# Copyright (c) 2022 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. """ Credits This code is modified from https://github.com/GitYCC/g2pW """ import json import os from typing import Any from typing import Dict from typing import List from typing import Tuple import numpy as np import onnxruntime from opencc import OpenCC from paddlenlp.transformers import BertTokenizer from pypinyin import pinyin from pypinyin import Style from paddlespeech.cli.utils import download_and_decompress from paddlespeech.resource.pretrained_models import g2pw_onnx_models from paddlespeech.t2s.frontend.g2pw.dataset import get_char_phoneme_labels from paddlespeech.t2s.frontend.g2pw.dataset import get_phoneme_labels from paddlespeech.t2s.frontend.g2pw.dataset import prepare_onnx_input from paddlespeech.t2s.frontend.g2pw.utils import load_config from paddlespeech.t2s.frontend.zh_normalization.char_convert import tranditional_to_simplified from paddlespeech.utils.env import MODEL_HOME model_version = '1.1' def predict(session, onnx_input: Dict[str, Any], labels: List[str]) -> Tuple[List[str], List[float]]: all_preds = [] all_confidences = [] probs = session.run([], { "input_ids": onnx_input['input_ids'], "token_type_ids": onnx_input['token_type_ids'], "attention_mask": onnx_input['attention_masks'], "phoneme_mask": onnx_input['phoneme_masks'], "char_ids": onnx_input['char_ids'], "position_ids": onnx_input['position_ids'] })[0] preds = np.argmax(probs, axis=1).tolist() max_probs = [] for index, arr in zip(preds, probs.tolist()): max_probs.append(arr[index]) all_preds += [labels[pred] for pred in preds] all_confidences += max_probs return all_preds, all_confidences class G2PWOnnxConverter: def __init__(self, model_dir: os.PathLike=MODEL_HOME, style: str='bopomofo', model_source: str=None, enable_non_tradional_chinese: bool=False): uncompress_path = download_and_decompress( g2pw_onnx_models['G2PWModel'][model_version], model_dir) sess_options = onnxruntime.SessionOptions() sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL sess_options.intra_op_num_threads = 2 self.session_g2pW = onnxruntime.InferenceSession( os.path.join(uncompress_path, 'g2pW.onnx'), sess_options=sess_options) self.config = load_config( config_path=os.path.join(uncompress_path, 'config.py'), use_default=True) self.model_source = model_source if model_source else self.config.model_source self.enable_opencc = enable_non_tradional_chinese self.tokenizer = BertTokenizer.from_pretrained(self.config.model_source) polyphonic_chars_path = os.path.join(uncompress_path, 'POLYPHONIC_CHARS.txt') monophonic_chars_path = os.path.join(uncompress_path, 'MONOPHONIC_CHARS.txt') self.polyphonic_chars = [ line.split('\t') for line in open(polyphonic_chars_path, encoding='utf-8').read() .strip().split('\n') ] self.non_polyphonic = { '一', '不', '和', '咋', '嗲', '剖', '差', '攢', '倒', '難', '奔', '勁', '拗', '肖', '瘙', '誒', '泊' } self.non_monophonic = {'似', '攢'} self.monophonic_chars = [ line.split('\t') for line in open(monophonic_chars_path, encoding='utf-8').read() .strip().split('\n') ] self.labels, self.char2phonemes = get_char_phoneme_labels( polyphonic_chars=self.polyphonic_chars ) if self.config.use_char_phoneme else get_phoneme_labels( polyphonic_chars=self.polyphonic_chars) self.chars = sorted(list(self.char2phonemes.keys())) self.polyphonic_chars_new = set(self.chars) for char in self.non_polyphonic: if char in self.polyphonic_chars_new: self.polyphonic_chars_new.remove(char) self.monophonic_chars_dict = { char: phoneme for char, phoneme in self.monophonic_chars } for char in self.non_monophonic: if char in self.monophonic_chars_dict: self.monophonic_chars_dict.pop(char) self.pos_tags = [ 'UNK', 'A', 'C', 'D', 'I', 'N', 'P', 'T', 'V', 'DE', 'SHI' ] with open( os.path.join(uncompress_path, 'bopomofo_to_pinyin_wo_tune_dict.json'), 'r', encoding='utf-8') as fr: self.bopomofo_convert_dict = json.load(fr) self.style_convert_func = { 'bopomofo': lambda x: x, 'pinyin': self._convert_bopomofo_to_pinyin, }[style] with open( os.path.join(uncompress_path, 'char_bopomofo_dict.json'), 'r', encoding='utf-8') as fr: self.char_bopomofo_dict = json.load(fr) if self.enable_opencc: self.cc = OpenCC('s2tw') def _convert_bopomofo_to_pinyin(self, bopomofo: str) -> str: tone = bopomofo[-1] assert tone in '12345' component = self.bopomofo_convert_dict.get(bopomofo[:-1]) if component: return component + tone else: print(f'Warning: "{bopomofo}" cannot convert to pinyin') return None def __call__(self, sentences: List[str]) -> List[List[str]]: if isinstance(sentences, str): sentences = [sentences] if self.enable_opencc: translated_sentences = [] for sent in sentences: translated_sent = self.cc.convert(sent) assert len(translated_sent) == len(sent) translated_sentences.append(translated_sent) sentences = translated_sentences texts, query_ids, sent_ids, partial_results = self._prepare_data( sentences=sentences) if len(texts) == 0: # sentences no polyphonic words return partial_results onnx_input = prepare_onnx_input( tokenizer=self.tokenizer, labels=self.labels, char2phonemes=self.char2phonemes, chars=self.chars, texts=texts, query_ids=query_ids, use_mask=self.config.use_mask, window_size=None) preds, confidences = predict( session=self.session_g2pW, onnx_input=onnx_input, labels=self.labels) if self.config.use_char_phoneme: preds = [pred.split(' ')[1] for pred in preds] results = partial_results for sent_id, query_id, pred in zip(sent_ids, query_ids, preds): results[sent_id][query_id] = self.style_convert_func(pred) return results def _prepare_data( self, sentences: List[str] ) -> Tuple[List[str], List[int], List[int], List[List[str]]]: texts, query_ids, sent_ids, partial_results = [], [], [], [] for sent_id, sent in enumerate(sentences): # pypinyin works well for Simplified Chinese than Traditional Chinese sent_s = tranditional_to_simplified(sent) pypinyin_result = pinyin(sent_s, style=Style.TONE3) partial_result = [None] * len(sent) for i, char in enumerate(sent): if char in self.polyphonic_chars_new: texts.append(sent) query_ids.append(i) sent_ids.append(sent_id) elif char in self.monophonic_chars_dict: partial_result[i] = self.style_convert_func( self.monophonic_chars_dict[char]) elif char in self.char_bopomofo_dict: partial_result[i] = pypinyin_result[i][0] # partial_result[i] = self.style_convert_func(self.char_bopomofo_dict[char][0]) else: partial_result[i] = pypinyin_result[i][0] partial_results.append(partial_result) return texts, query_ids, sent_ids, partial_results