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140 lines
6.1 KiB
140 lines
6.1 KiB
import os
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import json
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import onnxruntime
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import numpy as np
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from opencc import OpenCC
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from paddlenlp.transformers import BertTokenizer
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from paddlespeech.t2s.frontend.g2pw.dataset import prepare_data, prepare_onnx_input, get_phoneme_labels, get_char_phoneme_labels
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from paddlespeech.t2s.frontend.g2pw.utils import load_config
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MODEL_URL = 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel.tar'
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def predict(session, onnx_input, labels):
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all_preds = []
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all_confidences = []
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probs = session.run([],{"input_ids": onnx_input['input_ids'],
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"token_type_ids":onnx_input['token_type_ids'],
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"attention_mask":onnx_input['attention_masks'],
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"phoneme_mask":onnx_input['phoneme_masks'],
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"char_ids":onnx_input['char_ids'],
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"position_ids":onnx_input['position_ids']})[0]
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preds = np.argmax(probs,axis=1).tolist()
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max_probs = []
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for index,arr in zip(preds,probs.tolist()):
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max_probs.append(arr[index])
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all_preds += [labels[pred] for pred in preds]
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all_confidences += max_probs
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return all_preds, all_confidences
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def download_model(model_dir):
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wget_shell = "cd %s && wget %s"%(model_dir,MODEL_URL)
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os.system(wget_shell)
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shell = "cd %s ;tar -xvf %s;cd %s/G2PWModel;rm -rf .*" % (model_dir,MODEL_URL.split("/")[-1], model_dir)
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os.system(shell)
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rm_shell = "cd %s && rm -rf %s"%(model_dir,MODEL_URL.split("/")[-1])
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os.system(rm_shell)
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class G2PWOnnxConverter:
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def __init__(self, style='bopomofo', model_source=None, enable_non_tradional_chinese=False):
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model_dir = os.path.dirname(os.path.abspath(__file__))
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if not os.path.exists(os.path.join(model_dir, 'G2PWModel/g2pW.onnx')):
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download_model(model_dir)
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self.session_g2pW = onnxruntime.InferenceSession(os.path.join(model_dir, 'G2PWModel/g2pW.onnx'))
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self.config = load_config(os.path.join(model_dir, 'G2PWModel/config.py'), use_default=True)
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self.model_source = model_source if model_source else self.config.model_source
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self.enable_opencc = enable_non_tradional_chinese
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self.tokenizer = BertTokenizer.from_pretrained(self.config.model_source)
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polyphonic_chars_path = os.path.join(model_dir, 'G2PWModel/POLYPHONIC_CHARS.txt')
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monophonic_chars_path = os.path.join(model_dir, 'G2PWModel/MONOPHONIC_CHARS.txt')
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self.polyphonic_chars = [line.split('\t') for line in open(polyphonic_chars_path,encoding='utf-8').read().strip().split('\n')]
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self.monophonic_chars = [line.split('\t') for line in open(monophonic_chars_path,encoding='utf-8').read().strip().split('\n')]
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self.labels, self.char2phonemes = get_char_phoneme_labels(self.polyphonic_chars) if self.config.use_char_phoneme else get_phoneme_labels(self.polyphonic_chars)
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self.chars = sorted(list(self.char2phonemes.keys()))
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self.pos_tags = ['UNK', 'A', 'C', 'D', 'I', 'N', 'P', 'T', 'V', 'DE', 'SHI']
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with open(os.path.join(os.path.dirname(os.path.abspath(__file__)),
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'bopomofo_to_pinyin_wo_tune_dict.json'), 'r',encoding='utf-8') as fr:
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self.bopomofo_convert_dict = json.load(fr)
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self.style_convert_func = {
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'bopomofo': lambda x: x,
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'pinyin': self._convert_bopomofo_to_pinyin,
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}[style]
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with open(os.path.join(os.path.dirname(os.path.abspath(__file__)),
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'char_bopomofo_dict.json'), 'r',encoding='utf-8') as fr:
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self.char_bopomofo_dict = json.load(fr)
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if self.enable_opencc:
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self.cc = OpenCC('s2tw')
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def _convert_bopomofo_to_pinyin(self, bopomofo):
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tone = bopomofo[-1]
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assert tone in '12345'
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component = self.bopomofo_convert_dict.get(bopomofo[:-1])
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if component:
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return component + tone
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else:
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print(f'Warning: "{bopomofo}" cannot convert to pinyin')
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return None
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def __call__(self, sentences):
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if isinstance(sentences, str):
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sentences = [sentences]
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if self.enable_opencc:
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translated_sentences = []
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for sent in sentences:
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translated_sent = self.cc.convert(sent)
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assert len(translated_sent) == len(sent)
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translated_sentences.append(translated_sent)
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sentences = translated_sentences
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texts, query_ids, sent_ids, partial_results = self._prepare_data(sentences)
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onnx_input = prepare_onnx_input(self.tokenizer, self.labels, self.char2phonemes, self.chars, texts, query_ids,
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use_mask=self.config.use_mask, use_char_phoneme=self.config.use_char_phoneme,
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window_size=self.config.window_size)
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preds, confidences = predict(self.session_g2pW, onnx_input, self.labels)
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if self.config.use_char_phoneme:
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preds = [pred.split(' ')[1] for pred in preds]
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results = partial_results
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for sent_id, query_id, pred in zip(sent_ids, query_ids, preds):
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results[sent_id][query_id] = self.style_convert_func(pred)
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return results
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def _prepare_data(self, sentences):
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polyphonic_chars = set(self.chars)
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monophonic_chars_dict = {
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char: phoneme for char, phoneme in self.monophonic_chars
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}
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texts, query_ids, sent_ids, partial_results = [], [], [], []
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for sent_id, sent in enumerate(sentences):
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partial_result = [None] * len(sent)
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for i, char in enumerate(sent):
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if char in polyphonic_chars:
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texts.append(sent)
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query_ids.append(i)
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sent_ids.append(sent_id)
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elif char in monophonic_chars_dict:
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partial_result[i] = self.style_convert_func(monophonic_chars_dict[char])
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elif char in self.char_bopomofo_dict:
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partial_result[i] = self.style_convert_func(self.char_bopomofo_dict[char][0])
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partial_results.append(partial_result)
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return texts, query_ids, sent_ids, partial_results
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