263 lines
11 KiB
Python
263 lines
11 KiB
Python
# !/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#
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# Copyright 2016-2099 Ailemon.net
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#
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# This file is part of ASRT Speech Recognition Tool.
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#
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# ASRT is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# ASRT is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with ASRT. If not, see <https://www.gnu.org/licenses/>.
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# ============================================================================
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"""
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@author: nl8590687
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声学模型基础功能模板定义
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"""
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import os
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import time
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import random
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import numpy as np
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from utils.ops import get_edit_distance, read_wav_data
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from utils.config import load_config_file, DEFAULT_CONFIG_FILENAME, load_pinyin_dict
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from utils.thread import threadsafe_generator
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class ModelSpeech:
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"""
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语音模型类
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参数：
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speech_model: 声学模型类型 (BaseModel类) 实例对象
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speech_features: 声学特征类型(SpeechFeatureMeta类)实例对象
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"""
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def __init__(self, speech_model, speech_features, max_label_length=64):
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self.data_loader = None
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self.speech_model = speech_model
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self.trained_model, self.base_model = speech_model.get_model()
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self.speech_features = speech_features
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self.max_label_length = max_label_length
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@threadsafe_generator
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def _data_generator(self, batch_size, data_loader):
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"""
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数据生成器函数，用于Keras的generator_fit训练
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batch_size: 一次产生的数据量
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"""
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labels = np.zeros((batch_size, 1), dtype=np.float64)
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data_count = data_loader.get_data_count()
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index = 0
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while True:
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X = np.zeros((batch_size,) + self.speech_model.input_shape, dtype=np.float64)
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y = np.zeros((batch_size, self.max_label_length), dtype=np.int16)
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input_length = []
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label_length = []
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for i in range(batch_size):
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wavdata, sample_rate, data_labels = data_loader.get_data(index)
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data_input = self.speech_features.run(wavdata, sample_rate)
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data_input = data_input.reshape(data_input.shape[0], data_input.shape[1], 1)
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# 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.
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# 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
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pool_size = self.speech_model.input_shape[0] // self.speech_model.output_shape[0]
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inlen = min(data_input.shape[0] // pool_size + data_input.shape[0] % pool_size,
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self.speech_model.output_shape[0])
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input_length.append(inlen)
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X[i, 0:len(data_input)] = data_input
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y[i, 0:len(data_labels)] = data_labels
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label_length.append([len(data_labels)])
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index = (index + 1) % data_count
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label_length = np.matrix(label_length)
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input_length = np.array([input_length]).T
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yield [X, y, input_length, label_length], labels
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def train_model(self, optimizer, data_loader, epochs=1, save_step=1, batch_size=16, last_epoch=0, call_back=None):
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"""
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训练模型
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参数：
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optimizer：tensorflow.keras.optimizers 优化器实例对象
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data_loader：数据加载器类型 (SpeechData) 实例对象
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epochs: 迭代轮数
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save_step: 每多少epoch保存一次模型
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batch_size: mini batch大小
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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
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call_back: keras call back函数
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"""
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save_filename = os.path.join('save_models', self.speech_model.get_model_name(),
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self.speech_model.get_model_name())
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self.trained_model.compile(loss=self.speech_model.get_loss_function(), optimizer=optimizer)
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print('[ASRT] Compiles Model Successfully.')
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yielddatas = self._data_generator(batch_size, data_loader)
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data_count = data_loader.get_data_count() # 获取数据的数量
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# 计算每一个epoch迭代的次数
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num_iterate = data_count // batch_size
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iter_start = last_epoch
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iter_end = last_epoch + epochs
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for epoch in range(iter_start, iter_end): # 迭代轮数
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try:
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epoch += 1
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print('[ASRT Training] train epoch %d/%d .' % (epoch, iter_end))
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data_loader.shuffle()
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self.trained_model.fit_generator(yielddatas, num_iterate, callbacks=call_back)
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except StopIteration:
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print('[error] generator error. please check data format.')
