add read words list and signal scale
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nl8590687
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ab74ee4bfc
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2217754bf0
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@ -2,3 +2,4 @@
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*.model
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[Mm]odel_speech/
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*.wav
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@ -25,3 +25,7 @@ LSTM + CNN
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基于概率图的马尔可夫模型
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## Log
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日志
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链接：[进展日志](https://github.com/nl8590687/ASRT_SpeechRecognition/blob/master/log.md)
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@ -50,14 +50,20 @@ def fbank(signal, samplerate, conf):
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Compute fbank features from an audio signal.
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从一个声音信号中计算fbank特征向量
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Args:
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参数：
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signal: the audio signal from which to compute features. Should be an
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N*1 array
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要计算特征的声音信号，一个N*1维的数组
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samplerate: the samplerate of the signal we are working with.
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要处理信号的采样率
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conf: feature configuration
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特征的配置
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Returns:
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返回值：
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A numpy array of size (NUMFRAMES by nfilt) containing features, a numpy
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vector containing the signal energy
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返回一个包含特征向量的numpy数组，一个包含信号能量的numpy向量
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'''
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highfreq = int(conf['highfreq'])
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@ -5,6 +5,7 @@ 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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def read_wav_data(filename):
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'''
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@ -20,14 +21,27 @@ def read_wav_data(filename):
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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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time = np.arange(0, num_frame) * (1.0/framerate) # 计算声音的播放时间，单位为秒
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return wave_data, time
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wave_data = wave_data
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return wave_data, framerate
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def wav_show(wave_data, time): # 显示出来声音波形
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#wave_data, time = read_wave_data("C:\\Users\\nl\\Desktop\\A2_0.wav")
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#draw the wave
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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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sum=0
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for i in energy:
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sum=sum+i*i
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length=len(energy)
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print(length,sum)
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m=math.sqrt(length/sum)
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e=energy*m
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return e
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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[0])
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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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@ -53,11 +67,24 @@ def get_wav_symbol(filename):
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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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print('test')
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#if(__name__=='__main__'):
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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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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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return dic_symbol_list
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if(__name__=='__main__'):
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#dic=get_wav_symbol('E:\\语音数据集\\doc\\doc\\trans\\train.phone.txt')
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#print(dic)
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#dic=get_wav_list('E:\\语音数据集\\doc\\doc\\list\\train.wav.lst')
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#for i in dic:
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#print(i,dic[i])
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#wave_data, time = read_wav_data("C:\\Users\\nl\\Desktop\\A2_0.wav")
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#wav_show(wave_data,time)
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wave_data, fs = read_wav_data("A2_0.wav")
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wave_data[0]=wav_scale(wave_data[0])
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#print(fs)
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wav_show(wave_data[0],fs)
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@ -0,0 +1,191 @@
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'''@file sigproc.py
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contains the signal processing functionality
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The MIT License (MIT)
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Copyright (c) 2013 James Lyons
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Permission is hereby granted, free of charge, to any person obtaining a copy of
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this software and associated documentation files (the "Software"), to deal in
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the Software without restriction, including without limitation the rights to
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use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
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the Software, and to permit persons to whom the Software is furnished to do so,
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subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
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CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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This file includes routines for basic signal processing including framing and
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computing power spectra.
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Author: James Lyons 2012
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'''
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import math
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import numpy
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def framesig(sig, frame_len, frame_step, winfunc=lambda x: numpy.ones((x, ))):
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'''
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Frame a signal into overlapping frames.
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Args:
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sig: the audio signal to frame.
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frame_len: length of each frame measured in samples.
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frame_step: number of samples after the start of the previous frame that
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the next frame should begin.
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winfunc: the analysis window to apply to each frame. By default no
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window is applied.
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Returns:
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an array of frames. Size is NUMFRAMES by frame_len.
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'''
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slen = len(sig)
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frame_len = int(round(frame_len))
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frame_step = int(round(frame_step))
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if slen <= frame_len:
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numframes = 1
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else:
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numframes = 1 + int(math.ceil((1.0*slen - frame_len)/frame_step))
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padlen = int((numframes-1)*frame_step + frame_len)
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zeros = numpy.zeros((padlen - slen,))
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padsignal = numpy.concatenate((sig, zeros))
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indices = (numpy.tile(numpy.arange(0, frame_len), (numframes, 1))
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+ numpy.tile(numpy.arange(0, numframes*frame_step, frame_step),
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(frame_len, 1)).T)
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indices = numpy.array(indices, dtype=numpy.int32)
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frames = padsignal[indices]
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win = numpy.tile(winfunc(frame_len), (numframes, 1))
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return frames*win
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def deframesig(frames, siglen, frame_len, frame_step,
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winfunc=lambda x: numpy.ones((x, ))):
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'''
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Does overlap-add procedure to undo the action of framesig.
