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小道轻风/ASRT_SpeechRecognition

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speech_model.py 11.31 KB
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# !/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 <https://www.gnu.org/licenses/>.
# ============================================================================
"""
@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
from utils.thread import threadsafe_generator
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
@threadsafe_generator
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.float64)
data_count = data_loader.get_data_count()
index = 0
while True:
X = np.zeros((batch_size,) + self.speech_model.input_shape, dtype=np.float64)
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)])
index = (index + 1) % data_count
label_length = np.matrix(label_length)
input_length = np.array([input_length]).T
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.float64)
# 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
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