87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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数据预处理用的程序
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'''
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import os
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import wave
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import numpy as np
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def read_wav_data(filename):
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'''
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读取一个wav文件，返回声音信号的时域谱矩阵和播放时间
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'''
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wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流
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num_frame = wav.getnframes() # 获取帧数
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num_channel=wav.getnchannels() # 获取声道数
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framerate=wav.getframerate() # 获取帧速率
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num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数
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str_data = wav.readframes(num_frame) # 读取全部的帧
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wav.close() # 关闭流
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wave_data = np.fromstring(str_data, dtype = np.short) # 将声音文件数据转换为数组矩阵形式
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wave_data.shape = -1, num_channel # 按照声道数将数组整形，单声道时候是一列数组，双声道时候是两列的矩阵
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wave_data = wave_data.T # 将矩阵转置
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#wave_data = wave_data
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return wave_data, framerate
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def GetFrequencyFeature(self, wavsignal, fs):
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# wav波形 加时间窗以及时移10ms
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time_window = 25 # 单位ms
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data_input = []
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for i in range(0,int(len(wavsignal[0])/fs*1000 - time_window) // 10 ):
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p_start = i * 160
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p_end = p_start + 400
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data_line = []
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for j in range(p_start, p_end):
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data_line.append(wavsignal[0][j])
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data_line = fft(data_line) / len(wavsignal[0])
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data_input.append(data_line[0:len(data_line)//2]) # 除以2是取一半数据，因为是对称的
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return data_input
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def get_wav_list(filename):
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'''
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读取一个wav文件列表，返回一个存储该列表的字典类型值
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ps:在数据中专门有几个文件用于存放用于训练、验证和测试的wav文件列表
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'''
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txt_obj=open(filename,'r') # 打开文件并读入
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txt_text=txt_obj.read()
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txt_lines=txt_text.split('\n') # 文本分割
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dic_filelist={} # 初始化字典
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for i in txt_lines:
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if(i!=''):
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txt_l=i.split(' ')
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dic_filelist[txt_l[0]] = txt_l[1]
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txt_obj.close()
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return dic_filelist
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def GetWavDataList(type):
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'''
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加载用于计算的数据列表
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参数：
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type：选取的数据集类型
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train 训练集
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dev 开发集
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test 测试集
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'''
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# 设定选取哪一项作为要使用的数据集
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if(type=='train'):
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filename_wavlist = 'doc\\list\\train.wav.lst'
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elif(type=='dev'):
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filename_wavlist = 'doc\\list\\cv.wav.lst'
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elif(type=='test'):
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filename_wavlist = 'doc\\list\\test.wav.lst'
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else:
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filename_wavlist = '' # 默认留空
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# 读取数据列表，wav文件列表和其对应的符号列表
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self.dic_wavlist = get_wav_list(self.datapath + filename_wavlist)
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return filename_wavlist
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print('main')
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pass |