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293 lines
12 KiB
293 lines
12 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from typing import Dict
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from typing import List
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import jieba.posseg as psg
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import numpy as np
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import paddle
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from g2pM import G2pM
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from pypinyin import lazy_pinyin
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from pypinyin import Style
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from paddlespeech.t2s.frontend.generate_lexicon import generate_lexicon
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from paddlespeech.t2s.frontend.tone_sandhi import ToneSandhi
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from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer
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class Frontend():
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def __init__(self,
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g2p_model="pypinyin",
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phone_vocab_path=None,
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tone_vocab_path=None):
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self.tone_modifier = ToneSandhi()
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self.text_normalizer = TextNormalizer()
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self.punc = "：，；。？！“”‘’':,;.?!"
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# g2p_model can be pypinyin and g2pM
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self.g2p_model = g2p_model
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if self.g2p_model == "g2pM":
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self.g2pM_model = G2pM()
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self.pinyin2phone = generate_lexicon(
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with_tone=True, with_erhua=False)
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self.must_erhua = {"小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿"}
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self.not_erhua = {
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"虐儿", "为儿", "护儿", "瞒儿", "救儿", "替儿", "有儿", "一儿", "我儿", "俺儿", "妻儿",
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"拐儿", "聋儿", "乞儿", "患儿", "幼儿", "孤儿", "婴儿", "婴幼儿", "连体儿", "脑瘫儿",
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"流浪儿", "体弱儿", "混血儿", "蜜雪儿", "舫儿", "祖儿", "美儿", "应采儿", "可儿", "侄儿",
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"孙儿", "侄孙儿", "女儿", "男儿", "红孩儿", "花儿", "虫儿", "马儿", "鸟儿", "猪儿", "猫儿",
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"狗儿"
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}
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self.vocab_phones = {}
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self.vocab_tones = {}
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if phone_vocab_path:
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with open(phone_vocab_path, 'rt') as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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for phn, id in phn_id:
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self.vocab_phones[phn] = int(id)
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if tone_vocab_path:
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with open(tone_vocab_path, 'rt') as f:
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tone_id = [line.strip().split() for line in f.readlines()]
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for tone, id in tone_id:
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self.vocab_tones[tone] = int(id)
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def _get_initials_finals(self, word: str) -> List[List[str]]:
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initials = []
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finals = []
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if self.g2p_model == "pypinyin":
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orig_initials = lazy_pinyin(
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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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for c, v in zip(orig_initials, orig_finals):
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if re.match(r'i\d', v):
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if c in ['z', 'c', 's']:
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v = re.sub('i', 'ii', v)
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elif c in ['zh', 'ch', 'sh', 'r']:
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v = re.sub('i', 'iii', v)
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initials.append(c)
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finals.append(v)
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elif self.g2p_model == "g2pM":
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pinyins = self.g2pM_model(word, tone=True, char_split=False)
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for pinyin in pinyins:
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pinyin = pinyin.replace("u:", "v")
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if pinyin in self.pinyin2phone:
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initial_final_list = self.pinyin2phone[pinyin].split(" ")
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if len(initial_final_list) == 2:
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initials.append(initial_final_list[0])
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finals.append(initial_final_list[1])
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elif len(initial_final_list) == 1:
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initials.append('')
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finals.append(initial_final_list[1])
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else:
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# If it's not pinyin (possibly punctuation) or no conversion is required
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initials.append(pinyin)
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finals.append(pinyin)
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return initials, finals
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# if merge_sentences, merge all sentences into one phone sequence
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def _g2p(self,
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sentences: List[str],
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merge_sentences: bool=True,
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with_erhua: bool=True) -> List[List[str]]:
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segments = sentences
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phones_list = []
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for seg in segments:
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phones = []
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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 = self.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 = self._get_initials_finals(word)
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sub_finals = self.tone_modifier.modified_tone(word, pos,
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sub_finals)
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if with_erhua:
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sub_initials, sub_finals = self._merge_erhua(
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sub_initials, sub_finals, word, pos)
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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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for c, v in zip(initials, finals):
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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 and c not in self.punc:
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phones.append(c)
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if c and c in self.punc:
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phones.append('sp')
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if v and v not in self.punc:
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phones.append(v)
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phones_list.append(phones)
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if merge_sentences:
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merge_list = sum(phones_list, [])
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# rm the last 'sp' to avoid the noise at the end
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# cause in the training data, no 'sp' in the end
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if merge_list[-1] == 'sp':
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merge_list = merge_list[:-1]
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phones_list = []
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phones_list.append(merge_list)
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return phones_list
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def _merge_erhua(self,
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initials: List[str],
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finals: List[str],
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word: str,
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pos: str) -> List[List[str]]:
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if word not in self.must_erhua and (word in self.not_erhua or
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pos in {"a", "j", "nr"}):
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return initials, finals
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# "……" 等情况直接返回
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if len(finals) != len(word):
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return initials, finals
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assert len(finals) == len(word)
