#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # Copyright 2016-2099 Ailemon.net # # This file is part of ASRT Speech Recognition Tool. # # ASRT is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # ASRT is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with ASRT. If not, see . # ============================================================================ """ @author: nl8590687 声学模型基础功能模板定义 """ import os import time import random import numpy as np from utils.ops import get_edit_distance, read_wav_data from utils.config import load_config_file, DEFAULT_CONFIG_FILENAME, load_pinyin_dict class ModelSpeech: ''' 语音模型类 参数： speech_model: 声学模型类型 (BaseModel类) 实例对象 speech_features: 声学特征类型(SpeechFeatureMeta类)实例对象 ''' def __init__(self, speech_model, speech_features, max_label_length=64): self.data_loader = None self.speech_model = speech_model self.trained_model, self.base_model = speech_model.get_model() self.speech_features = speech_features self.max_label_length = max_label_length def _data_generator(self, batch_size, data_loader): ''' 数据生成器函数，用于Keras的generator_fit训练 batch_size: 一次产生的数据量 ''' labels = np.zeros((batch_size,1), dtype = np.float) data_count = data_loader.get_data_count() index = 0 while True: X = np.zeros((batch_size, )+self.speech_model.input_shape, dtype = np.float) y = np.zeros((batch_size, self.max_label_length), dtype=np.int16) input_length = [] label_length = [] for i in range(batch_size): wavdata, sample_rate, data_labels = data_loader.get_data(index) data_input = self.speech_features.run(wavdata, sample_rate) data_input = data_input.reshape(data_input.shape[0],data_input.shape[1],1) # 必须加上模pool_size得到的值，否则会出现inf问题，然后提示No valid path found. # 但是直接加又可能会出现sequence_length <= xxx 的问题，因此不能让其超过时间序列长度的最大值，比如200 pool_size = self.speech_model.input_shape[0] // self.speech_model.output_shape[0] inlen = min(data_input.shape[0] // pool_size + data_input.shape[0] % pool_size, self.speech_model.output_shape[0]) input_length.append(inlen) X[i,0:len(data_input)] = data_input y[i,0:len(data_labels)] = data_labels label_length.append([len(data_labels)]) label_length = np.matrix(label_length) input_length = np.array([input_length]).T index = (index+1) % data_count yield [X, y, input_length, label_length ], labels def train_model(self, optimizer, data_loader, epochs = 1, save_step = 1, batch_size = 16, last_epoch = 0, call_back=None): ''' 训练模型 参数： optimizer：tensorflow.keras.optimizers 优化器实例对象 data_loader：数据加载器类型 (SpeechData) 实例对象 epochs: 迭代轮数 save_step: 每多少epoch保存一次模型 batch_size: mini batch大小 last_epoch: 上一次epoch的编号，可用于断点处继续训练时，epoch编号不冲突 call_back: keras call back函数 ''' save_filename = os.path.join('save_models', self.speech_model.get_model_name(), self.speech_model.get_model_name()) self.trained_model.compile(loss=self.speech_model.get_loss_function(), optimizer = optimizer) print('[ASRT] Compiles Model Successfully.') yielddatas = self._data_generator(batch_size, data_loader) data_count = data_loader.get_data_count() # 获取数据的数量 # 计算每一个epoch迭代的次数 num_iterate = data_count // batch_size iter_start = last_epoch iter_end = last_epoch + epochs for epoch in range(iter_start, iter_end): # 迭代轮数 try: epoch += 1 print('[ASRT Training] train epoch %d/%d .' % (epoch, iter_end)) data_loader.shuffle() self.trained_model.fit_generator(yielddatas, num_iterate, callbacks = call_back) except StopIteration: print('[error] generator error. please check data format.') break if epoch % save_step == 0: if not os.path.exists('save_models'): # 判断保存模型的目录是否存在 os.makedirs('save_models') # 如果不存在，就新建一个，避免之后保存模型的时候炸掉 if not os.path.exists(os.path.join('save_models',self.speech_model.get_model_name())): # 判断保存模型的目录是否存在 