ElesisSiegherts
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Browse files- text/__init__.py +61 -0
- text/__pycache__/__init__.cpython-310.pyc +0 -0
- text/__pycache__/__init__.cpython-38.pyc +0 -0
- text/__pycache__/bert_utils.cpython-310.pyc +0 -0
- text/__pycache__/bert_utils.cpython-38.pyc +0 -0
- text/__pycache__/chinese.cpython-310.pyc +0 -0
- text/__pycache__/chinese.cpython-38.pyc +0 -0
- text/__pycache__/chinese_bert.cpython-310.pyc +0 -0
- text/__pycache__/chinese_bert.cpython-38.pyc +0 -0
- text/__pycache__/cleaner.cpython-310.pyc +0 -0
- text/__pycache__/cleaner.cpython-38.pyc +0 -0
- text/__pycache__/english.cpython-310.pyc +0 -0
- text/__pycache__/english.cpython-38.pyc +0 -0
- text/__pycache__/english_bert_mock.cpython-310.pyc +0 -0
- text/__pycache__/english_bert_mock.cpython-38.pyc +0 -0
- text/__pycache__/japanese.cpython-310.pyc +0 -0
- text/__pycache__/japanese.cpython-38.pyc +0 -0
- text/__pycache__/japanese_bert.cpython-310.pyc +0 -0
- text/__pycache__/japanese_bert.cpython-38.pyc +0 -0
- text/__pycache__/symbols.cpython-310.pyc +0 -0
- text/__pycache__/symbols.cpython-38.pyc +0 -0
- text/__pycache__/tone_sandhi.cpython-310.pyc +0 -0
- text/__pycache__/tone_sandhi.cpython-38.pyc +0 -0
- text/bert_utils.py +23 -0
- text/chinese.py +199 -0
- text/chinese_bert.py +101 -0
- text/cleaner.py +28 -0
- text/cmudict.rep +0 -0
- text/cmudict_cache.pickle +3 -0
- text/english.py +453 -0
- text/english_bert_mock.py +42 -0
- text/japanese.py +432 -0
- text/japanese_bert.py +44 -0
- text/opencpop-strict.txt +429 -0
- text/symbols.py +187 -0
- text/tone_sandhi.py +769 -0
text/__init__.py
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from text.symbols import *
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_symbol_to_id = {s: i for i, s in enumerate(symbols)}
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def cleaned_text_to_sequence(cleaned_text, tones, language):
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"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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Args:
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text: string to convert to a sequence
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Returns:
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List of integers corresponding to the symbols in the text
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"""
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phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
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tone_start = language_tone_start_map[language]
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tones = [i + tone_start for i in tones]
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lang_id = language_id_map[language]
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lang_ids = [lang_id for i in phones]
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return phones, tones, lang_ids
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def get_bert(norm_text, word2ph, language, device):
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from .chinese_bert import get_bert_feature as zh_bert
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from .english_bert_mock import get_bert_feature as en_bert
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from .japanese_bert import get_bert_feature as jp_bert
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lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert}
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bert = lang_bert_func_map[language](norm_text, word2ph, device)
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return bert
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def check_bert_models():
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import json
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from pathlib import Path
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from config import config
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from .bert_utils import _check_bert
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if config.mirror.lower() == "openi":
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import openi
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kwargs = {"token": config.openi_token} if config.openi_token else {}
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openi.login(**kwargs)
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with open("./bert/bert_models.json", "r") as fp:
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models = json.load(fp)
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for k, v in models.items():
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local_path = Path("./bert").joinpath(k)
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_check_bert(v["repo_id"], v["files"], local_path)
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def init_openjtalk():
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import platform
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if platform.platform() == "Linux":
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import pyopenjtalk
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pyopenjtalk.g2p("こんにちは,世界。")
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init_openjtalk()
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check_bert_models()
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text/__pycache__/__init__.cpython-310.pyc
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text/__pycache__/__init__.cpython-38.pyc
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text/__pycache__/bert_utils.cpython-310.pyc
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text/__pycache__/bert_utils.cpython-38.pyc
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text/__pycache__/chinese.cpython-310.pyc
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text/__pycache__/chinese.cpython-38.pyc
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text/__pycache__/chinese_bert.cpython-310.pyc
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text/__pycache__/chinese_bert.cpython-38.pyc
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text/__pycache__/cleaner.cpython-310.pyc
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text/__pycache__/cleaner.cpython-38.pyc
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text/__pycache__/english.cpython-310.pyc
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text/__pycache__/english.cpython-38.pyc
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text/__pycache__/english_bert_mock.cpython-310.pyc
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text/__pycache__/english_bert_mock.cpython-38.pyc
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text/__pycache__/japanese.cpython-310.pyc
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text/__pycache__/japanese.cpython-38.pyc
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text/__pycache__/japanese_bert.cpython-310.pyc
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text/__pycache__/japanese_bert.cpython-38.pyc
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text/__pycache__/symbols.cpython-310.pyc
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text/__pycache__/symbols.cpython-38.pyc
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text/__pycache__/tone_sandhi.cpython-310.pyc
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text/__pycache__/tone_sandhi.cpython-38.pyc
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text/bert_utils.py
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from pathlib import Path
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from huggingface_hub import hf_hub_download
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from config import config
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MIRROR: str = config.mirror
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def _check_bert(repo_id, files, local_path):
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for file in files:
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if not Path(local_path).joinpath(file).exists():
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if MIRROR.lower() == "openi":
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import openi
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openi.model.download_model(
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"Stardust_minus/Bert-VITS2", repo_id.split("/")[-1], "./bert"
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)
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else:
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hf_hub_download(
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repo_id, file, local_dir=local_path, local_dir_use_symlinks=False
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)
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text/chinese.py
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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 text.tone_sandhi import ToneSandhi
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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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}
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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(
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r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
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)
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return replaced_text
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def g2p(text):
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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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phones, tones, word2ph = _g2p(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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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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continue
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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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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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139 |
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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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154 |
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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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}
|
159 |
+
if pinyin[0] in single_rep_map.keys():
|
160 |
+
pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
|
161 |
+
|
162 |
+
assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
|
163 |
+
phone = pinyin_to_symbol_map[pinyin].split(" ")
|
164 |
+
word2ph.append(len(phone))
|
165 |
+
|
166 |
+
phones_list += phone
|
167 |
+
tones_list += [int(tone)] * len(phone)
|
168 |
+
return phones_list, tones_list, word2ph
|
169 |
+
|
170 |
+
|
171 |
+
def text_normalize(text):
|
172 |
+
numbers = re.findall(r"\d+(?:\.?\d+)?", text)
|
173 |
+
for number in numbers:
|
174 |
+
text = text.replace(number, cn2an.an2cn(number), 1)
|
175 |
+
text = replace_punctuation(text)
|
176 |
+
return text
|
177 |
+
|
178 |
+
|
179 |
+
def get_bert_feature(text, word2ph):
|
180 |
+
from text import chinese_bert
|
181 |
+
|
182 |
+
return chinese_bert.get_bert_feature(text, word2ph)
|
183 |
+
|
184 |
+
|
185 |
+
if __name__ == "__main__":
|
186 |
+
from text.chinese_bert import get_bert_feature
|
187 |
+
|
188 |
+
text = "啊!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"
|
189 |
+
text = text_normalize(text)
|
190 |
+
print(text)
|
191 |
+
phones, tones, word2ph = g2p(text)
|
192 |
+
bert = get_bert_feature(text, word2ph)
|
193 |
+
|
194 |
+
print(phones, tones, word2ph, bert.shape)
|
195 |
+
|
196 |
+
|
197 |
+
# # 示例用法
|
198 |
+
# text = "这是一个示例文本:,你好!这是一个测试...."
|
199 |
+
# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
|
text/chinese_bert.py
ADDED
@@ -0,0 +1,101 @@
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|
|
|
1 |
+
import sys
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
5 |
+
|
6 |
+
from config import config
|
7 |
+
|
8 |
+
LOCAL_PATH = "./bert/chinese-roberta-wwm-ext-large"
|
9 |
+
|
10 |
+
tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
|
11 |
+
|
12 |
+
models = dict()
|
13 |
+
|
14 |
+
|
15 |
+
def get_bert_feature(text, word2ph, device=config.bert_gen_config.device):
|
16 |
+
if (
|
17 |
+
sys.platform == "darwin"
|
18 |
+
and torch.backends.mps.is_available()
|
19 |
+
and device == "cpu"
|
20 |
+
):
|
21 |
+
device = "mps"
|
22 |
+
if not device:
|
23 |
+
device = "cuda"
|
24 |
+
if device not in models.keys():
|
25 |
+
models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device)
|
26 |
+
with torch.no_grad():
|
27 |
+
inputs = tokenizer(text, return_tensors="pt")
|
28 |
+
for i in inputs:
|
29 |
+
inputs[i] = inputs[i].to(device)
|
30 |
+
res = models[device](**inputs, output_hidden_states=True)
|
31 |
+
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
|
32 |
+
|
33 |
+
assert len(word2ph) == len(text) + 2
|
34 |
+
word2phone = word2ph
|
35 |
+
phone_level_feature = []
|
36 |
+
for i in range(len(word2phone)):
|
37 |
+
repeat_feature = res[i].repeat(word2phone[i], 1)
|
38 |
+
phone_level_feature.append(repeat_feature)
|
39 |
+
|
40 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
41 |
+
|
42 |
+
return phone_level_feature.T
|
43 |
+
|
44 |
+
|
45 |
+
if __name__ == "__main__":
|
46 |
+
word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征
|
47 |
+
word2phone = [
|
48 |
+
1,
|
49 |
+
2,
|
50 |
+
1,
|
51 |
+
2,
|
52 |
+
2,
|
53 |
+
1,
|
54 |
+
2,
|
55 |
+
2,
|
56 |
+
1,
|
57 |
+
2,
|
58 |
+
2,
|
59 |
+
1,
|
60 |
+
2,
|
61 |
+
2,
|
62 |
+
2,
|
63 |
+
2,
|
64 |
+
2,
|
65 |
+
1,
|
66 |
+
1,
|
67 |
+
2,
|
68 |
+
2,
|
69 |
+
1,
|
70 |
+
2,
|
71 |
+
2,
|
72 |
+
2,
|
73 |
+
2,
|
74 |
+
1,
|
75 |
+
2,
|
76 |
+
2,
|
77 |
+
2,
|
78 |
+
2,
|
79 |
+
2,
|
80 |
+
1,
|
81 |
+
2,
|
82 |
+
2,
|
83 |
+
2,
|
84 |
+
2,
|
85 |
+
1,
|
86 |
+
]
|
87 |
+
|
88 |
+
# 计算总帧数
|
89 |
+
total_frames = sum(word2phone)
|
90 |
+
print(word_level_feature.shape)
|
91 |
+
print(word2phone)
|
92 |
+
phone_level_feature = []
|
93 |
+
for i in range(len(word2phone)):
|
94 |
+
print(word_level_feature[i].shape)
|
95 |
+
|
96 |
+
# 对每个词重复word2phone[i]次
|
97 |
+
repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
|
98 |
+
phone_level_feature.append(repeat_feature)
|
99 |
+
|
100 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
101 |
+
print(phone_level_feature.shape) # torch.Size([36, 1024])
|
text/cleaner.py
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from text import chinese, japanese, english, cleaned_text_to_sequence
|
2 |
+
|
3 |
+
|
4 |
+
language_module_map = {"ZH": chinese, "JP": japanese, "EN": english}
|
5 |
+
|
6 |
+
|
7 |
+
def clean_text(text, language):
|
8 |
+
language_module = language_module_map[language]
|
9 |
+
norm_text = language_module.text_normalize(text)
|
10 |
+
phones, tones, word2ph = language_module.g2p(norm_text)
|
11 |
+
return norm_text, phones, tones, word2ph
|
12 |
+
|
13 |
+
|
14 |
+
def clean_text_bert(text, language):
|
15 |
+
language_module = language_module_map[language]
|
16 |
+
norm_text = language_module.text_normalize(text)
|
17 |
+
phones, tones, word2ph = language_module.g2p(norm_text)
|
18 |
+
bert = language_module.get_bert_feature(norm_text, word2ph)
|
19 |
+
return phones, tones, bert
|
20 |
+
|
21 |
+
|
22 |
+
def text_to_sequence(text, language):
|
23 |
+
norm_text, phones, tones, word2ph = clean_text(text, language)
|
24 |
+
return cleaned_text_to_sequence(phones, tones, language)
|
25 |
+
|
26 |
+
|
27 |
+
if __name__ == "__main__":
|
28 |
+
pass
|
text/cmudict.rep
ADDED
The diff for this file is too large to render.