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Args:
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frames the: array of frames.
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siglen the: length of the desired signal, use 0 if unknown. Output will
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be truncated to siglen samples.
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frame_len: length of each frame measured in samples.
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frame_step: number of samples after the start of the previous frame that
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the next frame should begin.
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winfunc: the analysis window to apply to each frame. By default no
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window is applied.
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Returns:
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a 1-D signal.
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'''
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frame_len = round(frame_len)
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frame_step = round(frame_step)
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numframes = numpy.shape(frames)[0]
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assert numpy.shape(frames)[1] == frame_len, '''"frames" matrix is wrong
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size, 2nd dim is not equal to frame_len'''
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indices = (numpy.tile(numpy.arange(0, frame_len), (numframes, 1))
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+ numpy.tile(numpy.arange(0, numframes*frame_step, frame_step),
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(frame_len, 1)).T)
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indices = numpy.array(indices, dtype=numpy.int32)
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padlen = (numframes-1)*frame_step + frame_len
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if siglen <= 0:
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siglen = padlen
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rec_signal = numpy.zeros((padlen, ))
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window_correction = numpy.zeros((padlen, ))
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win = winfunc(frame_len)
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for i in range(0, numframes):
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#add a little bit so it is never zero
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window_correction[indices[i, :]] = (window_correction[indices[i, :]]
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+ win + 1e-15)
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rec_signal[indices[i, :]] = rec_signal[indices[i, :]] + frames[i, :]
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rec_signal = rec_signal/window_correction
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return rec_signal[0:siglen]
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def magspec(frames, nfft):
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'''
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Compute the magnitude spectrum of each frame in frames.
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If frames is an NxD matrix, output will be NxNFFT.
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Args:
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frames: the array of frames. Each row is a frame.
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nfft: the FFT length to use. If NFFT > frame_len, the frames are
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zero-padded.
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Returns:
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If frames is an NxD matrix, output will be NxNFFT. Each row will be the
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magnitude spectrum of the corresponding frame.
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'''
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complex_spec = numpy.fft.rfft(frames, nfft)
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return numpy.absolute(complex_spec)
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def powspec(frames, nfft):
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'''
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Compute the power spectrum of each frame in frames.
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If frames is an NxD matrix, output will be NxNFFT.
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Args:
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frames: the array of frames. Each row is a frame.
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nfft: the FFT length to use. If NFFT > frame_len, the frames are
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zero-padded.
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Returns:
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If frames is an NxD matrix, output will be NxNFFT. Each row will be the
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power spectrum of the corresponding frame.
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'''
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return 1.0/nfft * numpy.square(magspec(frames, nfft))
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def logpowspec(frames, nfft, norm=1):
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'''
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Compute the log power spectrum of each frame in frames.
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If frames is an NxD matrix, output will be NxNFFT.
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Args:
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frames: the array of frames. Each row is a frame.
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nfft: the FFT length to use. If NFFT > frame_len, the frames are
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zero-padded.
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norm: If norm=1, the log power spectrum is normalised so that the max
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value (across all frames) is 1.
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Returns:
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If frames is an NxD matrix, output will be NxNFFT. Each row will be the
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log power spectrum of the corresponding frame.
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'''
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ps = powspec(frames, nfft)
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ps[ps <= 1e-30] = 1e-30
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lps = 10*numpy.log10(ps)
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if norm:
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return lps - numpy.max(lps)
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else:
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return lps
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def preemphasis(signal, coeff=0.95):
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'''
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perform preemphasis on the input signal.
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Args:
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signal: The signal to filter.
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coeff: The preemphasis coefficient. 0 is no filter, default is 0.95.
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Returns:
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the filtered signal.
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'''
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return numpy.append(signal[0], signal[1:]-coeff*signal[:-1])
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2
log.md
2
log.md
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@ -8,5 +8,7 @@
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如果有什么问题，团队内部需要在这里直接写出来
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## Log
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### 2017-08-28
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开始准备制作语音信号处理方面的功能
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### 2017-08-22
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准备使用Keras基于LSTM/CNN尝试实现
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4
main.py
4
main.py
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@ -36,7 +36,7 @@ class ModelSpeech(): # 语音模型类
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return _model
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def TrainModel(self,datas,epoch = 2,save_step=5000,filename='model_speech/LSTM_CNN_model'): # 训练模型
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print('test')
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pass
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def LoadModel(self,filename='model_speech/LSTM_CNN_model'): # 加载模型参数
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self._model.load_weights(filename)
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@ -45,7 +45,7 @@ class ModelSpeech(): # 语音模型类
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self._model.save_weights(filename+'.model')
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def TestModel(self): # 测试检验模型效果
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print('test')
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pass
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@property
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def model(self): # 返回keras model
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