PaddleSpeech/paddlespeech/t2s/frontend/zh_frontend.py

# 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 load_phrases_dict
from pypinyin import load_single_dict
from pypinyin import Style
from pypinyin_dict.phrase_pinyin_data import large_pinyin

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

INITIALS = [
    'b', 'p', 'm', 'f', 'd', 't', 'n', 'l', 'g', 'k', 'h', 'zh', 'ch', 'sh',
    'r', 'z', 'c', 's', 'j', 'q', 'x'
]
INITIALS += ['y', 'w', 'sp', 'spl', 'spn', 'sil']


def intersperse(lst, item):
    result = [item] * (len(lst) * 2 + 1)
    result[1::2] = lst
    return result


def insert_after_character(lst, item):
    result = [item]
    for phone in lst:
        result.append(phone)
        if phone not in INITIALS:
            # finals has tones
            # assert phone[-1] in "12345"
            result.append(item)
    return result


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)
        else:
            self.__init__pypinyin()
        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 __init__pypinyin(self):
        large_pinyin.load()

        load_phrases_dict({u'开户行': [[u'ka1i'], [u'hu4'], [u'hang2']]})
        load_phrases_dict({u'发卡行': [[u'fa4'], [u'ka3'], [u'hang2']]})
        load_phrases_dict({u'放款行': [[u'fa4ng'], [u'kua3n'], [u'hang2']]})
        load_phrases_dict({u'茧行': [[u'jia3n'], [u'hang2']]})
        load_phrases_dict({u'行号': [[u'hang2'], [u'ha4o']]})
        load_phrases_dict({u'各地': [[u'ge4'], [u'di4']]})
        load_phrases_dict({u'借还款': [[u'jie4'], [u'hua2n'], [u'kua3n']]})
        load_phrases_dict({u'时间为': [[u'shi2'], [u'jia1n'], [u'we2i']]})
        load_phrases_dict({u'为准': [[u'we2i'], [u'zhu3n']]})
        load_phrases_dict({u'色差': [[u'se4'], [u'cha1']]})

        # 调整字的拼音顺序
        load_single_dict({ord(u'地'): u'de,di4'})

    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.ndarray:
        # 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.ndarray:
        # 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,
                      add_blank: bool=False,
                      blank_token: str="<pad>",
                      to_tensor: bool=True) -> 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 add_blank:
                phones = insert_after_character(phones, blank_token)
            if tones:
                tone_ids = self._t2id(tones)
                if to_tensor:
                    tone_ids = paddle.to_tensor(tone_ids)
                temp_tone_ids.append(tone_ids)
            if phones:
                phone_ids = self._p2id(phones)
                # if use paddle.to_tensor() in onnxruntime, the first time will be too low
                if to_tensor:
                    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