See raw diff
|
|
text/cmudict_cache.pickle
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b9b21b20325471934ba92f2e4a5976989e7d920caa32e7a286eacb027d197949
|
3 |
+
size 6212655
|
text/english.py
ADDED
@@ -0,0 +1,453 @@
|
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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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|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import pickle
|
2 |
+
import os
|
3 |
+
import re
|
4 |
+
from g2p_en import G2p
|
5 |
+
from transformers import DebertaV2Tokenizer
|
6 |
+
|
7 |
+
from text import symbols
|
8 |
+
|
9 |
+
current_file_path = os.path.dirname(__file__)
|
10 |
+
CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
|
11 |
+
CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
|
12 |
+
_g2p = G2p()
|
13 |
+
LOCAL_PATH = "./bert/deberta-v3-large"
|
14 |
+
tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
|
15 |
+
|
16 |
+
arpa = {
|
17 |
+
"AH0",
|
18 |
+
"S",
|
19 |
+
"AH1",
|
20 |
+
"EY2",
|
21 |
+
"AE2",
|
22 |
+
"EH0",
|
23 |
+
"OW2",
|
24 |
+
"UH0",
|
25 |
+
"NG",
|
26 |
+
"B",
|
27 |
+
"G",
|
28 |
+
"AY0",
|
29 |
+
"M",
|
30 |
+
"AA0",
|
31 |
+
"F",
|
32 |
+
"AO0",
|
33 |
+
"ER2",
|
34 |
+
"UH1",
|
35 |
+
"IY1",
|
36 |
+
"AH2",
|
37 |
+
"DH",
|
38 |
+
"IY0",
|
39 |
+
"EY1",
|
40 |
+
"IH0",
|
41 |
+
"K",
|
42 |
+
"N",
|
43 |
+
"W",
|
44 |
+
"IY2",
|
45 |
+
"T",
|
46 |
+
"AA1",
|
47 |
+
"ER1",
|
48 |
+
"EH2",
|
49 |
+
"OY0",
|
50 |
+
"UH2",
|
51 |
+
"UW1",
|
52 |
+
"Z",
|
53 |
+
"AW2",
|
54 |
+
"AW1",
|
55 |
+
"V",
|
56 |
+
"UW2",
|
57 |
+
"AA2",
|
58 |
+
"ER",
|
59 |
+
"AW0",
|
60 |
+
"UW0",
|
61 |
+
"R",
|
62 |
+
"OW1",
|
63 |
+
"EH1",
|
64 |
+
"ZH",
|
65 |
+
"AE0",
|
66 |
+
"IH2",
|
67 |
+
"IH",
|
68 |
+
"Y",
|
69 |
+
"JH",
|
70 |
+
"P",
|
71 |
+
"AY1",
|
72 |
+
"EY0",
|
73 |
+
"OY2",
|
74 |
+
"TH",
|
75 |
+
"HH",
|
76 |
+
"D",
|
77 |
+
"ER0",
|
78 |
+
"CH",
|
79 |
+
"AO1",
|
80 |
+
"AE1",
|
81 |
+
"AO2",
|
82 |
+
"OY1",
|
83 |
+
"AY2",
|
84 |
+
"IH1",
|
85 |
+
"OW0",
|
86 |
+
"L",
|
87 |
+
"SH",
|
88 |
+
}
|
89 |
+
|
90 |
+
|
91 |
+
def post_replace_ph(ph):
|
92 |
+
rep_map = {
|
93 |
+
":": ",",
|
94 |
+
";": ",",
|
95 |
+
",": ",",
|
96 |
+
"。": ".",
|
97 |
+
"!": "!",
|
98 |
+
"?": "?",
|
99 |
+
"\n": ".",
|
100 |
+
"·": ",",
|
101 |
+
"、": ",",
|
102 |
+
"…": "...",
|
103 |
+
"···": "...",
|
104 |
+
"・・・": "...",
|
105 |
+
"v": "V",
|
106 |
+
}
|
107 |
+
if ph in rep_map.keys():
|
108 |
+
ph = rep_map[ph]
|
109 |
+
if ph in symbols:
|
110 |
+
return ph
|
111 |
+
if ph not in symbols:
|
112 |
+
ph = "UNK"
|
113 |
+
return ph
|
114 |
+
|
115 |
+
|
116 |
+
rep_map = {
|
117 |
+
":": ",",
|
118 |
+
";": ",",
|
119 |
+
",": ",",
|
120 |
+
"。": ".",
|
121 |
+
"!": "!",
|
122 |
+
"?": "?",
|
123 |
+
"\n": ".",
|
124 |
+
".": ".",
|
125 |
+
"…": "...",
|
126 |
+
"···": "...",
|
127 |
+
"・・・": "...",
|
128 |
+
"·": ",",
|
129 |
+
"・": ",",
|
130 |
+
"、": ",",
|
131 |
+
"$": ".",
|
132 |
+
"“": "'",
|
133 |
+
"”": "'",
|
134 |
+
'"': "'",
|
135 |
+
"‘": "'",
|
136 |
+
"’": "'",
|
137 |
+
"(": "'",
|
138 |
+
")": "'",
|
139 |
+
"(": "'",
|
140 |
+
")": "'",
|
141 |
+
"《": "'",
|
142 |
+
"》": "'",
|
143 |
+
"【": "'",
|
144 |
+
"】": "'",
|
145 |
+
"[": "'",
|
146 |
+
"]": "'",
|
147 |
+
"—": "-",
|
148 |
+
"−": "-",
|
149 |
+
"~": "-",
|
150 |
+
"~": "-",
|
151 |
+
"「": "'",
|
152 |
+
"」": "'",
|
153 |
+
}
|
154 |
+
|
155 |
+
|
156 |
+
def replace_punctuation(text):
|
157 |
+
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
158 |
+
|
159 |
+
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
160 |
+
|
161 |
+
# replaced_text = re.sub(
|
162 |
+
# r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
|
163 |
+
# + "".join(punctuation)
|
164 |
+
# + r"]+",
|
165 |
+
# "",
|
166 |
+
# replaced_text,
|
167 |
+
# )
|
168 |
+
|
169 |
+
return replaced_text
|
170 |
+
|
171 |
+
|
172 |
+
def read_dict():
|
173 |
+
g2p_dict = {}
|
174 |
+
start_line = 49
|
175 |
+
with open(CMU_DICT_PATH) as f:
|
176 |
+
line = f.readline()
|
177 |
+
line_index = 1
|
178 |
+
while line:
|
179 |
+
if line_index >= start_line:
|
180 |
+
line = line.strip()
|
181 |
+
word_split = line.split(" ")
|
182 |
+
word = word_split[0]
|
183 |
+
|
184 |
+
syllable_split = word_split[1].split(" - ")
|
185 |
+
g2p_dict[word] = []
|
186 |
+
for syllable in syllable_split:
|
187 |
+
phone_split = syllable.split(" ")
|
188 |
+
g2p_dict[word].append(phone_split)
|
189 |
+
|
190 |
+
line_index = line_index + 1
|
191 |
+
line = f.readline()
|
192 |
+
|
193 |
+
return g2p_dict
|
194 |
+
|
195 |
+
|
196 |
+
def cache_dict(g2p_dict, file_path):
|
197 |
+
with open(file_path, "wb") as pickle_file:
|
198 |
+
pickle.dump(g2p_dict, pickle_file)
|
199 |
+
|
200 |
+
|
201 |
+
def get_dict():
|
202 |
+
if os.path.exists(CACHE_PATH):
|
203 |
+
with open(CACHE_PATH, "rb") as pickle_file:
|
204 |
+
g2p_dict = pickle.load(pickle_file)
|
205 |
+
else:
|
206 |
+
g2p_dict = read_dict()
|
207 |
+
cache_dict(g2p_dict, CACHE_PATH)
|
208 |
+
|
209 |
+
return g2p_dict
|
210 |
+
|
211 |
+
|
212 |
+
eng_dict = get_dict()
|
213 |
+
|
214 |
+
|
215 |
+
def refine_ph(phn):
|
216 |
+
tone = 0
|
217 |
+
if re.search(r"\d$", phn):
|
218 |
+
tone = int(phn[-1]) + 1
|
219 |
+
phn = phn[:-1]
|
220 |
+
return phn.lower(), tone
|
221 |
+
|
222 |
+
|
223 |
+
def refine_syllables(syllables):
|
224 |
+
tones = []
|
225 |
+
phonemes = []
|
226 |
+
for phn_list in syllables:
|
227 |
+
for i in range(len(phn_list)):
|
228 |
+
phn = phn_list[i]
|
229 |
+
phn, tone = refine_ph(phn)
|
230 |
+
phonemes.append(phn)
|
231 |
+
tones.append(tone)
|
232 |
+
return phonemes, tones
|
233 |
+
|
234 |
+
|
235 |
+
import re
|
236 |
+
import inflect
|
237 |
+
|
238 |
+
_inflect = inflect.engine()
|
239 |
+
_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
|
240 |
+
_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
|
241 |
+
_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
|
242 |
+
_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
|
243 |
+
_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
|
244 |
+
_number_re = re.compile(r"[0-9]+")
|
245 |
+
|
246 |
+
# List of (regular expression, replacement) pairs for abbreviations:
|
247 |
+
_abbreviations = [
|
248 |
+
(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
|
249 |
+
for x in [
|
250 |
+
("mrs", "misess"),
|
251 |
+
("mr", "mister"),
|
252 |
+
("dr", "doctor"),
|
253 |
+
("st", "saint"),
|
254 |
+
("co", "company"),
|
255 |
+
("jr", "junior"),
|
256 |
+
("maj", "major"),
|
257 |
+
("gen", "general"),
|
258 |
+
("drs", "doctors"),
|
259 |
+
("rev", "reverend"),
|
260 |
+
("lt", "lieutenant"),
|
261 |
+
("hon", "honorable"),
|
262 |
+
("sgt", "sergeant"),
|
263 |
+
("capt", "captain"),
|
264 |
+
("esq", "esquire"),
|
265 |
+
("ltd", "limited"),
|
266 |
+
("col", "colonel"),
|
267 |
+
("ft", "fort"),
|
268 |
+
]
|
269 |
+
]
|
270 |
+
|
271 |
+
|
272 |
+
# List of (ipa, lazy ipa) pairs:
|
273 |
+
_lazy_ipa = [
|
274 |
+
(re.compile("%s" % x[0]), x[1])
|
275 |
+
for x in [
|
276 |
+
("r", "ɹ"),
|
277 |
+
("æ", "e"),
|
278 |
+
("ɑ", "a"),
|
279 |
+
("ɔ", "o"),
|
280 |
+
("ð", "z"),
|
281 |
+
("θ", "s"),
|
282 |
+
("ɛ", "e"),
|
283 |
+
("ɪ", "i"),
|
284 |
+
("ʊ", "u"),
|
285 |
+
("ʒ", "ʥ"),
|
286 |
+
("ʤ", "ʥ"),
|
287 |
+
("ˈ", "↓"),
|
288 |
+
]
|
289 |
+
]
|
290 |
+
|
291 |
+
# List of (ipa, lazy ipa2) pairs:
|
292 |
+
_lazy_ipa2 = [
|
293 |
+
(re.compile("%s" % x[0]), x[1])
|
294 |
+
for x in [
|
295 |
+
("r", "ɹ"),
|
296 |
+
("ð", "z"),
|
297 |
+
("θ", "s"),
|
298 |
+
("ʒ", "ʑ"),
|
299 |
+
("ʤ", "dʑ"),
|
300 |
+
("ˈ", "↓"),
|
301 |
+
]
|
302 |
+
]
|
303 |
+
|
304 |
+
# List of (ipa, ipa2) pairs
|
305 |
+
_ipa_to_ipa2 = [
|
306 |
+
(re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")]
|
307 |
+
]
|
308 |
+
|
309 |
+
|
310 |
+
def _expand_dollars(m):
|
311 |
+
match = m.group(1)
|
312 |
+
parts = match.split(".")
