# This code is modified from https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw # This code is modified from https://github.com/GitYCC/g2pW import warnings warnings.filterwarnings("ignore") import json import os import zipfile,requests from typing import Any from typing import Dict from typing import List from typing import Tuple import numpy as np import onnxruntime onnxruntime.set_default_logger_severity(3) from opencc import OpenCC from transformers import AutoTokenizer from pypinyin import pinyin from pypinyin import Style from .dataset import get_char_phoneme_labels from .dataset import get_phoneme_labels from .dataset import prepare_onnx_input from .utils import load_config from ..zh_normalization.char_convert import tranditional_to_simplified 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 def download_and_decompress(model_dir: str='G2PWModel/'): if not os.path.exists(model_dir): parent_directory = os.path.dirname(model_dir) zip_dir = os.path.join(parent_directory,"G2PWModel_1.1.zip") extract_dir = os.path.join(parent_directory,"G2PWModel_1.1") extract_dir_new = os.path.join(parent_directory,"G2PWModel") print("Downloading g2pw model...") modelscope_url = "https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip" with requests.get(modelscope_url, stream=True) as r: r.raise_for_status() with open(zip_dir, 'wb') as f: for chunk in r.iter_content(chunk_size=8192): if chunk: f.write(chunk) print("Extracting g2pw model...") with zipfile.ZipFile(zip_dir, "r") as zip_ref: zip_ref.extractall(parent_directory) os.rename(extract_dir, extract_dir_new) return model_dir class G2PWOnnxConverter: def __init__(self, model_dir: str='G2PWModel/', style: str='bopomofo', model_source: str=None, enable_non_tradional_chinese: bool=False): uncompress_path = download_and_decompress(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, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) # 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 = AutoTokenizer.from_pretrained(self.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', '听', '噢' } 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, neutral_tone_with_five=True, 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