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break
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if epoch % save_step == 0:
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if not os.path.exists('save_models'): # 判断保存模型的目录是否存在
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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
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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
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os.makedirs(
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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
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self.save_model(save_filename + '_epoch' + str(epoch))
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print('[ASRT Info] Model training complete. ')
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def load_model(self, filename):
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"""
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加载模型参数
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"""
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self.speech_model.load_weights(filename)
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def save_model(self, filename):
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"""
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保存模型参数
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"""
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self.speech_model.save_weights(filename)
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def evaluate_model(self, data_loader, data_count=-1, out_report=False, show_ratio=True, show_per_step=100):
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"""
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评估检验模型的识别效果
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"""
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data_nums = data_loader.get_data_count()
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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
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data_count = data_nums
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try:
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ran_num = random.randint(0, data_nums - 1) # 获取一个随机数
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words_num = 0
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word_error_num = 0
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nowtime = time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time()))
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if out_report:
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txt_obj = open('Test_Report_' + data_loader.dataset_type + '_' + nowtime + '.txt', 'w',
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encoding='UTF-8') # 打开文件并读入
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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
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txt_obj.seek(0) # 从文件首开始
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txt = ''
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i = 0
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while i < data_count:
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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
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data_input = self.speech_features.run(wavdata, fs)
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data_input = data_input.reshape(data_input.shape[0], data_input.shape[1], 1)
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# 数据格式出错处理 开始
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# 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
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if data_input.shape[0] > self.speech_model.input_shape[0]:
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print('*[Error]', 'wave data lenghth of num', (ran_num + i) % data_nums, 'is too long.',
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'this data\'s length is', data_input.shape[0],
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'expect <=', self.speech_model.input_shape[0],
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'\n A Exception raise when test Speech Model.')
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i += 1
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continue
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# 数据格式出错处理 结束
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pre = self.predict(data_input)
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words_n = data_labels.shape[0] # 获取每个句子的字数
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words_num += words_n # 把句子的总字数加上
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edit_distance = get_edit_distance(data_labels, pre) # 获取编辑距离
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if edit_distance <= words_n: # 当编辑距离小于等于句子字数时
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word_error_num += edit_distance # 使用编辑距离作为错误字数
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else: # 否则肯定是增加了一堆乱七八糟的奇奇怪怪的字
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word_error_num += words_n # 就直接加句子本来的总字数就好了
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if i % show_per_step == 0 and show_ratio:
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print('[ASRT Info] Testing: ', i, '/', data_count)
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txt = ''
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if out_report:
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txt += str(i) + '\n'
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txt += 'True:\t' + str(data_labels) + '\n'
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txt += 'Pred:\t' + str(pre) + '\n'
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txt += '\n'
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txt_obj.write(txt)
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i += 1
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# 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, '%')
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print('*[ASRT Test Result] Speech Recognition ' + data_loader.dataset_type + ' set word error ratio: ',
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word_error_num / words_num * 100, '%')
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if out_report:
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txt = '*[ASRT Test Result] Speech Recognition ' + data_loader.dataset_type + ' set word error ratio: ' + str(
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word_error_num / words_num * 100) + ' %'
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txt_obj.write(txt)
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txt_obj.truncate() # 去除文件末尾剩余未使用的空白存储字节
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txt_obj.close()
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except StopIteration:
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print('[ASRT Error] Model testing raise a error. Please check data format.')
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def predict(self, data_input):
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"""
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预测结果
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返回语音识别后的forward结果
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"""
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return self.speech_model.forward(data_input)
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def recognize_speech(self, wavsignal, fs):
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"""
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最终做语音识别用的函数，识别一个wav序列的语音
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"""
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# 获取输入特征
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data_input = self.speech_features.run(wavsignal, fs)
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data_input = np.array(data_input, dtype=np.float64)
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# print(data_input,data_input.shape)
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data_input = data_input.reshape(data_input.shape[0], data_input.shape[1], 1)
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r1 = self.predict(data_input)
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# 获取拼音列表
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list_symbol_dic, _ = load_pinyin_dict(load_config_file(DEFAULT_CONFIG_FILENAME)['dict_filename'])
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r_str = []
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for i in r1:
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r_str.append(list_symbol_dic[i])
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return r_str
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def recognize_speech_from_file(self, filename):
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"""
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最终做语音识别用的函数，识别指定文件名的语音
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"""
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wavsignal, sample_rate, _, _ = read_wav_data(filename)
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r = self.recognize_speech(wavsignal, sample_rate)
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return r
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@property
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def model(self):
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"""
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返回tf.keras model
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"""
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return self.trained_model
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