|
313 |
+
if len(parts) > 2:
|
314 |
+
return match + " dollars" # Unexpected format
|
315 |
+
dollars = int(parts[0]) if parts[0] else 0
|
316 |
+
cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
|
317 |
+
if dollars and cents:
|
318 |
+
dollar_unit = "dollar" if dollars == 1 else "dollars"
|
319 |
+
cent_unit = "cent" if cents == 1 else "cents"
|
320 |
+
return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
|
321 |
+
elif dollars:
|
322 |
+
dollar_unit = "dollar" if dollars == 1 else "dollars"
|
323 |
+
return "%s %s" % (dollars, dollar_unit)
|
324 |
+
elif cents:
|
325 |
+
cent_unit = "cent" if cents == 1 else "cents"
|
326 |
+
return "%s %s" % (cents, cent_unit)
|
327 |
+
else:
|
328 |
+
return "zero dollars"
|
329 |
+
|
330 |
+
|
331 |
+
def _remove_commas(m):
|
332 |
+
return m.group(1).replace(",", "")
|
333 |
+
|
334 |
+
|
335 |
+
def _expand_ordinal(m):
|
336 |
+
return _inflect.number_to_words(m.group(0))
|
337 |
+
|
338 |
+
|
339 |
+
def _expand_number(m):
|
340 |
+
num = int(m.group(0))
|
341 |
+
if num > 1000 and num < 3000:
|
342 |
+
if num == 2000:
|
343 |
+
return "two thousand"
|
344 |
+
elif num > 2000 and num < 2010:
|
345 |
+
return "two thousand " + _inflect.number_to_words(num % 100)
|
346 |
+
elif num % 100 == 0:
|
347 |
+
return _inflect.number_to_words(num // 100) + " hundred"
|
348 |
+
else:
|
349 |
+
return _inflect.number_to_words(
|
350 |
+
num, andword="", zero="oh", group=2
|
351 |
+
).replace(", ", " ")
|
352 |
+
else:
|
353 |
+
return _inflect.number_to_words(num, andword="")
|
354 |
+
|
355 |
+
|
356 |
+
def _expand_decimal_point(m):
|
357 |
+
return m.group(1).replace(".", " point ")
|
358 |
+
|
359 |
+
|
360 |
+
def normalize_numbers(text):
|
361 |
+
text = re.sub(_comma_number_re, _remove_commas, text)
|
362 |
+
text = re.sub(_pounds_re, r"\1 pounds", text)
|
363 |
+
text = re.sub(_dollars_re, _expand_dollars, text)
|
364 |
+
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
|
365 |
+
text = re.sub(_ordinal_re, _expand_ordinal, text)
|
366 |
+
text = re.sub(_number_re, _expand_number, text)
|
367 |
+
return text
|
368 |
+
|
369 |
+
|
370 |
+
def text_normalize(text):
|
371 |
+
text = normalize_numbers(text)
|
372 |
+
text = replace_punctuation(text)
|
373 |
+
text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text)
|
374 |
+
return text
|
375 |
+
|
376 |
+
|
377 |
+
def distribute_phone(n_phone, n_word):
|
378 |
+
phones_per_word = [0] * n_word
|
379 |
+
for task in range(n_phone):
|
380 |
+
min_tasks = min(phones_per_word)
|
381 |
+
min_index = phones_per_word.index(min_tasks)
|
382 |
+
phones_per_word[min_index] += 1
|
383 |
+
return phones_per_word
|
384 |
+
|
385 |
+
|
386 |
+
def sep_text(text):
|
387 |
+
words = re.split(r"([,;.\?\!\s+])", text)
|
388 |
+
words = [word for word in words if word.strip() != ""]
|
389 |
+
return words
|
390 |
+
|
391 |
+
|
392 |
+
def g2p(text):
|
393 |
+
phones = []
|
394 |
+
tones = []
|
395 |
+
# word2ph = []
|
396 |
+
words = sep_text(text)
|
397 |
+
tokens = [tokenizer.tokenize(i) for i in words]
|
398 |
+
for word in words:
|
399 |
+
if word.upper() in eng_dict:
|
400 |
+
phns, tns = refine_syllables(eng_dict[word.upper()])
|
401 |
+
phones.append([post_replace_ph(i) for i in phns])
|
402 |
+
tones.append(tns)
|
403 |
+
# word2ph.append(len(phns))
|
404 |
+
else:
|
405 |
+
phone_list = list(filter(lambda p: p != " ", _g2p(word)))
|
406 |
+
phns = []
|
407 |
+
tns = []
|
408 |
+
for ph in phone_list:
|
409 |
+
if ph in arpa:
|
410 |
+
ph, tn = refine_ph(ph)
|
411 |
+
phns.append(ph)
|
412 |
+
tns.append(tn)
|
413 |
+
else:
|
414 |
+
phns.append(ph)
|
415 |
+
tns.append(0)
|
416 |
+
phones.append([post_replace_ph(i) for i in phns])
|
417 |
+
tones.append(tns)
|
418 |
+
# word2ph.append(len(phns))
|
419 |
+
# phones = [post_replace_ph(i) for i in phones]
|
420 |
+
|
421 |
+
word2ph = []
|
422 |
+
for token, phoneme in zip(tokens, phones):
|
423 |
+
phone_len = len(phoneme)
|
424 |
+
word_len = len(token)
|
425 |
+
|
426 |
+
aaa = distribute_phone(phone_len, word_len)
|
427 |
+
word2ph += aaa
|
428 |
+
|
429 |
+
phones = ["_"] + [j for i in phones for j in i] + ["_"]
|
430 |
+
tones = [0] + [j for i in tones for j in i] + [0]
|
431 |
+
word2ph = [1] + word2ph + [1]
|
432 |
+
assert len(phones) == len(tones), text
|
433 |
+
assert len(phones) == sum(word2ph), text
|
434 |
+
|
435 |
+
return phones, tones, word2ph
|
436 |
+
|
437 |
+
|
438 |
+
def get_bert_feature(text, word2ph):
|
439 |
+
from text import english_bert_mock
|
440 |
+
|
441 |
+
return english_bert_mock.get_bert_feature(text, word2ph)
|
442 |
+
|
443 |
+
|
444 |
+
if __name__ == "__main__":
|
445 |
+
# print(get_dict())
|
446 |
+
# print(eng_word_to_phoneme("hello"))
|
447 |
+
print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
|
448 |
+
# all_phones = set()
|
449 |
+
# for k, syllables in eng_dict.items():
|
450 |
+
# for group in syllables:
|
451 |
+
# for ph in group:
|
452 |
+
# all_phones.add(ph)
|
453 |
+
# print(all_phones)
|
text/english_bert_mock.py
ADDED
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import sys
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from transformers import DebertaV2Model, DebertaV2Tokenizer
|
5 |
+
|
6 |
+
from config import config
|
7 |
+
|
8 |
+
|
9 |
+
LOCAL_PATH = "./bert/deberta-v3-large"
|
10 |
+
|
11 |
+
tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
|
12 |
+
|
13 |
+
models = dict()
|
14 |
+
|
15 |
+
|
16 |
+
def get_bert_feature(text, word2ph, device=config.bert_gen_config.device):
|
17 |
+
if (
|
18 |
+
sys.platform == "darwin"
|
19 |
+
and torch.backends.mps.is_available()
|
20 |
+
and device == "cpu"
|
21 |
+
):
|
22 |
+
device = "mps"
|
23 |
+
if not device:
|
24 |
+
device = "cuda"
|
25 |
+
if device not in models.keys():
|
26 |
+
models[device] = DebertaV2Model.from_pretrained(LOCAL_PATH).to(device)
|
27 |
+
with torch.no_grad():
|
28 |
+
inputs = tokenizer(text, return_tensors="pt")
|
29 |
+
for i in inputs:
|
30 |
+
inputs[i] = inputs[i].to(device)
|
31 |
+
res = models[device](**inputs, output_hidden_states=True)
|
32 |
+
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
|
33 |
+
assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph))
|
34 |
+
word2phone = word2ph
|
35 |
+
phone_level_feature = []
|
36 |
+
for i in range(len(word2phone)):
|
37 |
+
repeat_feature = res[i].repeat(word2phone[i], 1)
|
38 |
+
phone_level_feature.append(repeat_feature)
|
39 |
+
|
40 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
41 |
+
|
42 |
+
return phone_level_feature.T
|
text/japanese.py
ADDED
@@ -0,0 +1,432 @@
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|
|
|
|
|
1 |
+
# Convert Japanese text to phonemes which is
|
2 |
+
# compatible with Julius https://github.com/julius-speech/segmentation-kit
|
3 |
+
import re
|
4 |
+
import unicodedata
|
5 |
+
|
6 |
+
from transformers import AutoTokenizer
|
7 |
+
|
8 |
+
from text import punctuation, symbols
|
9 |
+
|
10 |
+
from num2words import num2words
|
11 |
+
|
12 |
+
import pyopenjtalk
|
13 |
+
import jaconv
|
14 |
+
|
15 |
+
|
16 |
+
def kata2phoneme(text: str) -> str:
|
17 |
+
"""Convert katakana text to phonemes."""
|
18 |
+
text = text.strip()
|
19 |
+
if text == "ー":
|
20 |
+
return ["ー"]
|
21 |
+
elif text.startswith("ー"):
|
22 |
+
return ["ー"] + kata2phoneme(text[1:])
|
23 |
+
res = []
|
24 |
+
prev = None
|
25 |
+
while text:
|
26 |
+
if re.match(_MARKS, text):
|
27 |
+
res.append(text)
|
28 |
+
text = text[1:]
|
29 |
+
continue
|
30 |
+
if text.startswith("ー"):
|
31 |
+
if prev:
|
32 |
+
res.append(prev[-1])
|
33 |
+
text = text[1:]
|
34 |
+
continue
|
35 |
+
res += pyopenjtalk.g2p(text).lower().replace("cl", "q").split(" ")
|
36 |
+
break
|
37 |
+
# res = _COLON_RX.sub(":", res)
|
38 |
+
return res
|
39 |
+
|
40 |
+
|
41 |
+
def hira2kata(text: str) -> str:
|
42 |
+
return jaconv.hira2kata(text)
|
43 |
+
|
44 |
+
|
45 |
+
_SYMBOL_TOKENS = set(list("・、。?!"))
|
46 |
+
_NO_YOMI_TOKENS = set(list("「」『』―()[][]"))
|
47 |
+
_MARKS = re.compile(
|
48 |
+
r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
|
49 |
+
)
|
50 |
+
|
51 |
+
|
52 |
+
def text2kata(text: str) -> str:
|
53 |
+
parsed = pyopenjtalk.run_frontend(text)
|
54 |
+
|
55 |
+
res = []
|
56 |
+
for parts in parsed:
|
57 |
+
word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
|
58 |
+
"’", ""
|
59 |
+
)
|
60 |
+
if yomi:
|
61 |
+
if re.match(_MARKS, yomi):
|
62 |
+
if len(word) > 1:
|
63 |
+
word = [replace_punctuation(i) for i in list(word)]
|
64 |
+
yomi = word
|
65 |
+
res += yomi
|
66 |
+
sep += word
|
67 |
+
continue
|
68 |
+
elif word not in rep_map.keys() and word not in rep_map.values():
|
69 |
+
word = ","
|
70 |
+
yomi = word
|
71 |
+
res.append(yomi)
|
72 |
+
else:
|
73 |
+
if word in _SYMBOL_TOKENS:
|
74 |
+
res.append(word)
|
75 |
+
elif word in ("っ", "ッ"):
|
76 |
+
res.append("ッ")
|
77 |
+
elif word in _NO_YOMI_TOKENS:
|
78 |
+
pass
|
79 |
+
else:
|
80 |
+
res.append(word)
|
81 |
+
return hira2kata("".join(res))
|
82 |
+
|
83 |
+
|
84 |
+
def text2sep_kata(text: str) -> (list, list):
|
85 |
+
parsed = pyopenjtalk.run_frontend(text)
|
86 |
+
|
87 |
+
res = []
|
88 |
+
sep = []
|
89 |
+
for parts in parsed:
|
90 |
+
word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
|
91 |
+
"’", ""
|
92 |
+
)
|
93 |
+
if yomi:
|
94 |
+
if re.match(_MARKS, yomi):
|
95 |
+
if len(word) > 1:
|
96 |
+
word = [replace_punctuation(i) for i in list(word)]
|
97 |
+
yomi = word
|
98 |
+
res += yomi
|
99 |
+
sep += word
|
100 |
+
continue
|
101 |
+
elif word not in rep_map.keys() and word not in rep_map.values():
|
102 |
+
word = ","
|
103 |
+
yomi = word
|
104 |
+
res.append(yomi)
|
105 |
+
else:
|
106 |
+
if word in _SYMBOL_TOKENS:
|
107 |
+
res.append(word)
|
108 |
+
elif word in ("っ", "ッ"):
|
109 |
+
res.append("ッ")
|
110 |
+
elif word in _NO_YOMI_TOKENS:
|
111 |
+
pass
|
112 |
+
else:
|
113 |
+
res.append(word)
|
114 |
+
sep.append(word)
|
115 |
+
return sep, [hira2kata(i) for i in res], get_accent(parsed)
|
116 |
+
|
117 |
+
|
118 |
+
def get_accent(parsed):
|
119 |
+
labels = pyopenjtalk.make_label(parsed)
|
120 |
+
|
121 |
+
phonemes = []
|
122 |
+
accents = []
|
123 |
+
for n, label in enumerate(labels):
|
124 |
+
phoneme = re.search(r"\-([^\+]*)\+", label).group(1)
|
125 |
+
if phoneme not in ["sil", "pau"]:
|
126 |
+
phonemes.append(phoneme.replace("cl", "q").lower())
|
127 |
+
else:
|
128 |
+
continue
|
129 |
+
a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
|
130 |
+
a2 = int(re.search(r"\+(\d+)\+", label).group(1))
|
131 |
+
if re.search(r"\-([^\+]*)\+", labels[n + 1]).group(1) in ["sil", "pau"]:
|
132 |
+
a2_next = -1
|
133 |
+
else:
|
134 |
+
a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
|
135 |
+
# Falling
|
136 |
+
if a1 == 0 and a2_next == a2 + 1:
|
137 |
+
accents.append(-1)
|
138 |
+
# Rising
|
139 |
+
elif a2 == 1 and a2_next == 2:
|
140 |
+
accents.append(1)
|
141 |
+
else:
|
142 |
+
accents.append(0)
|
143 |
+
return list(zip(phonemes, accents))
|
144 |
+
|
145 |
+
|
146 |
+
_ALPHASYMBOL_YOMI = {
|
147 |
+
"#": "シャープ",
|
148 |
+
"%": "パーセント",
|
149 |
+
"&": "アンド",
|
150 |
+
"+": "プラス",
|
151 |
+
"-": "マイナス",
|
152 |
+
":": "コロン",
|
153 |
+
";": "セミコロン",
|
154 |
+
"<": "小なり",
|
155 |
+
"=": "イコール",
|
156 |
+
">": "大なり",
|
157 |
+
"@": "アット",
|
158 |
+
"a": "エー",
|
159 |
+
"b": "ビー",
|
160 |
+
"c": "シー",
|
161 |
+
"d": "ディー",
|
162 |
+
"e": "イー",
|
163 |
+
"f": "エフ",
|
164 |
+
"g": "ジー",
|
165 |
+
"h": "エイチ",
|
166 |
+
"i": "アイ",
|
167 |
+
"j": "ジェー",
|
168 |
+
"k": "ケー",
|
169 |
+
"l": "エル",
|
170 |
+
"m": "エム",
|
171 |
+
"n": "エヌ",
|
172 |
+
"o": "オー",
|
173 |
+
"p": "ピー",
|
174 |
+
"q": "キュー",
|
175 |
+
"r": "アール",
|
176 |
+
"s": "エス",
|
177 |
+
"t": "ティー",
|
178 |
+
"u": "ユー",
|
179 |
+
"v": "ブイ",
|
180 |
+
"w": "ダブリュー",
|
181 |
+
"x": "エックス",
|
182 |
+
"y": "ワイ",
|
183 |
+
"z": "ゼット",
|
184 |
+
"α": "アルファ",
|
185 |
+
"β": "ベータ",
|
186 |
+
"γ": "ガンマ",
|
187 |
+
"δ": "デルタ",
|
188 |
+
"ε": "イプシロン",
|
189 |
+
"ζ": "ゼータ",
|
190 |
+
"η": "イータ",
|
191 |
+
"θ": "シータ",
|
192 |
+
"ι": "イオタ",
|
193 |
+
"κ": "カッパ",
|
194 |
+
"λ": "ラムダ",
|
195 |
+
"μ": "ミュー",
|
196 |
+
"ν": "ニュー",
|
197 |
+
"ξ": "クサイ",
|
198 |
+
"ο": "オミクロン",
|
199 |
+
"π": "パイ",
|
200 |
+
"ρ": "ロー",
|
201 |
+
"σ": "シグマ",
|
202 |
+
"τ": "タウ",
|
203 |
+
"υ": "ウプシロン",
|
204 |
+
"φ": "ファイ",
|
205 |
+
"χ": "カイ",
|
206 |
+
"ψ": "プサイ",
|
207 |
+
"ω": "オメガ",
|
208 |
+
}
|
209 |
+
|
210 |
+
|
211 |
+
_NUMBER_WITH_SEPARATOR_RX = re.compile("[0-9]{1,3}(,[0-9]{3})+")
|
212 |
+
_CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
|
213 |
+
_CURRENCY_RX = re.compile(r"([$¥£€])([0-9.]*[0-9])")
|
214 |
+
_NUMBER_RX = re.compile(r"[0-9]+(\.[0-9]+)?")
