301 lines
8.8 KiB
Python
301 lines
8.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import platform as plat
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import numpy as np
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from general_function.file_wav import *
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from python_speech_features import mfcc
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from python_speech_features import delta
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from python_speech_features import logfbank
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import random
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#import scipy.io.wavfile as wav
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from scipy.fftpack import fft
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import matplotlib.pyplot as plt
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class DataSpeech():
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def __init__(self,path):
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'''
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初始化
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参数：
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path：数据存放位置根目录
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'''
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system_type = plat.system() # 由于不同的系统的文件路径表示不一样，需要进行判断
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self.datapath = path; # 数据存放位置根目录
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self.slash = ''
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if(system_type == 'Windows'):
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self.slash='\\' # 反斜杠
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elif(system_type == 'Linux'):
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self.slash='/' # 正斜杠
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else:
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print('*[Message] Unknown System\n')
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self.slash='/' # 正斜杠
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if(self.slash != self.datapath[-1]): # 在目录路径末尾增加斜杠
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self.datapath = self.datapath + self.slash
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self.dic_wavlist = {}
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self.dic_symbollist = {}
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self.SymbolNum = 0 # 记录拼音符号数量
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self.list_symbol = self.GetSymbolList() # 全部汉语拼音符号列表
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self.list_wavnum = [] # wav文件标记列表
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self.list_symbolnum = [] # symbol标记列表
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self.DataNum = 0 # 记录数据量
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pass
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def LoadDataList(self,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' + self.slash + 'list' + self.slash + 'train.wav.lst'
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filename_symbollist = 'doc' + self.slash + 'trans' + self.slash + 'train.syllable.txt'
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elif(type=='dev'):
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filename_wavlist = 'doc' + self.slash + 'list' + self.slash + 'cv.wav.lst'
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filename_symbollist = 'doc' + self.slash + 'trans' + self.slash + 'cv.syllable.txt'
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elif(type=='test'):
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filename_wavlist = 'doc' + self.slash + 'list' + self.slash + 'test.wav.lst'
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filename_symbollist = 'doc' + self.slash + 'trans' + self.slash + 'test.syllable.txt'
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else:
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filename_wavlist = '' # 默认留空
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filename_symbollist = ''
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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,self.list_wavnum = get_wav_list(self.datapath + filename_wavlist)
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self.dic_symbollist,self.list_symbolnum = get_wav_symbol(self.datapath + filename_symbollist)
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self.DataNum = self.GetDataNum()
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def GetDataNum(self):
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'''
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获取数据的数量
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当wav数量和symbol数量一致的时候返回正确的值，否则返回-1，代表出错。
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'''
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if(len(self.dic_wavlist) == len(self.dic_symbollist)):
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DataNum = len(self.dic_wavlist)
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else:
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DataNum = -1
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return DataNum
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def GetMfccFeature(self, wavsignal, fs):
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# 获取输入特征
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feat_mfcc=mfcc(wavsignal[0],fs)
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feat_mfcc_d=delta(feat_mfcc,2)
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feat_mfcc_dd=delta(feat_mfcc_d,2)
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# 返回值分别是mfcc特征向量的矩阵及其一阶差分和二阶差分矩阵
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wav_feature = np.column_stack((feat_mfcc, feat_mfcc_d, feat_mfcc_dd))
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return wav_feature
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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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#print(int(len(wavsignal[0])/fs*1000 - time_window) // 10)
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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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#print('wavsignal[0][j]:\n',wavsignal[0][j])
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data_line = abs(fft(data_line)) / len(wavsignal[0])
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#data_line = abs(fft(data_line))
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data_input.append(data_line[0:len(data_line)//2])
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#print('data_line:\n',data_line)
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return data_input
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def GetData(self,n_start,n_amount=1):
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'''
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读取数据，返回神经网络输入值和输出值矩阵(可直接用于神经网络训练的那种)
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参数：
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n_start：从编号为n_start数据开始选取数据
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n_amount：选取的数据数量，默认为1，即一次一个wav文件
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返回：
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三个包含wav特征矩阵的神经网络输入值，和一个标定的类别矩阵神经网络输出值
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'''
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# 读取一个文件
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filename = self.dic_wavlist[self.list_wavnum[n_start]]
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if('Windows' == plat.system()):
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filename=filename.replace('/','\\') # windows系统下需要执行这一行，对文件路径做特别处理
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wavsignal,fs = read_wav_data(self.datapath+filename)
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#print(wavsignal, fs)
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#print(max(wavsignal[0]))
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#wavsignal[0] = np.array(wavsignal[0], dtype=np.float32)
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#wavsignal[0]=wavsignal[0].reshape(wavsignal[0].shape[0])
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#print('wavsignal[0]:\n',wavsignal[0][1])
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# 归一化
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#wavsignal[0] = wav_scale(wavsignal[0])
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#print('wavsignal[0]:\n {:.4f}'.format(wavsignal[0][1]))
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#print('长度：',len(wavsignal[0]))
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#print(max(wavsignal[0]))
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#print(sum(abs(wavsignal[0]))/len(wavsignal[0]))
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data_input = self.GetFrequencyFeature(wavsignal, fs)
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#print('data_input:\n', data_input)
