#!/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):
if(16000 != fs):
raise ValueError('[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this audio is ' + str(fs) + ' Hz. ')
# 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):
if(16000 != fs):
raise ValueError('[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this audio is ' + str(fs) + ' Hz. ')
# 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):
if(16000 != fs):
raise ValueError('[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this audio is ' + str(fs) + ' Hz. ')
# 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 GetFrequencyFeature4(wavsignal, fs):
'''
主要是用来修正3版的bug
'''
if(16000 != fs):
raise ValueError('[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this audio is ' + str(fs) + ' Hz. ')
# 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 + 1 # 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, window_length // 2), dtype = np.float) # 用于存放最终的频率特征数据
data_line = np.zeros((1, window_length), 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: window_length // 2] # 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()