|
215 |
+
|
216 |
+
|
217 |
+
def japanese_convert_numbers_to_words(text: str) -> str:
|
218 |
+
res = _NUMBER_WITH_SEPARATOR_RX.sub(lambda m: m[0].replace(",", ""), text)
|
219 |
+
res = _CURRENCY_RX.sub(lambda m: m[2] + _CURRENCY_MAP.get(m[1], m[1]), res)
|
220 |
+
res = _NUMBER_RX.sub(lambda m: num2words(m[0], lang="ja"), res)
|
221 |
+
return res
|
222 |
+
|
223 |
+
|
224 |
+
def japanese_convert_alpha_symbols_to_words(text: str) -> str:
|
225 |
+
return "".join([_ALPHASYMBOL_YOMI.get(ch, ch) for ch in text.lower()])
|
226 |
+
|
227 |
+
|
228 |
+
def japanese_text_to_phonemes(text: str) -> str:
|
229 |
+
"""Convert Japanese text to phonemes."""
|
230 |
+
res = unicodedata.normalize("NFKC", text)
|
231 |
+
res = japanese_convert_numbers_to_words(res)
|
232 |
+
# res = japanese_convert_alpha_symbols_to_words(res)
|
233 |
+
res = text2kata(res)
|
234 |
+
res = kata2phoneme(res)
|
235 |
+
return res
|
236 |
+
|
237 |
+
|
238 |
+
def is_japanese_character(char):
|
239 |
+
# 定义日语文字系统的 Unicode 范围
|
240 |
+
japanese_ranges = [
|
241 |
+
(0x3040, 0x309F), # 平假名
|
242 |
+
(0x30A0, 0x30FF), # 片假名
|
243 |
+
(0x4E00, 0x9FFF), # 汉字 (CJK Unified Ideographs)
|
244 |
+
(0x3400, 0x4DBF), # 汉字扩展 A
|
245 |
+
(0x20000, 0x2A6DF), # 汉字扩展 B
|
246 |
+
# 可以根据需要添加其他汉字扩展范围
|
247 |
+
]
|
248 |
+
|
249 |
+
# 将字符的 Unicode 编码转换为整数
|
250 |
+
char_code = ord(char)
|
251 |
+
|
252 |
+
# 检查字符是否在任何一个日语范围内
|
253 |
+
for start, end in japanese_ranges:
|
254 |
+
if start <= char_code <= end:
|
255 |
+
return True
|
256 |
+
|
257 |
+
return False
|
258 |
+
|
259 |
+
|
260 |
+
rep_map = {
|
261 |
+
":": ",",
|
262 |
+
";": ",",
|
263 |
+
",": ",",
|
264 |
+
"。": ".",
|
265 |
+
"!": "!",
|
266 |
+
"?": "?",
|
267 |
+
"\n": ".",
|
268 |
+
".": ".",
|
269 |
+
"…": "...",
|
270 |
+
"···": "...",
|
271 |
+
"・・・": "...",
|
272 |
+
"·": ",",
|
273 |
+
"・": ",",
|
274 |
+
"、": ",",
|
275 |
+
"$": ".",
|
276 |
+
"“": "'",
|
277 |
+
"”": "'",
|
278 |
+
'"': "'",
|
279 |
+
"‘": "'",
|
280 |
+
"’": "'",
|
281 |
+
"(": "'",
|
282 |
+
")": "'",
|
283 |
+
"(": "'",
|
284 |
+
")": "'",
|
285 |
+
"《": "'",
|
286 |
+
"》": "'",
|
287 |
+
"【": "'",
|
288 |
+
"】": "'",
|
289 |
+
"[": "'",
|
290 |
+
"]": "'",
|
291 |
+
"—": "-",
|
292 |
+
"−": "-",
|
293 |
+
"~": "-",
|
294 |
+
"~": "-",
|
295 |
+
"「": "'",
|
296 |
+
"」": "'",
|
297 |
+
}
|
298 |
+
|
299 |
+
|
300 |
+
def replace_punctuation(text):
|
301 |
+
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
302 |
+
|
303 |
+
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
304 |
+
|
305 |
+
replaced_text = re.sub(
|
306 |
+
r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
|
307 |
+
+ "".join(punctuation)
|
308 |
+
+ r"]+",
|
309 |
+
"",
|
310 |
+
replaced_text,
|
311 |
+
)
|
312 |
+
|
313 |
+
return replaced_text
|
314 |
+
|
315 |
+
|
316 |
+
def text_normalize(text):
|
317 |
+
res = unicodedata.normalize("NFKC", text)
|
318 |
+
res = japanese_convert_numbers_to_words(res)
|
319 |
+
# res = "".join([i for i in res if is_japanese_character(i)])
|
320 |
+
res = replace_punctuation(res)
|
321 |
+
res = res.replace("゙", "")
|
322 |
+
return res
|
323 |
+
|
324 |
+
|
325 |
+
def distribute_phone(n_phone, n_word):
|
326 |
+
phones_per_word = [0] * n_word
|
327 |
+
for task in range(n_phone):
|
328 |
+
min_tasks = min(phones_per_word)
|
329 |
+
min_index = phones_per_word.index(min_tasks)
|
330 |
+
phones_per_word[min_index] += 1
|
331 |
+
return phones_per_word
|
332 |
+
|
333 |
+
|
334 |
+
def handle_long(sep_phonemes):
|
335 |
+
for i in range(len(sep_phonemes)):
|
336 |
+
if sep_phonemes[i][0] == "ー":
|
337 |
+
sep_phonemes[i][0] = sep_phonemes[i - 1][-1]
|
338 |
+
if "ー" in sep_phonemes[i]:
|
339 |
+
for j in range(len(sep_phonemes[i])):
|
340 |
+
if sep_phonemes[i][j] == "ー":
|
341 |
+
sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1]
|
342 |
+
return sep_phonemes
|
343 |
+
|
344 |
+
|
345 |
+
tokenizer = AutoTokenizer.from_pretrained("./bert/deberta-v2-large-japanese-char-wwm")
|
346 |
+
|
347 |
+
|
348 |
+
def align_tones(phones, tones):
|
349 |
+
res = []
|
350 |
+
for pho in phones:
|
351 |
+
temp = [0] * len(pho)
|
352 |
+
for idx, p in enumerate(pho):
|
353 |
+
if len(tones) == 0:
|
354 |
+
break
|
355 |
+
if p == tones[0][0]:
|
356 |
+
temp[idx] = tones[0][1]
|
357 |
+
if idx > 0:
|
358 |
+
temp[idx] += temp[idx - 1]
|
359 |
+
tones.pop(0)
|
360 |
+
temp = [0] + temp
|
361 |
+
temp = temp[:-1]
|
362 |
+
if -1 in temp:
|
363 |
+
temp = [i + 1 for i in temp]
|
364 |
+
res.append(temp)
|
365 |
+
res = [i for j in res for i in j]
|
366 |
+
assert not any([i < 0 for i in res]) and not any([i > 1 for i in res])
|
367 |
+
return res
|
368 |
+
|
369 |
+
|
370 |
+
def rearrange_tones(tones, phones):
|
371 |
+
res = [0] * len(tones)
|
372 |
+
for i in range(len(tones)):
|
373 |
+
if i == 0:
|
374 |
+
if tones[i] not in punctuation:
|
375 |
+
res[i] = 1
|
376 |
+
elif tones[i] == prev:
|
377 |
+
if phones[i] in punctuation:
|
378 |
+
res[i] = 0
|
379 |
+
else:
|
380 |
+
res[i] = 1
|
381 |
+
elif tones[i] > prev:
|
382 |
+
res[i] = 2
|
383 |
+
elif tones[i] < prev:
|
384 |
+
res[i - 1] = 3
|
385 |
+
res[i] = 1
|
386 |
+
prev = tones[i]
|
387 |
+
return res
|
388 |
+
|
389 |
+
|
390 |
+
def g2p(norm_text):
|
391 |
+
sep_text, sep_kata, acc = text2sep_kata(norm_text)
|
392 |
+
sep_tokenized = []
|
393 |
+
for i in sep_text:
|
394 |
+
if i not in punctuation:
|
395 |
+
sep_tokenized.append(tokenizer.tokenize(i))
|
396 |
+
else:
|
397 |
+
sep_tokenized.append([i])
|
398 |
+
|
399 |
+
sep_phonemes = handle_long([kata2phoneme(i) for i in sep_kata])
|
400 |
+
# 异常处理,MeCab不认识的词的话会一路传到这里来,然后炸掉。目前来看只有那些超级稀有的生僻词会出现这种情况
|
401 |
+
for i in sep_phonemes:
|
402 |
+
for j in i:
|
403 |
+
assert j in symbols, (sep_text, sep_kata, sep_phonemes)
|
404 |
+
tones = align_tones(sep_phonemes, acc)
|
405 |
+
|
406 |
+
word2ph = []
|
407 |
+
for token, phoneme in zip(sep_tokenized, sep_phonemes):
|
408 |
+
phone_len = len(phoneme)
|
409 |
+
word_len = len(token)
|
410 |
+
|
411 |
+
aaa = distribute_phone(phone_len, word_len)
|
412 |
+
word2ph += aaa
|
413 |
+
phones = ["_"] + [j for i in sep_phonemes for j in i] + ["_"]
|
414 |
+
# tones = [0] + rearrange_tones(tones, phones[1:-1]) + [0]
|
415 |
+
tones = [0] + tones + [0]
|
416 |
+
word2ph = [1] + word2ph + [1]
|
417 |
+
assert len(phones) == len(tones)
|
418 |
+
return phones, tones, word2ph
|
419 |
+
|
420 |
+
|
421 |
+
if __name__ == "__main__":
|
422 |
+
tokenizer = AutoTokenizer.from_pretrained("./bert/deberta-v2-large-japanese")
|
423 |
+
text = "hello,こんにちは、世界ー!……"
|
424 |
+
from text.japanese_bert import get_bert_feature
|
425 |
+
|
426 |
+
text = text_normalize(text)
|
427 |
+
print(text)
|
428 |
+
|
429 |
+
phones, tones, word2ph = g2p(text)
|
430 |
+
bert = get_bert_feature(text, word2ph)
|
431 |
+
|
432 |
+
print(phones, tones, word2ph, bert.shape)
|
text/japanese_bert.py
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import sys
