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262 lines
11 KiB
262 lines
11 KiB
# !/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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