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new_initials = []
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new_finals = []
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for i, phn in enumerate(finals):
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if i == len(finals) - 1 and word[i] == "儿" and phn in {
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"er2", "er5"
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} and word[-2:] not in self.not_erhua and new_finals:
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new_finals[-1] = new_finals[-1][:-1] + "r" + new_finals[-1][-1]
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else:
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new_finals.append(phn)
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new_initials.append(initials[i])
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return new_initials, new_finals
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def _p2id(self, phonemes: List[str]) -> np.array:
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# replace unk phone with sp
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phonemes = [
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phn if phn in self.vocab_phones else "sp" for phn in phonemes
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]
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phone_ids = [self.vocab_phones[item] for item in phonemes]
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return np.array(phone_ids, np.int64)
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def _t2id(self, tones: List[str]) -> np.array:
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# replace unk phone with sp
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tones = [tone if tone in self.vocab_tones else "0" for tone in tones]
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tone_ids = [self.vocab_tones[item] for item in tones]
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return np.array(tone_ids, np.int64)
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def _get_phone_tone(self, phonemes: List[str],
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get_tone_ids: bool=False) -> List[List[str]]:
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phones = []
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tones = []
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if get_tone_ids and self.vocab_tones:
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for full_phone in phonemes:
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# split tone from finals
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match = re.match(r'^(\w+)([012345])$', full_phone)
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if match:
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phone = match.group(1)
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tone = match.group(2)
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# if the merged erhua not in the vocab
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# assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, we split 'iaor' into ['iao','er']
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# and the tones accordingly change from ['3'] to ['3','2'], while '2' is the tone of 'er2'
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if len(phone) >= 2 and phone != "er" and phone[
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-1] == 'r' and phone not in self.vocab_phones and phone[:
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-1] in self.vocab_phones:
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phones.append(phone[:-1])
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phones.append("er")
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tones.append(tone)
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tones.append("2")
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else:
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phones.append(phone)
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tones.append(tone)
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else:
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phones.append(full_phone)
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tones.append('0')
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else:
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for phone in phonemes:
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# if the merged erhua not in the vocab
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# assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, change ['iaor3'] to ['iao3','er2']
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if len(phone) >= 3 and phone[:-1] != "er" and phone[
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-2] == 'r' and phone not in self.vocab_phones and (
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phone[:-2] + phone[-1]) in self.vocab_phones:
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phones.append((phone[:-2] + phone[-1]))
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phones.append("er2")
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else:
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phones.append(phone)
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return phones, tones
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def get_phonemes(self,
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sentence: str,
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merge_sentences: bool=True,
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with_erhua: bool=True,
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robot: bool=False,
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print_info: bool=False) -> List[List[str]]:
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sentences = self.text_normalizer.normalize(sentence)
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phonemes = self._g2p(
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sentences, merge_sentences=merge_sentences, with_erhua=with_erhua)
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# change all tones to `1`
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if robot:
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new_phonemes = []
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for sentence in phonemes:
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new_sentence = []
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for item in sentence:
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# `er` only have tone `2`
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if item[-1] in "12345" and item != "er2":
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item = item[:-1] + "1"
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new_sentence.append(item)
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new_phonemes.append(new_sentence)
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phonemes = new_phonemes
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if print_info:
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print("----------------------------")
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print("text norm results:")
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print(sentences)
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print("----------------------------")
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print("g2p results:")
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print(phonemes)
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print("----------------------------")
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return phonemes
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def get_input_ids(self,
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sentence: str,
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merge_sentences: bool=True,
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get_tone_ids: bool=False,
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robot: bool=False,
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print_info: bool=False) -> Dict[str, List[paddle.Tensor]]:
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phonemes = self.get_phonemes(
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sentence,
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merge_sentences=merge_sentences,
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print_info=print_info,
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robot=robot)
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result = {}
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phones = []
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tones = []
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temp_phone_ids = []
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temp_tone_ids = []
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for part_phonemes in phonemes:
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phones, tones = self._get_phone_tone(
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part_phonemes, get_tone_ids=get_tone_ids)
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if tones:
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tone_ids = self._t2id(tones)
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tone_ids = paddle.to_tensor(tone_ids)
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temp_tone_ids.append(tone_ids)
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if phones:
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phone_ids = self._p2id(phones)
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phone_ids = paddle.to_tensor(phone_ids)
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temp_phone_ids.append(phone_ids)
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if temp_tone_ids:
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result["tone_ids"] = temp_tone_ids
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if temp_phone_ids:
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result["phone_ids"] = temp_phone_ids
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return result
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