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
5 |
+
|
6 |
+
from config import config
|
7 |
+
from text.japanese import text2sep_kata
|
8 |
+
|
9 |
+
LOCAL_PATH = "./bert/deberta-v2-large-japanese-char-wwm"
|
10 |
+
|
11 |
+
tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
|
12 |
+
|
13 |
+
models = dict()
|
14 |
+
|
15 |
+
|
16 |
+
def get_bert_feature(text, word2ph, device=config.bert_gen_config.device):
|
17 |
+
text = "".join(text2sep_kata(text)[0])
|
18 |
+
if (
|
19 |
+
sys.platform == "darwin"
|
20 |
+
and torch.backends.mps.is_available()
|
21 |
+
and device == "cpu"
|
22 |
+
):
|
23 |
+
device = "mps"
|
24 |
+
if not device:
|
25 |
+
device = "cuda"
|
26 |
+
if device not in models.keys():
|
27 |
+
models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device)
|
28 |
+
with torch.no_grad():
|
29 |
+
inputs = tokenizer(text, return_tensors="pt")
|
30 |
+
for i in inputs:
|
31 |
+
inputs[i] = inputs[i].to(device)
|
32 |
+
res = models[device](**inputs, output_hidden_states=True)
|
33 |
+
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
|
34 |
+
|
35 |
+
assert len(word2ph) == len(text) + 2
|
36 |
+
word2phone = word2ph
|
37 |
+
phone_level_feature = []
|
38 |
+
for i in range(len(word2phone)):
|
39 |
+
repeat_feature = res[i].repeat(word2phone[i], 1)
|
40 |
+
phone_level_feature.append(repeat_feature)
|
41 |
+
|
42 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
43 |
+
|
44 |
+
return phone_level_feature.T
|
text/opencpop-strict.txt
ADDED
@@ -0,0 +1,429 @@
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
a AA a
|
2 |
+
ai AA ai
|
3 |
+
an AA an
|
4 |
+
ang AA ang
|
5 |
+
ao AA ao
|
6 |
+
ba b a
|
7 |
+
bai b ai
|
8 |
+
ban b an
|
9 |
+
bang b ang
|
10 |
+
bao b ao
|
11 |
+
bei b ei
|
12 |
+
ben b en
|
13 |
+
beng b eng
|
14 |
+
bi b i
|
15 |
+
bian b ian
|
16 |
+
biao b iao
|
17 |
+
bie b ie
|
18 |
+
bin b in
|
19 |
+
bing b ing
|
20 |
+
bo b o
|
21 |
+
bu b u
|
22 |
+
ca c a
|
23 |
+
cai c ai
|
24 |
+
can c an
|
25 |
+
cang c ang
|
26 |
+
cao c ao
|
27 |
+
ce c e
|
28 |
+
cei c ei
|
29 |
+
cen c en
|
30 |
+
ceng c eng
|
31 |
+
cha ch a
|
32 |
+
chai ch ai
|
33 |
+
chan ch an
|
34 |
+
chang ch ang
|
35 |
+
chao ch ao
|
36 |
+
che ch e
|
37 |
+
chen ch en
|
38 |
+
cheng ch eng
|
39 |
+
chi ch ir
|
40 |
+
chong ch ong
|
41 |
+
chou ch ou
|
42 |
+
chu ch u
|
43 |
+
chua ch ua
|
44 |
+
chuai ch uai
|
45 |
+
chuan ch uan
|
46 |
+
chuang ch uang
|
47 |
+
chui ch ui
|
48 |
+
chun ch un
|
49 |
+
chuo ch uo
|
50 |
+
ci c i0
|
51 |
+
cong c ong
|
52 |
+
cou c ou
|
53 |
+
cu c u
|
54 |
+
cuan c uan
|
55 |
+
cui c ui
|
56 |
+
cun c un
|
57 |
+
cuo c uo
|
58 |
+
da d a
|
59 |
+
dai d ai
|
60 |
+
dan d an
|
61 |
+
dang d ang
|
62 |
+
dao d ao
|
63 |
+
de d e
|
64 |
+
dei d ei
|
65 |
+
den d en
|
66 |
+
deng d eng
|
67 |
+
di d i
|
68 |
+
dia d ia
|
69 |
+
dian d ian
|
70 |
+
diao d iao
|
71 |
+
die d ie
|
72 |
+
ding d ing
|
73 |
+
diu d iu
|
74 |
+
dong d ong
|
75 |
+
dou d ou
|
76 |
+
du d u
|
77 |
+
duan d uan
|
78 |
+
dui d ui
|
79 |
+
dun d un
|
80 |
+
duo d uo
|
81 |
+
e EE e
|
82 |
+
ei EE ei
|
83 |
+
en EE en
|
84 |
+
eng EE eng
|
85 |
+
er EE er
|
86 |
+
fa f a
|
87 |
+
fan f an
|
88 |
+
fang f ang
|
89 |
+
fei f ei
|
90 |
+
fen f en
|
91 |
+
feng f eng
|
92 |
+
fo f o
|
93 |
+
fou f ou
|
94 |
+
fu f u
|
95 |
+
ga g a
|
96 |
+
gai g ai
|
97 |
+
gan g an
|
98 |
+
gang g ang
|
99 |
+
gao g ao
|
100 |
+
ge g e
|
101 |
+
gei g ei
|
102 |
+
gen g en
|
103 |
+
geng g eng
|
104 |
+
gong g ong
|
105 |
+
gou g ou
|
106 |
+
gu g u
|
107 |
+
gua g ua
|
108 |
+
guai g uai
|
109 |
+
guan g uan
|
110 |
+
guang g uang
|
111 |
+
gui g ui
|
112 |
+
gun g un
|
113 |
+
guo g uo
|
114 |
+
ha h a
|
115 |
+
hai h ai
|
116 |
+
han h an
|
117 |
+
hang h ang
|
118 |
+
hao h ao
|
119 |
+
he h e
|
120 |
+
hei h ei
|
121 |
+
hen h en
|
122 |
+
heng h eng
|
123 |
+
hong h ong
|
124 |
+
hou h ou
|
125 |
+
hu h u
|
126 |
+
hua h ua
|
127 |
+
huai h uai
|
128 |
+
huan h uan
|
129 |
+
huang h uang
|
130 |
+
hui h ui
|
131 |
+
hun h un
|
132 |
+
huo h uo
|
133 |
+
ji j i
|
134 |
+
jia j ia
|
135 |
+
jian j ian
|
136 |
+
jiang j iang
|
137 |
+
jiao j iao
|
138 |
+
jie j ie
|
139 |
+
jin j in
|
140 |
+
jing j ing
|
141 |
+
jiong j iong
|
142 |
+
jiu j iu
|
143 |
+
ju j v
|
144 |
+
jv j v
|
145 |
+
juan j van
|
146 |
+
jvan j van
|
147 |
+
jue j ve
|
148 |
+
jve j ve
|
149 |
+
jun j vn
|
150 |
+
jvn j vn
|
151 |
+
ka k a
|
152 |
+
kai k ai
|
153 |
+
kan k an
|
154 |
+
kang k ang
|
155 |
+
kao k ao
|
156 |
+
ke k e
|
157 |
+
kei k ei
|
158 |
+
ken k en
|
159 |
+
keng k eng
|
160 |
+
kong k ong
|
161 |
+
kou k ou
|
162 |
+
ku k u
|
163 |
+
kua k ua
|
164 |
+
kuai k uai
|
165 |
+
kuan k uan
|
166 |
+
kuang k uang
|
167 |
+
kui k ui
|
168 |
+
kun k un
|
169 |
+
kuo k uo
|
170 |
+
la l a
|
171 |
+
lai l ai
|
172 |
+
lan l an
|
173 |
+
lang l ang
|
174 |
+
lao l ao
|
175 |
+
le l e
|
176 |
+
lei l ei
|
177 |
+
leng l eng
|
178 |
+
li l i
|
179 |
+
lia l ia
|
180 |
+
lian l ian
|
181 |
+
liang l iang
|
182 |
+
liao l iao
|
183 |
+
lie l ie
|
184 |
+
lin l in
|
185 |
+
ling l ing
|
186 |
+
liu l iu
|
187 |
+
lo l o
|
188 |
+
long l ong
|
189 |
+
lou l ou
|
190 |
+
lu l u
|
191 |
+
luan l uan
|
192 |
+
lun l un
|
193 |
+
luo l uo
|
194 |
+
lv l v
|
195 |
+
lve l ve
|
196 |
+
ma m a
|
197 |
+
mai m ai
|
198 |
+
man m an
|
199 |
+
mang m ang
|
200 |
+
mao m ao
|
201 |
+
me m e
|
202 |
+
mei m ei
|
203 |
+
men m en
|
204 |
+
meng m eng
|
205 |
+
mi m i
|
206 |
+
mian m ian
|
207 |
+
miao m iao
|
208 |
+
mie m ie
|
209 |
+
min m in
|
210 |
+
ming m ing
|
211 |
+
miu m iu
|
212 |
+
mo m o
|
213 |
+
mou m ou
|
214 |
+
mu m u
|
215 |
+
na n a
|
216 |
+
nai n ai
|
217 |
+
nan n an
|
218 |
+
nang n ang
|
219 |
+
nao n ao
|
220 |
+
ne n e
|
221 |
+
nei n ei
|
222 |
+
nen n en
|
223 |
+
neng n eng
|
224 |
+
ni n i
|
225 |
+
nian n ian
|
226 |
+
niang n iang
|
227 |
+
niao n iao
|
228 |
+
nie n ie
|
229 |
+
nin n in
|
230 |
+
ning n ing
|
231 |
+
niu n iu
|
232 |
+
nong n ong
|
233 |
+
nou n ou
|
234 |
+
nu n u
|
235 |
+
nuan n uan
|
236 |
+
nun n un
|
237 |
+
nuo n uo
|
238 |
+
nv n v
|
239 |
+
nve n ve
|
240 |
+
o OO o
|
241 |
+
ou OO ou
|
242 |
+
pa p a
|
243 |
+
pai p ai
|
244 |
+
pan p an
|
245 |
+
pang p ang
|
246 |
+
pao p ao
|
247 |
+
pei p ei
|
248 |
+
pen p en
|
249 |
+
peng p eng
|
250 |
+
pi p i
|
251 |
+
pian p ian
|
252 |
+
piao p iao
|
253 |
+
pie p ie
|
254 |
+
pin p in
|
255 |
+
ping p ing
|
256 |
+
po p o
|
257 |
+
pou p ou
|
258 |
+
pu p u
|
259 |
+
qi q i
|
260 |
+
qia q ia
|
261 |
+
qian q ian
|
262 |
+
qiang q iang
|
263 |
+
qiao q iao
|
264 |
+
qie q ie
|
265 |
+
qin q in
|
266 |
+
qing q ing
|
267 |
+
qiong q iong
|
268 |
+
qiu q iu
|
269 |
+
qu q v
|
270 |
+
qv q v
|
271 |
+
quan q van
|
272 |
+
qvan q van
|
273 |
+
que q ve
|
274 |
+
qve q ve
|
275 |
+
qun q vn
|
276 |
+
qvn q vn
|
277 |
+
ran r an
|
278 |
+
rang r ang
|
279 |
+
rao r ao
|
280 |
+
re r e
|
281 |
+
ren r en