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#data_input = self.GetMfccFeature(wavsignal, fs)
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# 获取输出特征
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list_symbol=self.dic_symbollist[self.list_symbolnum[n_start]]
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feat_out=[]
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#print("数据编号",n_start,filename)
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for i in list_symbol:
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if(''!=i):
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n=self.SymbolToNum(i)
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#v=self.NumToVector(n)
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#feat_out.append(v)
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feat_out.append(n)
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#print('feat_out:',feat_out)
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# 获得对应的拼音符号向量
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#arr_zero = np.zeros((1, 39), dtype=np.int16) #一个全是0的行向量
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#while(len(data_input)<1600): #长度不够时补全到1600
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# data_input = np.row_stack((data_input,arr_zero))
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#data_input = data_input.T
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data_input = np.array(data_input)
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data_label = np.array(feat_out)
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return data_input, data_label
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def data_genetator(self, batch_size=32, audio_length = 1600):
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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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需要再修改。。。
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'''
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X = np.zeros((batch_size, audio_length, 200), dtype=np.float)
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#y = np.zeros((batch_size, 64, self.SymbolNum), dtype=np.int16)
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y = np.zeros((batch_size, 64), dtype=np.int16)
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labels = []
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for i in range(0,batch_size):
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#input_length.append([1500])
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labels.append([1e-12]) # 最终的ctc loss结果，0代表着没有ctc上的loss
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#labels = np.matrix(labels)
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labels = np.array(labels, dtype = np.float)
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#print(input_length,len(input_length))
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while True:
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#generator = ImageCaptcha(width=width, height=height)
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input_length = []
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label_length = []
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ran_num = random.randint(0,self.DataNum - 1) # 获取一个随机数
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for i in range(batch_size):
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data_input, data_labels = self.GetData((ran_num + i) % self.DataNum) # 从随机数开始连续向后取一定数量数据
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#data_input, data_labels = self.GetData(1 % self.DataNum) # 从随机数开始连续向后取一定数量数据
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#input_length.append(data_input.shape[1] // 4 - 2)
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#print(data_input.shape[0],len(data_input))
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input_length.append(data_input.shape[0] // 4 - 3)
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#print(data_input, data_labels)
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#print('data_input长度:',len(data_input))
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X[i,0:len(data_input)] = data_input
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#print('data_labels长度:',len(data_labels))
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#print(data_labels)
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y[i,0:len(data_labels)] = data_labels
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#print(i,y[i].shape)
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#y[i] = y[i].T
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#print(i,y[i].shape)
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label_length.append([len(data_labels)])
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label_length = np.array(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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pass
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def GetSymbolList(self):
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'''
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加载拼音符号列表，用于标记符号
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返回一个列表list类型变量
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'''
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txt_obj=open(self.datapath+'dict.txt','r',encoding='UTF-8') # 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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list_symbol=[] # 初始化符号列表
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for i in txt_lines:
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if(i!=''):
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txt_l=i.split('\t')
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list_symbol.append(txt_l[0])
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txt_obj.close()
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list_symbol.append('_')
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self.SymbolNum = len(list_symbol)
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return list_symbol
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def GetSymbolNum(self):
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'''
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获取拼音符号数量
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'''
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return len(self.list_symbol)
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def SymbolToNum(self,symbol):
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'''
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符号转为数字
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'''
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if(symbol != ''):
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return self.list_symbol.index(symbol)
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return self.SymbolNum
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def NumToVector(self,num):
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'''
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数字转为对应的向量
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'''
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v_tmp=[]
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for i in range(0,len(self.list_symbol)):
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if(i==num):
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v_tmp.append(1)
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else:
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v_tmp.append(0)
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v=np.array(v_tmp)
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return v
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if(__name__=='__main__'):
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#path='E:\\语音数据集'
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#l=DataSpeech(path)
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#l.LoadDataList('train')
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#print(l.GetDataNum())
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#data0=l.GetData(0)
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#print(data0)
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#data0=data0[0].reshape(data0[0].shape[0],data0[0].shape[1])
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#print(data0, data0 is list)
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#plt.subplot(111)
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#plt.imshow(data0.T, cmap=plt.get_cmap('Blues_r'))
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#plt.show()
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#aa=l.data_genetator()
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#for i in aa:
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#a,b=i
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#print(a,b)
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pass
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