231 lines
7.2 KiB
Python
231 lines
7.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import wave
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import numpy as np
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import matplotlib.pyplot as plt
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import math
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import time
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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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from scipy.fftpack import fft
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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 GetMfccFeature(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(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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wav_length = len(wavsignal[0]) # 计算一条语音信号的原始长度
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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
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for i in range(0, range0_end):
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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 = fft(data_line) / wav_length
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data_line2 = []
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for fre_sig in data_line:
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# 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
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# 直接使用复数的话，之后会被numpy将虚部丢弃，造成信息丢失
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#print('fre_sig:\n',fre_sig)
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data_line2.append(fre_sig.real)
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data_line2.append(fre_sig.imag)
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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
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#print('data_input:\n',data_input)
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#print('data_line:\n',data_line)
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#print(len(data_input),len(data_input[0]))
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return data_input
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def GetFrequencyFeature2(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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window_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值
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wav_arr = np.array(wavsignal)
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#wav_length = len(wavsignal[0])
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wav_length = wav_arr.shape[1]
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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
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data_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据
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data_line = np.zeros((1, 400), dtype = np.float)
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for i in range(0, range0_end):
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p_start = i * 160
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p_end = p_start + 400
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data_line = wav_arr[0, p_start:p_end]
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'''
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x=np.linspace(0, 400 - 1, 400, dtype = np.int64)
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w = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗
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data_line = data_line * w # 加窗
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'''
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data_line = np.abs(fft(data_line)) / wav_length
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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
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#print(data_input.shape)
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return data_input
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x=np.linspace(0, 400 - 1, 400, dtype = np.int64)
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w = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗
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def GetFrequencyFeature3(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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window_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值
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wav_arr = np.array(wavsignal)
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#wav_length = len(wavsignal[0])
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wav_length = wav_arr.shape[1]
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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
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data_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据
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data_line = np.zeros((1, 400), dtype = np.float)
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for i in range(0, range0_end):
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p_start = i * 160
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p_end = p_start + 400
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data_line = wav_arr[0, p_start:p_end]
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data_line = data_line * w # 加窗
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data_line = np.abs(fft(data_line)) / wav_length
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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
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#print(data_input.shape)
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data_input = np.log(data_input + 1)
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return data_input
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def wav_scale(energy):
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'''
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语音信号能量归一化
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'''
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means = energy.mean() # 均值
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var=energy.var() # 方差
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e=(energy-means)/math.sqrt(var) # 归一化能量
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return e
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def wav_scale2(energy):
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'''
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语音信号能量归一化
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'''
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maxnum = max(energy)
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e = energy / maxnum
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return e
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def wav_scale3(energy):
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'''
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语音信号能量归一化
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'''
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for i in range(len(energy)):
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#if i == 1:
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# #print('wavsignal[0]:\n {:.4f}'.format(energy[1]),energy[1] is int)
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energy[i] = float(energy[i]) / 100.0
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#if i == 1:
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# #print('wavsignal[0]:\n {:.4f}'.format(energy[1]),energy[1] is int)
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return energy
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def wav_show(wave_data, fs): # 显示出来声音波形
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time = np.arange(0, len(wave_data)) * (1.0/fs) # 计算声音的播放时间，单位为秒
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# 画声音波形
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#plt.subplot(211)
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plt.plot(time, wave_data)
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#plt.subplot(212)
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#plt.plot(time, wave_data[1], c = "g")
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plt.show()
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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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list_wavmark=[] # 初始化wav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(' ')
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dic_filelist[txt_l[0]] = txt_l[1]
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list_wavmark.append(txt_l[0])
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txt_obj.close()
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return dic_filelist,list_wavmark
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def get_wav_symbol(filename):
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'''
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读取指定数据集中，所有wav文件对应的语音符号
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返回一个存储符号集的字典类型值
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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_symbol_list={} # 初始化字典
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list_symbolmark=[] # 初始化symbol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(' ')
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dic_symbol_list[txt_l[0]]=txt_l[1:]
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list_symbolmark.append(txt_l[0])
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txt_obj.close()
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return dic_symbol_list,list_symbolmark
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if(__name__=='__main__'):
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wave_data, fs = read_wav_data("A2_0.wav")
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wav_show(wave_data[0],fs)
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t0=time.time()
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freimg = GetFrequencyFeature3(wave_data,fs)
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t1=time.time()
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print('time cost:',t1-t0)
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freimg = freimg.T
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plt.subplot(111)
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plt.imshow(freimg)
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plt.colorbar(cax=None,ax=None,shrink=0.5)
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plt.show()
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