|
282 |
+
reng r eng
|
283 |
+
ri r ir
|
284 |
+
rong r ong
|
285 |
+
rou r ou
|
286 |
+
ru r u
|
287 |
+
rua r ua
|
288 |
+
ruan r uan
|
289 |
+
rui r ui
|
290 |
+
run r un
|
291 |
+
ruo r uo
|
292 |
+
sa s a
|
293 |
+
sai s ai
|
294 |
+
san s an
|
295 |
+
sang s ang
|
296 |
+
sao s ao
|
297 |
+
se s e
|
298 |
+
sen s en
|
299 |
+
seng s eng
|
300 |
+
sha sh a
|
301 |
+
shai sh ai
|
302 |
+
shan sh an
|
303 |
+
shang sh ang
|
304 |
+
shao sh ao
|
305 |
+
she sh e
|
306 |
+
shei sh ei
|
307 |
+
shen sh en
|
308 |
+
sheng sh eng
|
309 |
+
shi sh ir
|
310 |
+
shou sh ou
|
311 |
+
shu sh u
|
312 |
+
shua sh ua
|
313 |
+
shuai sh uai
|
314 |
+
shuan sh uan
|
315 |
+
shuang sh uang
|
316 |
+
shui sh ui
|
317 |
+
shun sh un
|
318 |
+
shuo sh uo
|
319 |
+
si s i0
|
320 |
+
song s ong
|
321 |
+
sou s ou
|
322 |
+
su s u
|
323 |
+
suan s uan
|
324 |
+
sui s ui
|
325 |
+
sun s un
|
326 |
+
suo s uo
|
327 |
+
ta t a
|
328 |
+
tai t ai
|
329 |
+
tan t an
|
330 |
+
tang t ang
|
331 |
+
tao t ao
|
332 |
+
te t e
|
333 |
+
tei t ei
|
334 |
+
teng t eng
|
335 |
+
ti t i
|
336 |
+
tian t ian
|
337 |
+
tiao t iao
|
338 |
+
tie t ie
|
339 |
+
ting t ing
|
340 |
+
tong t ong
|
341 |
+
tou t ou
|
342 |
+
tu t u
|
343 |
+
tuan t uan
|
344 |
+
tui t ui
|
345 |
+
tun t un
|
346 |
+
tuo t uo
|
347 |
+
wa w a
|
348 |
+
wai w ai
|
349 |
+
wan w an
|
350 |
+
wang w ang
|
351 |
+
wei w ei
|
352 |
+
wen w en
|
353 |
+
weng w eng
|
354 |
+
wo w o
|
355 |
+
wu w u
|
356 |
+
xi x i
|
357 |
+
xia x ia
|
358 |
+
xian x ian
|
359 |
+
xiang x iang
|
360 |
+
xiao x iao
|
361 |
+
xie x ie
|
362 |
+
xin x in
|
363 |
+
xing x ing
|
364 |
+
xiong x iong
|
365 |
+
xiu x iu
|
366 |
+
xu x v
|
367 |
+
xv x v
|
368 |
+
xuan x van
|
369 |
+
xvan x van
|
370 |
+
xue x ve
|
371 |
+
xve x ve
|
372 |
+
xun x vn
|
373 |
+
xvn x vn
|
374 |
+
ya y a
|
375 |
+
yan y En
|
376 |
+
yang y ang
|
377 |
+
yao y ao
|
378 |
+
ye y E
|
379 |
+
yi y i
|
380 |
+
yin y in
|
381 |
+
ying y ing
|
382 |
+
yo y o
|
383 |
+
yong y ong
|
384 |
+
you y ou
|
385 |
+
yu y v
|
386 |
+
yv y v
|
387 |
+
yuan y van
|
388 |
+
yvan y van
|
389 |
+
yue y ve
|
390 |
+
yve y ve
|
391 |
+
yun y vn
|
392 |
+
yvn y vn
|
393 |
+
za z a
|
394 |
+
zai z ai
|
395 |
+
zan z an
|
396 |
+
zang z ang
|
397 |
+
zao z ao
|
398 |
+
ze z e
|
399 |
+
zei z ei
|
400 |
+
zen z en
|
401 |
+
zeng z eng
|
402 |
+
zha zh a
|
403 |
+
zhai zh ai
|
404 |
+
zhan zh an
|
405 |
+
zhang zh ang
|
406 |
+
zhao zh ao
|
407 |
+
zhe zh e
|
408 |
+
zhei zh ei
|
409 |
+
zhen zh en
|
410 |
+
zheng zh eng
|
411 |
+
zhi zh ir
|
412 |
+
zhong zh ong
|
413 |
+
zhou zh ou
|
414 |
+
zhu zh u
|
415 |
+
zhua zh ua
|
416 |
+
zhuai zh uai
|
417 |
+
zhuan zh uan
|
418 |
+
zhuang zh uang
|
419 |
+
zhui zh ui
|
420 |
+
zhun zh un
|
421 |
+
zhuo zh uo
|
422 |
+
zi z i0
|
423 |
+
zong z ong
|
424 |
+
zou z ou
|
425 |
+
zu z u
|
426 |
+
zuan z uan
|
427 |
+
zui z ui
|
428 |
+
zun z un
|
429 |
+
zuo z uo
|
text/symbols.py
ADDED
@@ -0,0 +1,187 @@
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|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
punctuation = ["!", "?", "…", ",", ".", "'", "-"]
|
2 |
+
pu_symbols = punctuation + ["SP", "UNK"]
|
3 |
+
pad = "_"
|
4 |
+
|
5 |
+
# chinese
|
6 |
+
zh_symbols = [
|
7 |
+
"E",
|
8 |
+
"En",
|
9 |
+
"a",
|
10 |
+
"ai",
|
11 |
+
"an",
|
12 |
+
"ang",
|
13 |
+
"ao",
|
14 |
+
"b",
|
15 |
+
"c",
|
16 |
+
"ch",
|
17 |
+
"d",
|
18 |
+
"e",
|
19 |
+
"ei",
|
20 |
+
"en",
|
21 |
+
"eng",
|
22 |
+
"er",
|
23 |
+
"f",
|
24 |
+
"g",
|
25 |
+
"h",
|
26 |
+
"i",
|
27 |
+
"i0",
|
28 |
+
"ia",
|
29 |
+
"ian",
|
30 |
+
"iang",
|
31 |
+
"iao",
|
32 |
+
"ie",
|
33 |
+
"in",
|
34 |
+
"ing",
|
35 |
+
"iong",
|
36 |
+
"ir",
|
37 |
+
"iu",
|
38 |
+
"j",
|
39 |
+
"k",
|
40 |
+
"l",
|
41 |
+
"m",
|
42 |
+
"n",
|
43 |
+
"o",
|
44 |
+
"ong",
|
45 |
+
"ou",
|
46 |
+
"p",
|
47 |
+
"q",
|
48 |
+
"r",
|
49 |
+
"s",
|
50 |
+
"sh",
|
51 |
+
"t",
|
52 |
+
"u",
|
53 |
+
"ua",
|
54 |
+
"uai",
|
55 |
+
"uan",
|
56 |
+
"uang",
|
57 |
+
"ui",
|
58 |
+
"un",
|
59 |
+
"uo",
|
60 |
+
"v",
|
61 |
+
"van",
|
62 |
+
"ve",
|
63 |
+
"vn",
|
64 |
+
"w",
|
65 |
+
"x",
|
66 |
+
"y",
|
67 |
+
"z",
|
68 |
+
"zh",
|
69 |
+
"AA",
|
70 |
+
"EE",
|
71 |
+
"OO",
|
72 |
+
]
|
73 |
+
num_zh_tones = 6
|
74 |
+
|
75 |
+
# japanese
|
76 |
+
ja_symbols = [
|
77 |
+
"N",
|
78 |
+
"a",
|
79 |
+
"a:",
|
80 |
+
"b",
|
81 |
+
"by",
|
82 |
+
"ch",
|
83 |
+
"d",
|
84 |
+
"dy",
|
85 |
+
"e",
|
86 |
+
"e:",
|
87 |
+
"f",
|
88 |
+
"g",
|
89 |
+
"gy",
|
90 |
+
"h",
|
91 |
+
"hy",
|
92 |
+
"i",
|
93 |
+
"i:",
|
94 |
+
"j",
|
95 |
+
"k",
|
96 |
+
"ky",
|
97 |
+
"m",
|
98 |
+
"my",
|
99 |
+
"n",
|
100 |
+
"ny",
|
101 |
+
"o",
|
102 |
+
"o:",
|
103 |
+
"p",
|
104 |
+
"py",
|
105 |
+
"q",
|
106 |
+
"r",
|
107 |
+
"ry",
|
108 |
+
"s",
|
109 |
+
"sh",
|
110 |
+
"t",
|
111 |
+
"ts",
|
112 |
+
"ty",
|
113 |
+
"u",
|
114 |
+
"u:",
|
115 |
+
"w",
|
116 |
+
"y",
|
117 |
+
"z",
|
118 |
+
"zy",
|
119 |
+
]
|
120 |
+
num_ja_tones = 2
|
121 |
+
|
122 |
+
# English
|
123 |
+
en_symbols = [
|
124 |
+
"aa",
|
125 |
+
"ae",
|
126 |
+
"ah",
|
127 |
+
"ao",
|
128 |
+
"aw",
|
129 |
+
"ay",
|
130 |
+
"b",
|
131 |
+
"ch",
|
132 |
+
"d",
|
133 |
+
"dh",
|
134 |
+
"eh",
|
135 |
+
"er",
|
136 |
+
"ey",
|
137 |
+
"f",
|
138 |
+
"g",
|
139 |
+
"hh",
|
140 |
+
"ih",
|
141 |
+
"iy",
|
142 |
+
"jh",
|
143 |
+
"k",
|
144 |
+
"l",
|
145 |
+
"m",
|
146 |
+
"n",
|
147 |
+
"ng",
|
148 |
+
"ow",
|
149 |
+
"oy",
|
150 |
+
"p",
|
151 |
+
"r",
|
152 |
+
"s",
|
153 |
+
"sh",
|
154 |
+
"t",
|
155 |
+
"th",
|
156 |
+
"uh",
|
157 |
+
"uw",
|
158 |
+
"V",
|
159 |
+
"w",
|
160 |
+
"y",
|
161 |
+
"z",
|
162 |
+
"zh",
|
163 |
+
]
|
164 |
+
num_en_tones = 4
|
165 |
+
|
166 |
+
# combine all symbols
|
167 |
+
normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
|
168 |
+
symbols = [pad] + normal_symbols + pu_symbols
|
169 |
+
sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
|
170 |
+
|
171 |
+
# combine all tones
|
172 |
+
num_tones = num_zh_tones + num_ja_tones + num_en_tones
|
173 |
+
|
174 |
+
# language maps
|
175 |
+
language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
|
176 |
+
num_languages = len(language_id_map.keys())
|
177 |
+
|
178 |
+
language_tone_start_map = {
|
179 |
+
"ZH": 0,
|
180 |
+
"JP": num_zh_tones,
|
181 |
+
"EN": num_zh_tones + num_ja_tones,
|
182 |
+
}
|
183 |
+
|
184 |
+
if __name__ == "__main__":
|
185 |
+
a = set(zh_symbols)
|
186 |
+
b = set(en_symbols)
|
187 |
+
print(sorted(a & b))
|
text/tone_sandhi.py
ADDED
@@ -0,0 +1,769 @@
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|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
from typing import List
|
15 |
+
from typing import Tuple
|
16 |
+
|
17 |
+
import jieba
|
18 |
+
from pypinyin import lazy_pinyin
|
19 |
+
from pypinyin import Style
|
20 |
+
|
21 |
+
|
22 |
+
class ToneSandhi:
|
23 |
+
def __init__(self):
|
24 |
+
self.must_neural_tone_words = {
|
25 |
+
"麻烦",
|
26 |
+
