#!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import wave import numpy as np import matplotlib.pyplot as plt import math import time from python_speech_features import mfcc from python_speech_features import delta from python_speech_features import logfbank from scipy.fftpack import fft def read_wav_data(filename): ''' 读取一个wav文件，返回声音信号的时域谱矩阵和播放时间 ''' wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流 num_frame = wav.getnframes() # 获取帧数 num_channel=wav.getnchannels() # 获取声道数 framerate=wav.getframerate() # 获取帧速率 num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数 str_data = wav.readframes(num_frame) # 读取全部的帧 wav.close() # 关闭流 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 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 wave_data = wave_data.T # 将矩阵转置 #wave_data = wave_data return wave_data, framerate def GetMfccFeature(wavsignal, fs): # 获取输入特征 feat_mfcc=mfcc(wavsignal[0],fs) feat_mfcc_d=delta(feat_mfcc,2) feat_mfcc_dd=delta(feat_mfcc_d,2) # 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 wav_feature = np.column_stack((feat_mfcc, feat_mfcc_d, feat_mfcc_dd)) return wav_feature def GetFrequencyFeature(wavsignal, fs): # wav波形 加时间窗以及时移10ms time_window = 25 # 单位ms data_input = [] #print(int(len(wavsignal[0])/fs*1000 - time_window) // 10) wav_length = len(wavsignal[0]) # 计算一条语音信号的原始长度 range0_end = int(len(wavsignal[0])/fs*1000 - time_window) // 10 # 计算循环终止的位置，也就是最终生成的窗数 for i in range(0, range0_end): p_start = i * 160 p_end = p_start + 400 data_line = [] for j in range(p_start, p_end): data_line.append(wavsignal[0][j]) #print('wavsignal[0][j]:\n',wavsignal[0][j]) #data_line = abs(fft(data_line)) / len(wavsignal[0]) data_line = fft(data_line) / wav_length data_line2 = [] for fre_sig in data_line: # 分别取出频率信号的实部和虚部作为语音信号的频率特征 # 直接使用复数的话，之后会被numpy将虚部丢弃，造成信息丢失 #print('fre_sig:\n',fre_sig) data_line2.append(fre_sig.real) data_line2.append(fre_sig.imag) data_input.append(data_line2[0:len(data_line2)//2]) # 除以2是取一半数据，因为是对称的 #print('data_input:\n',data_input) #print('data_line:\n',data_line) #print(len(data_input),len(data_input[0])) return data_input def GetFrequencyFeature2(wavsignal, fs): # wav波形 加时间窗以及时移10ms time_window = 25 # 单位ms window_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值 wav_arr = np.array(wavsignal) #wav_length = len(wavsignal[0]) wav_length = wav_arr.shape[1] range0_end = int(len(wavsignal[0])/fs*1000 - time_window) // 10 # 计算循环终止的位置，也就是最终生成的窗数 data_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据 data_line = np.zeros((1, 400), dtype = np.float) for i in range(0, range0_end): p_start = i * 160 p_end = p_start + 400 data_line = wav_arr[0, p_start:p_end] ''' x=np.linspace(0, 400 - 1, 400, dtype = np.int64) w = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗 data_line = data_line * w # 加窗 ''' data_line = np.abs(fft(data_line)) / wav_length data_input[i]=data_line[0:200] # 设置为400除以2的值（即200）是取一半数据，因为是对称的 #print(data_input.shape) return data_input x=np.linspace(0, 400 - 1, 400, dtype = np.int64) w = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗 def GetFrequencyFeature3(wavsignal, fs): # wav波形 加时间窗以及时移10ms time_window = 25 # 单位ms window_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值 wav_arr = np.array(wavsignal) #wav_length = len(wavsignal[0]) wav_length = wav_arr.shape[1] range0_end = int(len(wavsignal[0])/fs*1000 - time_window) // 10 # 计算循环终止的位置，也就是最终生成的窗数 data_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据 data_line = np.zeros((1, 400), dtype = np.float) for i in range(0, range0_end): p_start = i * 160 p_end = p_start + 400 data_line = wav_arr[0, p_start:p_end] data_line = data_line * w # 加窗 data_line = np.abs(fft(data_line)) / wav_length data_input[i]=data_line[0:200] # 设置为400除以2的值（即200）是取一半数据，因为是对称的 #print(data_input.shape) data_input = np.log(data_input + 1) return data_input def wav_scale(energy): ''' 语音信号能量归一化 ''' means = energy.mean() # 均值 var=energy.var() # 方差 e=(energy-means)/math.sqrt(var) # 归一化能量 return e def wav_scale2(energy): ''' 语音信号能量归一化 ''' maxnum = max(energy) e = energy / maxnum return e def wav_scale3(energy): ''' 语音信号能量归一化 ''' for i in range(len(energy)): #if i == 1: # #print('wavsignal[0]:\n {:.4f}'.format(energy[1]),energy[1] is int) energy[i] = float(energy[i]) / 100.0 #if i == 1: # #print('wavsignal[0]:\n {:.4f}'.format(energy[1]),energy[1] is int) return energy def wav_show(wave_data, fs): # 显示出来声音波形 time = np.arange(0, len(wave_data)) * (1.0/fs) # 计算声音的播放时间，单位为秒 # 画声音波形 #plt.subplot(211) plt.plot(time, wave_data) #plt.subplot(212) #plt.plot(time, wave_data[1], c = "g") plt.show() def get_wav_list(filename): ''' 读取一个wav文件列表，返回一个存储该列表的字典类型值 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 ''' txt_obj=open(filename,'r') # 打开文件并读入 txt_text=txt_obj.read() txt_lines=txt_text.split('\n') # 文本分割 dic_filelist={} # 初始化字典 list_wavmark=[] # 初始化wav列表 for i in txt_lines: if(i!=''): txt_l=i.split(' ') dic_filelist[txt_l[0]] = txt_l[1] list_wavmark.append(txt_l[0]) txt_obj.close() return dic_filelist,list_wavmark def get_wav_symbol(filename): ''' 读取指定数据集中，所有wav文件对应的语音符号 返回一个存储符号集的字典类型值 ''' txt_obj=open(filename,'r') # 打开文件并读入 txt_text=txt_obj.read() txt_lines=txt_text.split('\n') # 文本分割 dic_symbol_list={} # 初始化字典 list_symbolmark=[] # 初始化symbol列表 for i in txt_lines: if(i!=''): txt_l=i.split(' ') dic_symbol_list[txt_l[0]]=txt_l[1:] list_symbolmark.append(txt_l[0]) txt_obj.close() return dic_symbol_list,list_symbolmark if(__name__=='__main__'): wave_data, fs = read_wav_data("A2_0.wav") wav_show(wave_data[0],fs) t0=time.time() freimg = GetFrequencyFeature3(wave_data,fs) t1=time.time() print('time cost:',t1-t0) freimg = freimg.T plt.subplot(111) plt.imshow(freimg) plt.colorbar(cax=None,ax=None,shrink=0.5) plt.show()