(text=None, pad_start_end=False, tokenized=tokenized_en)
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# apply offset to tones_en
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tones_en = [t + language_tone_start_map['EN'] for t in tones_en]
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phones_list += phones_en
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tones_list += tones_en
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word2ph += word2ph_en
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else:
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raw_pinyin = c + v
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# NOTE: post process for pypinyin outputs
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# we discriminate i, ii and iii
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if c == v:
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assert c in punctuation
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phone = [c]
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tone = "0"
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word2ph.append(1)
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else:
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v_without_tone = v[:-1]
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tone = v[-1]
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pinyin = c + v_without_tone
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assert tone in "12345"
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if c:
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# 多音节
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v_rep_map = {
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"uei": "ui",
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"iou": "iu",
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"uen": "un",
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}
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if v_without_tone in v_rep_map.keys():
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pinyin = c + v_rep_map[v_without_tone]
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else:
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# 单音节
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pinyin_rep_map = {
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"ing": "ying",
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"i": "yi",
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"in": "yin",
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"u": "wu",
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}
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if pinyin in pinyin_rep_map.keys():
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pinyin = pinyin_rep_map[pinyin]
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else:
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single_rep_map = {
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"v": "yu",
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"e": "e",
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"i": "y",
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"u": "w",
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}
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if pinyin[0] in single_rep_map.keys():
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pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
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assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
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phone = pinyin_to_symbol_map[pinyin].split(" ")
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word2ph.append(len(phone))
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phones_list += phone
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tones_list += [int(tone)] * len(phone)
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return phones_list, tones_list, word2ph
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def text_normalize(text):
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numbers = re.findall(r"\d+(?:\.?\d+)?", text)
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for number in numbers:
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text = text.replace(number, cn2an.an2cn(number), 1)
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text = replace_punctuation(text)
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return text
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def get_bert_feature(text, word2ph, device):
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from . import chinese_bert
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return chinese_bert.get_bert_feature(text, word2ph, model_id='bert-base-multilingual-uncased', device=device)
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from .chinese import _g2p as _chinese_g2p
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def _g2p_v2(segments):
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spliter = '#$&^!@'
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phones_list = []
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tones_list = []
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word2ph = []
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for text in segments:
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assert spliter not in text
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# replace all english words
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text = re.sub('([a-zA-Z\s]+)', lambda x: f'{spliter}{x.group(1)}{spliter}', text)
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texts = text.split(spliter)
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texts = [t for t in texts if len(t) > 0]
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for text in texts:
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if re.match('[a-zA-Z\s]+', text):
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# english
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tokenized_en = tokenizer.tokenize(text)
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phones_en, tones_en, word2ph_en = g2p_en(text=None, pad_start_end=False, tokenized=tokenized_en)
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# apply offset to tones_en
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tones_en = [t + language_tone_start_map['EN'] for t in tones_en]
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phones_list += phones_en
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tones_list += tones_en
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word2ph += word2ph_en
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else:
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phones_zh, tones_zh, word2ph_zh = _chinese_g2p([text])
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phones_list += phones_zh
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tones_list += tones_zh
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word2ph += word2ph_zh
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return phones_list, tones_list, word2ph
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if __name__ == "__main__":
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# from text.chinese_bert import get_bert_feature
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text = "NFT啊！chemistry 但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏"
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text = '我最近在学习machine learning，希望能够在未来的artificial intelligence领域有所建树。'
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text = '今天下午，我们准备去shopping mall购物，然后晚上去看一场movie。'
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text = '我们现在 also 能够 help 很多公司 use some machine learning 的 algorithms 啊!'
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text = text_normalize(text)
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print(text)
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phones, tones, word2ph = g2p(text, impl='v2')
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bert = get_bert_feature(text, word2ph, device='cuda:0')
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print(phones)
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import pdb; pdb.set_trace()
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# # 示例用法
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# text = "这是一个示例文本：,你好！这是一个测试...."
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# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
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