"麻利",
|
27 |
+
"鸳鸯",
|
28 |
+
"高粱",
|
29 |
+
"骨头",
|
30 |
+
"骆驼",
|
31 |
+
"马虎",
|
32 |
+
"首饰",
|
33 |
+
"馒头",
|
34 |
+
"馄饨",
|
35 |
+
"风筝",
|
36 |
+
"难为",
|
37 |
+
"队伍",
|
38 |
+
"阔气",
|
39 |
+
"闺女",
|
40 |
+
"门道",
|
41 |
+
"锄头",
|
42 |
+
"铺盖",
|
43 |
+
"铃铛",
|
44 |
+
"铁匠",
|
45 |
+
"钥匙",
|
46 |
+
"里脊",
|
47 |
+
"里头",
|
48 |
+
"部分",
|
49 |
+
"那么",
|
50 |
+
"道士",
|
51 |
+
"造化",
|
52 |
+
"迷糊",
|
53 |
+
"连累",
|
54 |
+
"这么",
|
55 |
+
"这个",
|
56 |
+
"运气",
|
57 |
+
"过去",
|
58 |
+
"软和",
|
59 |
+
"转悠",
|
60 |
+
"踏实",
|
61 |
+
"跳蚤",
|
62 |
+
"跟头",
|
63 |
+
"趔趄",
|
64 |
+
"财主",
|
65 |
+
"豆腐",
|
66 |
+
"讲究",
|
67 |
+
"记性",
|
68 |
+
"记号",
|
69 |
+
"认识",
|
70 |
+
"规矩",
|
71 |
+
"见识",
|
72 |
+
"裁缝",
|
73 |
+
"补丁",
|
74 |
+
"衣裳",
|
75 |
+
"衣服",
|
76 |
+
"衙门",
|
77 |
+
"街坊",
|
78 |
+
"行李",
|
79 |
+
"行当",
|
80 |
+
"蛤蟆",
|
81 |
+
"蘑菇",
|
82 |
+
"薄荷",
|
83 |
+
"葫芦",
|
84 |
+
"葡萄",
|
85 |
+
"萝卜",
|
86 |
+
"荸荠",
|
87 |
+
"苗条",
|
88 |
+
"苗头",
|
89 |
+
"苍蝇",
|
90 |
+
"芝麻",
|
91 |
+
"舒服",
|
92 |
+
"舒坦",
|
93 |
+
"舌头",
|
94 |
+
"自在",
|
95 |
+
"膏药",
|
96 |
+
"脾气",
|
97 |
+
"脑袋",
|
98 |
+
"脊梁",
|
99 |
+
"能耐",
|
100 |
+
"胳膊",
|
101 |
+
"胭脂",
|
102 |
+
"胡萝",
|
103 |
+
"胡琴",
|
104 |
+
"胡同",
|
105 |
+
"聪明",
|
106 |
+
"耽误",
|
107 |
+
"耽搁",
|
108 |
+
"耷拉",
|
109 |
+
"耳朵",
|
110 |
+
"老爷",
|
111 |
+
"老实",
|
112 |
+
"老婆",
|
113 |
+
"老头",
|
114 |
+
"老太",
|
115 |
+
"翻腾",
|
116 |
+
"罗嗦",
|
117 |
+
"罐头",
|
118 |
+
"编辑",
|
119 |
+
"结实",
|
120 |
+
"红火",
|
121 |
+
"累赘",
|
122 |
+
"糨糊",
|
123 |
+
"糊涂",
|
124 |
+
"精神",
|
125 |
+
"粮食",
|
126 |
+
"簸箕",
|
127 |
+
"篱笆",
|
128 |
+
"算计",
|
129 |
+
"算盘",
|
130 |
+
"答应",
|
131 |
+
"笤帚",
|
132 |
+
"笑语",
|
133 |
+
"笑话",
|
134 |
+
"窟窿",
|
135 |
+
"窝囊",
|
136 |
+
"窗户",
|
137 |
+
"稳当",
|
138 |
+
"稀罕",
|
139 |
+
"称呼",
|
140 |
+
"秧歌",
|
141 |
+
"秀气",
|
142 |
+
"秀才",
|
143 |
+
"福气",
|
144 |
+
"祖宗",
|
145 |
+
"砚台",
|
146 |
+
"码头",
|
147 |
+
"石榴",
|
148 |
+
"石头",
|
149 |
+
"石匠",
|
150 |
+
"知识",
|
151 |
+
"眼睛",
|
152 |
+
"眯缝",
|
153 |
+
"眨巴",
|
154 |
+
"眉毛",
|
155 |
+
"相声",
|
156 |
+
"盘算",
|
157 |
+
"白净",
|
158 |
+
"痢疾",
|
159 |
+
"痛快",
|
160 |
+
"疟疾",
|
161 |
+
"疙瘩",
|
162 |
+
"疏忽",
|
163 |
+
"畜生",
|
164 |
+
"生意",
|
165 |
+
"甘蔗",
|
166 |
+
"琵琶",
|
167 |
+
"琢磨",
|
168 |
+
"琉璃",
|
169 |
+
"玻璃",
|
170 |
+
"玫瑰",
|
171 |
+
"玄乎",
|
172 |
+
"狐狸",
|
173 |
+
"状元",
|
174 |
+
"特务",
|
175 |
+
"牲口",
|
176 |
+
"牙碜",
|
177 |
+
"牌楼",
|
178 |
+
"爽快",
|
179 |
+
"爱人",
|
180 |
+
"热闹",
|
181 |
+
"烧饼",
|
182 |
+
"烟筒",
|
183 |
+
"烂糊",
|
184 |
+
"点心",
|
185 |
+
"炊帚",
|
186 |
+
"灯笼",
|
187 |
+
"火候",
|
188 |
+
"漂亮",
|
189 |
+
"滑溜",
|
190 |
+
"溜达",
|
191 |
+
"温和",
|
192 |
+
"清楚",
|
193 |
+
"消息",
|
194 |
+
"浪头",
|
195 |
+
"活泼",
|
196 |
+
"比方",
|
197 |
+
"正经",
|
198 |
+
"欺负",
|
199 |
+
"模糊",
|
200 |
+
"槟榔",
|
201 |
+
"棺材",
|
202 |
+
"棒槌",
|
203 |
+
"棉花",
|
204 |
+
"核桃",
|
205 |
+
"栅栏",
|
206 |
+
"柴火",
|
207 |
+
"架势",
|
208 |
+
"枕头",
|
209 |
+
"枇杷",
|
210 |
+
"机灵",
|
211 |
+
"本事",
|
212 |
+
"木头",
|
213 |
+
"木匠",
|
214 |
+
"朋友",
|
215 |
+
"月饼",
|
216 |
+
"月亮",
|
217 |
+
"暖和",
|
218 |
+
"明白",
|
219 |
+
"时候",
|
220 |
+
"新鲜",
|
221 |
+
"故事",
|
222 |
+
"收拾",
|
223 |
+
"收成",
|
224 |
+
"提防",
|
225 |
+
"挖苦",
|
226 |
+
"挑剔",
|
227 |
+
"指甲",
|
228 |
+
"指头",
|
229 |
+
"拾掇",
|
230 |
+
"拳头",
|
231 |
+
"拨弄",
|
232 |
+
"招牌",
|
233 |
+
"招呼",
|
234 |
+
"抬举",
|
235 |
+
"护士",
|
236 |
+
"折腾",
|
237 |
+
"扫帚",
|
238 |
+
"打量",
|
239 |
+
"打算",
|
240 |
+
"打点",
|
241 |
+
"打扮",
|
242 |
+
"打听",
|
243 |
+
"打发",
|
244 |
+
"扎实",
|
245 |
+
"扁担",
|
246 |
+
"戒指",
|
247 |
+
"懒得",
|
248 |
+
"意识",
|
249 |
+
"意思",
|
250 |
+
"情形",
|
251 |
+
"悟性",
|
252 |
+
"怪物",
|
253 |
+
"思量",
|
254 |
+
"怎么",
|
255 |
+
"念头",
|
256 |
+
"念叨",
|
257 |
+
"快活",
|
258 |
+
"忙活",
|
259 |
+
"志气",
|
260 |
+
"心思",
|
261 |
+
"得罪",
|
262 |
+
"张罗",
|
263 |
+
"弟兄",
|
264 |
+
"开通",
|
265 |
+
"应酬",
|
266 |
+
"庄稼",
|
267 |
+
"干事",
|
268 |
+
"帮手",
|
269 |
+
"帐篷",
|
270 |
+
"希罕",
|
271 |
+
"师父",
|
272 |
+
"师傅",
|
273 |
+
"巴结",
|
274 |
+
"巴掌",
|
275 |
+
"差事",
|
276 |
+
"工夫",
|
277 |
+
"岁数",
|
278 |
+
"屁股",
|
279 |
+
"尾巴",
|
280 |
+
"少爷",
|
281 |
+
"小气",
|
282 |
+
"小伙",
|
283 |
+
"将就",
|
284 |
+
"对头",
|
285 |
+
"对付",
|
286 |
+
"寡妇",
|
287 |
+
"家伙",
|
288 |
+
"客气",
|
289 |
+
"实在",
|
290 |
+
"官司",
|
291 |
+
"学问",
|
292 |
+
"学生",
|
293 |
+
"字号",
|
294 |
+
"嫁妆",
|
295 |
+
"媳妇",
|
296 |
+
"媒人",
|
297 |
+
"婆家",
|
298 |
+
"娘家",
|
299 |
+
"委屈",
|
300 |
+
"姑娘",
|
301 |
+
"姐夫",
|
302 |
+
"妯娌",
|
303 |
+
"妥当",
|
304 |
+
"妖精",
|
305 |
+
"奴才",
|
306 |
+
"女婿",
|
307 |
+
"头发",
|
308 |
+
"太阳",
|
309 |
+
"大爷",
|
310 |
+
"大方",
|
311 |
+
"大意",
|
312 |
+
"大夫",
|
313 |
+
"多少",
|
314 |
+
"多么",
|
315 |
+
"外甥",
|
316 |
+
"壮实",
|
317 |
+
"地道",
|
318 |
+
"地方",
|
319 |
+
"在乎",
|
320 |
+
"困难",
|
321 |
+
"嘴巴",
|
322 |
+
"嘱咐",
|
323 |
+
"嘟囔",
|
324 |
+
"嘀咕",
|
325 |
+
"喜欢",
|
326 |
+
"喇嘛",
|
327 |
+
"喇叭",
|
328 |
+
"商量",
|
329 |
+
"唾沫",
|
330 |
+
"哑巴",
|
331 |
+
"哈欠",
|
332 |
+
"哆嗦",
|
333 |
+
"咳嗽",
|
334 |
+
"和尚",
|
335 |
+
"告诉",
|
336 |
+
"告示",
|
337 |
+
"含糊",
|
338 |
+
"吓唬",
|
339 |
+
"后头",
|
340 |
+
"名字",
|
341 |
+
"名堂",
|
342 |
+
"合同",
|
343 |
+
"吆喝",
|
344 |
+
"叫唤",
|
345 |
+
"口袋",
|
346 |
+
"厚道",
|
347 |
+
"厉害",
|
348 |
+
"千斤",
|
349 |
+
"包袱",
|
350 |
+
"包涵",
|
351 |
+
"匀称",
|
352 |
+
"勤快",
|
353 |
+
"动静",
|
354 |
+
"动弹",
|
355 |
+
"功夫",
|
356 |
+
"力气",
|
357 |
+
"前头",
|
358 |
+
"刺猬",
|
359 |
+
"刺激",
|
360 |
+
"别扭",
|
361 |
+
"利落",
|
362 |
+
"利索",
|
363 |
+
"利害",
|
364 |
+
"分析",
|
365 |
+
"出息",
|
366 |
+
"凑合",
|
367 |
+
"凉快",
|
368 |
+
"冷战",
|
369 |
+
"冤枉",
|
370 |
+
"冒失",
|
371 |
+
"养活",
|
372 |
+
"关系",
|
373 |
+
"先生",
|
374 |
+
"兄弟",
|
375 |
+
"便宜",
|
376 |
+
"使唤",
|
377 |
+
"佩服",
|
378 |
+
"作坊",
|
379 |
+
"体面",
|
380 |
+
"位置",
|
381 |
+
"似的",
|
382 |
+
"伙计",
|
383 |
+
"休息",
|
384 |
+
"什么",
|
385 |
+
"人家",
|
386 |
+
"亲戚",
|
387 |
+
"亲家",
|
388 |
+
"交情",
|
389 |
+
"云彩",
|
390 |
+
"事情",
|
391 |
+
"买卖",
|
392 |
+
"主意",
|
393 |
+
"丫头",
|
394 |
+
"丧气",
|
395 |
+
"两口",
|
396 |
+
"东西",
|
397 |
+
"东家",
|
398 |
+
"世故",
|
399 |
+
"不由",
|
400 |
+
"不在",
|
401 |
+
"下水",
|
402 |
+
"下巴",
|
403 |
+
"上头",
|
404 |
+
"上司",
|
405 |
+
"丈夫",
|
406 |
+
"丈人",
|
407 |
+
"一辈",
|
408 |
+
"那个",
|
409 |
+
"菩萨",
|
410 |
+
"父亲",
|
411 |
+
"母亲",
|
412 |
+
"咕噜",
|
413 |
+
"邋遢",
|
414 |
+
"费用",
|
415 |
+
"冤家",
|
416 |
+
"甜头",
|
417 |
+
"介绍",
|
418 |
+
"荒唐",
|
419 |
+
"大人",
|
420 |
+
"泥鳅",
|
421 |
+
"幸福",
|
422 |
+
"熟悉",
|
423 |
+
"计划",
|
424 |
+
"扑腾",
|
425 |
+
"蜡烛",
|
426 |
+
"姥爷",
|
427 |
+
"照顾",
|
428 |
+
"喉咙",
|
429 |
+
"吉他",
|
430 |
+
"弄堂",
|
431 |
+
"蚂蚱",
|
432 |
+
"凤凰",
|
433 |
+
"拖沓",
|
434 |
+
"寒碜",
|
435 |
+
"糟蹋",
|
436 |
+
"倒腾",
|
437 |
+
"报复",
|
438 |
+
"逻辑",
|
439 |
+
"盘缠",
|
440 |
+
"喽啰",
|
441 |
+
"牢骚",
|
442 |
+
"咖喱",
|
443 |
+
"扫把",
|
444 |
+
"惦记",
|
445 |
+
}
|
446 |
+
self.must_not_neural_tone_words = {
|
447 |
+
"男子",
|
448 |
+
"女子",
|
449 |
+
"分子",
|
450 |
+
"原子",
|
451 |
+
"量子",
|
452 |
+
"莲子",
|
453 |
+
"石子",
|
454 |
+
"瓜子",
|
455 |
+
"电子",
|
456 |
+
"人人",
|
457 |
+
"虎虎",
|
458 |
+
}
|
459 |
+
self.punc = ":,;。?!“”‘’':,;.?!"
