# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re from typing import Dict from typing import List import jieba.posseg as psg import numpy as np import paddle from g2pM import G2pM from pypinyin import lazy_pinyin from pypinyin import Style from paddlespeech.t2s.frontend.generate_lexicon import generate_lexicon from paddlespeech.t2s.frontend.tone_sandhi import ToneSandhi from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer class Frontend(): def __init__(self, g2p_model="pypinyin", phone_vocab_path=None, tone_vocab_path=None): self.tone_modifier = ToneSandhi() self.text_normalizer = TextNormalizer() self.punc = "：，；。？！“”‘’':,;.?!" # g2p_model can be pypinyin and g2pM self.g2p_model = g2p_model if self.g2p_model == "g2pM": self.g2pM_model = G2pM() self.pinyin2phone = generate_lexicon( with_tone=True, with_erhua=False) self.must_erhua = {"小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿"} self.not_erhua = { "虐儿", "为儿", "护儿", "瞒儿", "救儿", "替儿", "有儿", "一儿", "我儿", "俺儿", "妻儿", "拐儿", "聋儿", "乞儿", "患儿", "幼儿", "孤儿", "婴儿", "婴幼儿", "连体儿", "脑瘫儿", "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", "孙儿", "侄孙儿", "女儿", "男儿", "红孩儿", "花儿", "虫儿", "马儿", "鸟儿", "猪儿", "猫儿", "狗儿" } self.vocab_phones = {} self.vocab_tones = {} if phone_vocab_path: with open(phone_vocab_path, 'rt') as f: phn_id = [line.strip().split() for line in f.readlines()] for phn, id in phn_id: self.vocab_phones[phn] = int(id) if tone_vocab_path: with open(tone_vocab_path, 'rt') as f: tone_id = [line.strip().split() for line in f.readlines()] for tone, id in tone_id: self.vocab_tones[tone] = int(id) def _get_initials_finals(self, word: str) -> List[List[str]]: initials = [] finals = [] if self.g2p_model == "pypinyin": orig_initials = lazy_pinyin( word, neutral_tone_with_five=True, style=Style.INITIALS) orig_finals = lazy_pinyin( word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) for c, v in zip(orig_initials, orig_finals): if re.match(r'i\d', v): if c in ['z', 'c', 's']: v = re.sub('i', 'ii', v) elif c in ['zh', 'ch', 'sh', 'r']: v = re.sub('i', 'iii', v) initials.append(c) finals.append(v) elif self.g2p_model == "g2pM": pinyins = self.g2pM_model(word, tone=True, char_split=False) for pinyin in pinyins: pinyin = pinyin.replace("u:", "v") if pinyin in self.pinyin2phone: initial_final_list = self.pinyin2phone[pinyin].split(" ") if len(initial_final_list) == 2: initials.append(initial_final_list[0]) finals.append(initial_final_list[1]) elif len(initial_final_list) == 1: initials.append('') finals.append(initial_final_list[1]) else: # If it's not pinyin (possibly punctuation) or no conversion is required initials.append(pinyin) finals.append(pinyin) return initials, finals # if merge_sentences, merge all sentences into one phone sequence def _g2p(self, sentences: List[str], merge_sentences: bool=True, with_erhua: bool=True) -> List[List[str]]: segments = sentences phones_list = [] for seg in segments: phones = [] # Replace all English words in the sentence seg = re.sub('[a-zA-Z]+', '', seg) seg_cut = psg.lcut(seg) initials = [] finals = [] seg_cut = self.tone_modifier.pre_merge_for_modify(seg_cut) for word, pos in seg_cut: if pos == 'eng': continue sub_initials, sub_finals = self._get_initials_finals(word) sub_finals = self.tone_modifier.modified_tone(word, pos, sub_finals) if with_erhua: sub_initials, sub_finals = self._merge_erhua( sub_initials, sub_finals, word, pos) initials.append(sub_initials) finals.append(sub_finals) # assert len(sub_initials) == len(sub_finals) == len(word) initials = sum(initials, []) finals = sum(finals, []) for c, v in zip(initials, finals): # NOTE: post process for pypinyin outputs # we discriminate i, ii and iii if c and c not in self.punc: phones.append(c) if c and c in self.punc: phones.append('sp') if v and v not in self.punc: phones.append(v) phones_list.append(phones) if merge_sentences: merge_list = sum(phones_list, []) # rm the last 'sp' to avoid the noise at the end # cause in the training data, no 'sp' in the end if merge_list[-1] == 'sp': merge_list = merge_list[:-1] phones_list = [] phones_list.append(merge_list) return phones_list def _merge_erhua(self, initials: List[str], finals: List[str], word: str, pos: str) -> List[List[str]]: if word not in self.must_erhua and (word in self.not_erhua or pos in {"a", "j", "nr"}): return initials, finals # "……" 等情况直接返回 if len(finals) != len(word): return initials, finals assert len(finals) == len(word) new_initials = [] new_finals = [] for i, phn in enumerate(finals): if i == len(finals) - 1 and word[i] == "儿" and phn in { "er2", "er5" } and word[-2:] not in self.not_erhua and new_finals: new_finals[-1] = new_finals[-1][:-1] + "r" + new_finals[-1][-1] else: new_finals.append(phn) new_initials.append(initials[i]) return new_initials, new_finals def _p2id(self, phonemes: List[str]) -> np.array: # replace unk phone with sp phonemes = [ phn if phn in self.vocab_phones else "sp" for phn in phonemes ] phone_ids = [self.vocab_phones[item] for item in phonemes] return np.array(phone_ids, np.int64) def _t2id(self, tones: List[str]) -> np.array: # replace unk phone with sp tones = [tone if tone in self.vocab_tones else "0" for tone in tones] tone_ids = [self.vocab_tones[item] for item in tones] return np.array(tone_ids, np.int64) def _get_phone_tone(self, phonemes: List[str], get_tone_ids: bool=False) -> List[List[str]]: phones = [] tones = [] if get_tone_ids and self.vocab_tones: for full_phone in phonemes: # split tone from finals match = re.match(r'^(\w+)([012345])$', full_phone) if match: phone = match.group(1) tone = match.group(2) # if the merged erhua not in the vocab # assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, we split 'iaor' into ['iao','er'] # and the tones accordingly change from ['3'] to ['3','2'], while '2' is the tone of 'er2' if len(phone) >= 2 and phone != "er" and phone[ -1] == 'r' and phone not in self.vocab_phones and phone[: -1] in self.vocab_phones: phones.append(phone[:-1]) phones.append("er") tones.append(tone) tones.append("2") else: phones.append(phone) tones.append(tone) else: phones.append(full_phone) tones.append('0') else: for phone in phonemes: # if the merged erhua not in the vocab # assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, change ['iaor3'] to ['iao3','er2'] if len(phone) >= 3 and phone[:-1] != "er" and phone[ -2] == 'r' and phone not in self.vocab_phones and ( phone[:-2] + phone[-1]) in self.vocab_phones: phones.append((phone[:-2] + phone[-1])) phones.append("er2") else: phones.append(phone) return phones, tones def get_phonemes(self, sentence: str, merge_sentences: bool=True, with_erhua: bool=True, robot: bool=False, print_info: bool=False) -> List[List[str]]: sentences = self.text_normalizer.normalize(sentence) phonemes = self._g2p( sentences, merge_sentences=merge_sentences, with_erhua=with_erhua) # change all tones to `1` if robot: new_phonemes = [] for sentence in phonemes: new_sentence = [] for item in sentence: # `er` only have tone `2` if item[-1] in "12345" and item != "er2": item = item[:-1] + "1" new_sentence.append(item) new_phonemes.append(new_sentence) phonemes = new_phonemes if print_info: print("----------------------------") print("text norm results:") print(sentences) print("----------------------------") print("g2p results:") print(phonemes) print("----------------------------") return phonemes def get_input_ids(self, sentence: str, merge_sentences: bool=True, get_tone_ids: bool=False, robot: bool=False, print_info: bool=False) -> Dict[str, List[paddle.Tensor]]: phonemes = self.get_phonemes( sentence, merge_sentences=merge_sentences, print_info=print_info, robot=robot) result = {} phones = [] tones = [] temp_phone_ids = [] temp_tone_ids = [] for part_phonemes in phonemes: phones, tones = self._get_phone_tone( part_phonemes, get_tone_ids=get_tone_ids) if tones: tone_ids = self._t2id(tones) tone_ids = paddle.to_tensor(tone_ids) temp_tone_ids.append(tone_ids) if phones: phone_ids = self._p2id(phones) phone_ids = paddle.to_tensor(phone_ids) temp_phone_ids.append(phone_ids) if temp_tone_ids: result["tone_ids"] = temp_tone_ids if temp_phone_ids: result["phone_ids"] = temp_phone_ids return result