os.makedirs(os.path.join('save_models',self.speech_model.get_model_name())) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉 self.save_model(save_filename + '_epoch' + str(epoch)) print('[ASRT Info] Model training complete. ') def load_model(self,filename): ''' 加载模型参数 ''' self.speech_model.load_weights(filename) def save_model(self,filename): ''' 保存模型参数 ''' self.speech_model.save_weights(filename) def evaluate_model(self, data_loader, data_count = -1, out_report = False, show_ratio = True, show_per_step = 100): ''' 评估检验模型的识别效果 ''' data_nums = data_loader.get_data_count() if(data_count <= 0 or data_count > data_nums): # 当data_count为小于等于0或者大于测试数据量的值时，则使用全部数据来测试 data_count = data_nums try: ran_num = random.randint(0,data_nums - 1) # 获取一个随机数 words_num = 0 word_error_num = 0 nowtime = time.strftime('%Y%m%d_%H%M%S',time.localtime(time.time())) if out_report: txt_obj = open('Test_Report_' + data_loader.dataset_type + '_' + nowtime + '.txt', 'w', encoding='UTF-8') # 打开文件并读入 txt_obj.truncate((data_count + 1) * 300) # 预先分配一定数量的磁盘空间，避免后期在硬盘中文件存储位置频繁移动，以防写入速度越来越慢 txt_obj.seek(0) # 从文件首开始 txt = '' i = 0 while i < data_count: wavdata, fs, data_labels = data_loader.get_data((ran_num + i) % data_nums) # 从随机数开始连续向后取一定数量数据 data_input = self.speech_features.run(wavdata, fs) data_input = data_input.reshape(data_input.shape[0],data_input.shape[1],1) # 数据格式出错处理 开始 # 当输入的wav文件长度过长时自动跳过该文件，转而使用下一个wav文件来运行 if data_input.shape[0] > self.speech_model.input_shape[0]: print('*[Error]','wave data lenghth of num',(ran_num + i) % data_nums, 'is too long.', 'this data\'s length is', data_input.shape[0], 'expect <=', self.speech_model.input_shape[0], '\n A Exception raise when test Speech Model.') i += 1 continue # 数据格式出错处理 结束 pre = self.predict(data_input) words_n = data_labels.shape[0] # 获取每个句子的字数 words_num += words_n # 把句子的总字数加上 edit_distance = get_edit_distance(data_labels, pre) # 获取编辑距离 if edit_distance <= words_n: # 当编辑距离小于等于句子字数时 word_error_num += edit_distance # 使用编辑距离作为错误字数 else: # 否则肯定是增加了一堆乱七八糟的奇奇怪怪的字 word_error_num += words_n # 就直接加句子本来的总字数就好了 if i % show_per_step == 0 and show_ratio: print('[ASRT Info] Testing: ',i,'/',data_count) txt = '' if out_report: txt += str(i) + '\n' txt += 'True:\t' + str(data_labels) + '\n' txt += 'Pred:\t' + str(pre) + '\n' txt += '\n' txt_obj.write(txt) i += 1 #print('*[测试结果] 语音识别 ' + str_dataset + ' 集语音单字错误率：', word_error_num / words_num * 100, '%') print('*[ASRT Test Result] Speech Recognition ' + data_loader.dataset_type + ' set word error ratio: ', word_error_num / words_num * 100, '%') if out_report: txt = '*[ASRT Test Result] Speech Recognition ' + data_loader.dataset_type + ' set word error ratio: ' + str(word_error_num / words_num * 100) + ' %' txt_obj.write(txt) txt_obj.truncate() # 去除文件末尾剩余未使用的空白存储字节 txt_obj.close() except StopIteration: print('[ASRT Error] Model testing raise a error. Please check data format.') def predict(self, data_input): ''' 预测结果 返回语音识别后的forward结果 ''' return self.speech_model.forward(data_input) def recognize_speech(self, wavsignal, fs): ''' 最终做语音识别用的函数，识别一个wav序列的语音 ''' # 获取输入特征 data_input = self.speech_features.run(wavsignal, fs) data_input = np.array(data_input, dtype = np.float) #print(data_input,data_input.shape) data_input = data_input.reshape(data_input.shape[0],data_input.shape[1],1) r1 = self.predict(data_input) # 获取拼音列表 list_symbol_dic, _ = load_pinyin_dict(load_config_file(DEFAULT_CONFIG_FILENAME)['dict_filename']) r_str=[] for i in r1: r_str.append(list_symbol_dic[i]) return r_str def recognize_speech_from_file(self, filename): ''' 最终做语音识别用的函数，识别指定文件名的语音 ''' wavsignal,sample_rate, _, _ = read_wav_data(filename) r = self.recognize_speech(wavsignal, sample_rate) return r @property def model(self): ''' 返回tf.keras model ''' return self.trained_model