|
460 |
+
|
461 |
+
# the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
|
462 |
+
# e.g.
|
463 |
+
# word: "家里"
|
464 |
+
# pos: "s"
|
465 |
+
# finals: ['ia1', 'i3']
|
466 |
+
def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
|
467 |
+
# reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
|
468 |
+
for j, item in enumerate(word):
|
469 |
+
if (
|
470 |
+
j - 1 >= 0
|
471 |
+
and item == word[j - 1]
|
472 |
+
and pos[0] in {"n", "v", "a"}
|
473 |
+
and word not in self.must_not_neural_tone_words
|
474 |
+
):
|
475 |
+
finals[j] = finals[j][:-1] + "5"
|
476 |
+
ge_idx = word.find("个")
|
477 |
+
if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
|
478 |
+
finals[-1] = finals[-1][:-1] + "5"
|
479 |
+
elif len(word) >= 1 and word[-1] in "的地得":
|
480 |
+
finals[-1] = finals[-1][:-1] + "5"
|
481 |
+
# e.g. 走了, 看着, 去过
|
482 |
+
# elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
|
483 |
+
# finals[-1] = finals[-1][:-1] + "5"
|
484 |
+
elif (
|
485 |
+
len(word) > 1
|
486 |
+
and word[-1] in "们子"
|
487 |
+
and pos in {"r", "n"}
|
488 |
+
and word not in self.must_not_neural_tone_words
|
489 |
+
):
|
490 |
+
finals[-1] = finals[-1][:-1] + "5"
|
491 |
+
# e.g. 桌上, 地下, 家里
|
492 |
+
elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
|
493 |
+
finals[-1] = finals[-1][:-1] + "5"
|
494 |
+
# e.g. 上来, 下去
|
495 |
+
elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
|
496 |
+
finals[-1] = finals[-1][:-1] + "5"
|
497 |
+
# 个做量词
|
498 |
+
elif (
|
499 |
+
ge_idx >= 1
|
500 |
+
and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
|
501 |
+
) or word == "个":
|
502 |
+
finals[ge_idx] = finals[ge_idx][:-1] + "5"
|
503 |
+
else:
|
504 |
+
if (
|
505 |
+
word in self.must_neural_tone_words
|
506 |
+
or word[-2:] in self.must_neural_tone_words
|
507 |
+
):
|
508 |
+
finals[-1] = finals[-1][:-1] + "5"
|
509 |
+
|
510 |
+
word_list = self._split_word(word)
|
511 |
+
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
|
512 |
+
for i, word in enumerate(word_list):
|
513 |
+
# conventional neural in Chinese
|
514 |
+
if (
|
515 |
+
word in self.must_neural_tone_words
|
516 |
+
or word[-2:] in self.must_neural_tone_words
|
517 |
+
):
|
518 |
+
finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
|
519 |
+
finals = sum(finals_list, [])
|
520 |
+
return finals
|
521 |
+
|
522 |
+
def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
523 |
+
# e.g. 看不懂
|
524 |
+
if len(word) == 3 and word[1] == "不":
|
525 |
+
finals[1] = finals[1][:-1] + "5"
|
526 |
+
else:
|
527 |
+
for i, char in enumerate(word):
|
528 |
+
# "不" before tone4 should be bu2, e.g. 不怕
|
529 |
+
if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
|
530 |
+
finals[i] = finals[i][:-1] + "2"
|
531 |
+
return finals
|
532 |
+
|
533 |
+
def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
534 |
+
# "一" in number sequences, e.g. 一零零, 二一零
|
535 |
+
if word.find("一") != -1 and all(
|
536 |
+
[item.isnumeric() for item in word if item != "一"]
|
537 |
+
):
|
538 |
+
return finals
|
539 |
+
# "一" between reduplication words should be yi5, e.g. 看一看
|
540 |
+
elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
|
541 |
+
finals[1] = finals[1][:-1] + "5"
|
542 |
+
# when "一" is ordinal word, it should be yi1
|
543 |
+
elif word.startswith("第一"):
|
544 |
+
finals[1] = finals[1][:-1] + "1"
|
545 |
+
else:
|
546 |
+
for i, char in enumerate(word):
|
547 |
+
if char == "一" and i + 1 < len(word):
|
548 |
+
# "一" before tone4 should be yi2, e.g. 一段
|
549 |
+
if finals[i + 1][-1] == "4":
|
550 |
+
finals[i] = finals[i][:-1] + "2"
|
551 |
+
# "一" before non-tone4 should be yi4, e.g. 一天
|
552 |
+
else:
|
553 |
+
# "一" 后面如果是标点,还读一声
|
554 |
+
if word[i + 1] not in self.punc:
|
555 |
+
finals[i] = finals[i][:-1] + "4"
|
556 |
+
return finals
|
557 |
+
|
558 |
+
def _split_word(self, word: str) -> List[str]:
|
559 |
+
word_list = jieba.cut_for_search(word)
|
560 |
+
word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
|
561 |
+
first_subword = word_list[0]
|
562 |
+
first_begin_idx = word.find(first_subword)
|
563 |
+
if first_begin_idx == 0:
|
564 |
+
second_subword = word[len(first_subword) :]
|
565 |
+
new_word_list = [first_subword, second_subword]
|
566 |
+
else:
|
567 |
+
second_subword = word[: -len(first_subword)]
|
568 |
+
new_word_list = [second_subword, first_subword]
|
569 |
+
return new_word_list
|
570 |
+
|
571 |
+
def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
572 |
+
if len(word) == 2 and self._all_tone_three(finals):
|
573 |
+
finals[0] = finals[0][:-1] + "2"
|
574 |
+
elif len(word) == 3:
|
575 |
+
word_list = self._split_word(word)
|
576 |
+
if self._all_tone_three(finals):
|
577 |
+
# disyllabic + monosyllabic, e.g. 蒙古/包
|
578 |
+
if len(word_list[0]) == 2:
|
579 |
+
finals[0] = finals[0][:-1] + "2"
|
580 |
+
finals[1] = finals[1][:-1] + "2"
|
581 |
+
# monosyllabic + disyllabic, e.g. 纸/老虎
|
582 |
+
elif len(word_list[0]) == 1:
|
583 |
+
finals[1] = finals[1][:-1] + "2"
|
584 |
+
else:
|
585 |
+
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
|
586 |
+
if len(finals_list) == 2:
|
587 |
+
for i, sub in enumerate(finals_list):
|
588 |
+
# e.g. 所有/人
|
589 |
+
if self._all_tone_three(sub) and len(sub) == 2:
|
590 |
+
finals_list[i][0] = finals_list[i][0][:-1] + "2"
|
591 |
+
# e.g. 好/喜欢
|
592 |
+
elif (
|
593 |
+
i == 1
|
594 |
+
and not self._all_tone_three(sub)
|
595 |
+
and finals_list[i][0][-1] == "3"
|
596 |
+
and finals_list[0][-1][-1] == "3"
|
597 |
+
):
|
598 |
+
finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
|
599 |
+
finals = sum(finals_list, [])
|
600 |
+
# split idiom into two words who's length is 2
|
601 |
+
elif len(word) == 4:
|
602 |
+
finals_list = [finals[:2], finals[2:]]
|
603 |
+
finals = []
|
604 |
+
for sub in finals_list:
|
605 |
+
if self._all_tone_three(sub):
|
606 |
+
sub[0] = sub[0][:-1] + "2"
|
607 |
+
finals += sub
|
608 |
+
|
609 |
+
return finals
|
610 |
+
|
611 |
+
def _all_tone_three(self, finals: List[str]) -> bool:
|
612 |
+
return all(x[-1] == "3" for x in finals)
|
613 |
+
|
614 |
+
# merge "不" and the word behind it
|
615 |
+
# if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
|
616 |
+
def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
617 |
+
new_seg = []
|
618 |
+
last_word = ""
|
619 |
+
for word, pos in seg:
|
620 |
+
if last_word == "不":
|
621 |
+
word = last_word + word
|
622 |
+
if word != "不":
|
623 |
+
new_seg.append((word, pos))
|
624 |
+
last_word = word[:]
|
625 |
+
if last_word == "不":
|
626 |
+
new_seg.append((last_word, "d"))
|
627 |
+
last_word = ""
|
628 |
+
return new_seg
|
629 |
+
|
630 |
+
# function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
|
631 |
+
# function 2: merge single "一" and the word behind it
|
632 |
+
# if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
|
633 |
+
# e.g.
|
634 |
+
# input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
|
635 |
+
# output seg: [['听一听', 'v']]
|
636 |
+
def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
637 |
+
new_seg = []
|
638 |
+
# function 1
|
639 |
+
for i, (word, pos) in enumerate(seg):
|
640 |
+
if (
|
641 |
+
i - 1 >= 0
|
642 |
+
and word == "一"
|
643 |
+
and i + 1 < len(seg)
|
644 |
+
and seg[i - 1][0] == seg[i + 1][0]
|
645 |
+
and seg[i - 1][1] == "v"
|
646 |
+
):
|
647 |
+
new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
|
648 |
+
else:
|
649 |
+
if (
|
650 |
+
i - 2 >= 0
|
651 |
+
and seg[i - 1][0] == "一"
|
652 |
+
and seg[i - 2][0] == word
|
653 |
+
and pos == "v"
|
654 |
+
):
|
655 |
+
continue
|
656 |
+
else:
|
657 |
+
new_seg.append([word, pos])
|
658 |
+
seg = new_seg
|
659 |
+
new_seg = []
|
660 |
+
# function 2
|
661 |
+
for i, (word, pos) in enumerate(seg):
|
662 |
+
if new_seg and new_seg[-1][0] == "一":
|
663 |
+
new_seg[-1][0] = new_seg[-1][0] + word
|
664 |
+
else:
|
665 |
+
new_seg.append([word, pos])
|
666 |
+
return new_seg
|
667 |
+
|
668 |
+
# the first and the second words are all_tone_three
|
669 |
+
def _merge_continuous_three_tones(
|
670 |
+
self, seg: List[Tuple[str, str]]
|
671 |
+
) -> List[Tuple[str, str]]:
|
672 |
+
new_seg = []
|
673 |
+
sub_finals_list = [
|
674 |
+
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
675 |
+
for (word, pos) in seg
|
676 |
+
]
|
677 |
+
assert len(sub_finals_list) == len(seg)
|
678 |
+
merge_last = [False] * len(seg)
|
679 |
+
for i, (word, pos) in enumerate(seg):
|
680 |
+
if (
|
681 |
+
i - 1 >= 0
|
682 |
+
and self._all_tone_three(sub_finals_list[i - 1])
|
683 |
+
and self._all_tone_three(sub_finals_list[i])
|
684 |
+
and not merge_last[i - 1]
|
685 |
+
):
|
686 |
+
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
687 |
+
if (
|
688 |
+
not self._is_reduplication(seg[i - 1][0])
|
689 |
+
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
|
690 |
+
):
|
691 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
692 |
+
merge_last[i] = True
|
693 |
+
else:
|
694 |
+
new_seg.append([word, pos])
|
695 |
+
else:
|
696 |
+
new_seg.append([word, pos])
|
697 |
+
|
698 |
+
return new_seg
|
699 |
+
|
700 |
+
def _is_reduplication(self, word: str) -> bool:
|
701 |
+
return len(word) == 2 and word[0] == word[1]
|
702 |
+
|
703 |
+
# the last char of first word and the first char of second word is tone_three
|
704 |
+
def _merge_continuous_three_tones_2(
|
705 |
+
self, seg: List[Tuple[str, str]]
|
706 |
+
) -> List[Tuple[str, str]]:
|
707 |
+
new_seg = []
|
708 |
+
sub_finals_list = [
|
709 |
+
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
710 |
+
for (word, pos) in seg
|
711 |
+
]
|
712 |
+
assert len(sub_finals_list) == len(seg)
|
713 |
+
merge_last = [False] * len(seg)
|
714 |
+
for i, (word, pos) in enumerate(seg):
|
715 |
+
if (
|
716 |
+
i - 1 >= 0
|
717 |
+
and sub_finals_list[i - 1][-1][-1] == "3"
|
718 |
+
and sub_finals_list[i][0][-1] == "3"
|
719 |
+
and not merge_last[i - 1]
|
720 |
+
):
|
721 |
+
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
722 |
+
if (
|
723 |
+
not self._is_reduplication(seg[i - 1][0])
|
724 |
+
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
|
725 |
+
):
|
726 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
727 |
+
merge_last[i] = True
|
728 |
+
else:
|
729 |
+
new_seg.append([word, pos])
|
730 |
+
else:
|
731 |
+
new_seg.append([word, pos])
|
732 |
+
return new_seg
|
733 |
+
|
734 |
+
def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
735 |
+
new_seg = []
|
736 |
+
for i, (word, pos) in enumerate(seg):
|
737 |
+
if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
|
738 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
739 |
+
else:
|
740 |
+
new_seg.append([word, pos])
|
741 |
+
return new_seg
|
742 |
+
|
743 |
+
def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
744 |
+
new_seg = []
|
745 |
+
for i, (word, pos) in enumerate(seg):
|
746 |
+
if new_seg and word == new_seg[-1][0]:
|
747 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
748 |
+
else:
|
749 |
+
new_seg.append([word, pos])
|
750 |
+
return new_seg
|
751 |
+
|
752 |
+
def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
753 |
+
seg = self._merge_bu(seg)
|
754 |
+
try:
|
755 |
+
seg = self._merge_yi(seg)
|
756 |
+
except:
|
757 |
+
print("_merge_yi failed")
|
758 |
+
seg = self._merge_reduplication(seg)
|
759 |
+
seg = self._merge_continuous_three_tones(seg)
|
760 |
+
seg = self._merge_continuous_three_tones_2(seg)
|
761 |
+
seg = self._merge_er(seg)
|
762 |
+
return seg
|
763 |
+
|
764 |
+
def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
|
765 |
+
finals = self._bu_sandhi(word, finals)
|
766 |
+
finals = self._yi_sandhi(word, finals)
|
767 |
+
finals = self._neural_sandhi(word, pos, finals)
|
768 |
+
finals = self._three_sandhi(word, finals)
|
769 |
+
return finals
|