def ssc(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Spectral Subband Centroid features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 26.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
pspec = numpy.where(pspec == 0,numpy.finfo(float).eps,pspec) # if things are all zeros we get problems
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
R = numpy.tile(numpy.linspace(1,samplerate/2,numpy.size(pspec,1)),(numpy.size(pspec,0),1))
return numpy.dot(pspec*R,fb.T) / feat
def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Mel-filterbank energy features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between seccessive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 20.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: 2 values. The first is a numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The
second return value is the energy in each frame (total energy, unwindowed)
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
return feat,energy
def ssc(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Spectral Subband Centroid features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between seccessive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 20.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
R = numpy.tile(numpy.linspace(1,samplerate/2,numpy.size(pspec,1)),(numpy.size(pspec,0),1))
return numpy.dot(pspec*R,fb.T) / feat
def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Mel-filterbank energy features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 26.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: 2 values. The first is a numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The
second return value is the energy in each frame (total energy, unwindowed)
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
energy = numpy.where(energy == 0,numpy.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
feat = numpy.where(feat == 0,numpy.finfo(float).eps,feat) # if feat is zero, we get problems with log
return feat,energy
def feature_extract(wav_name, winlen=0.025, winstep=0.01):
"""This function returns (mfcc) feature vectors extracted from wav_name"""
rate, signal = wav.read(wav_name)
signal = numpy.sum(signal, axis=1)/signal.shape[1]
signal = sigproc.framesig(signal, rate*winlen, rate*winstep)
signal = vad.vad_filter(signal)
signal = sigproc.deframesig(signal, 0, rate*winlen, rate*winstep)
mfcc_feat = mfcc(signal, rate)
return mfcc_feat
def feature_extract(wav_name, winlen=0.025, winstep=0.01):
"""This function returns (mfcc) feature vectors extracted from wav_name"""
rate, signal = wav.read(wav_name)
signal = numpy.sum(signal, axis=1) / signal.shape[1]
signal = sigproc.framesig(signal, rate * winlen, rate * winstep)
signal = vad.vad_filter(signal)
signal = sigproc.deframesig(signal, 0, rate * winlen, rate * winstep)
mfcc_feat = mfcc(signal, rate)
return mfcc_feat
def doDet(fileIn):
(fsr,sig) = wav.read(fileIn)
segChunks,segTimes = framesig(sig,seglen*fs,segstp*fs,'box',1,fs)
if len(segChunks.shape) == 1:
segChunks = np.reshape(segChunks,1,segChunks.reshape[0])
#allOut = np.zeros((segChunks.shape[0],1))
allFeats = np.zeros((segChunks.shape[0],nComp))
#print allOut.shape
for t in range(segChunks.shape[0]):
seg = segChunks[t,:]
mfcc_feat,mspec,logmelspec = mfcc(seg,fs,winlen=wlen,winstep=wstep,numcep=ncep,nfilt=numfilt,nfft=fftsz,lowfreq=0,highfreq=fs/2,preemph=0.97,ceplifter=22,appendEnergy=True)
#if (math.isnan(numpy.sum(numpy.sum(mfcc_feat)))) or (math.isinf(numpy.sum(numpy.sum(mfcc_feat)))):
# print 'Escaping this Seg -- NaN or Inf occurres'
#else:
# numpy.savetxt(fltoread.replace('AllData',mfccset).rstrip('.wav')+'_POSITIVE_'+tlist[0]+'_'+tlist[1]+'_'+str(t)+'.mfcc',mfc\
# c_feat,delimiter=' ')
cdist=gmmmixt.predict_proba(mfcc_feat)
hist = np.sum(cdist,axis=0)
histfeat = hist/float(hist.shape[0])
histfeat = histfeat.reshape(1,histfeat.shape[0])
allFeats[t,:] = histfeat
histKer = computeKernel(allFeats,trdata,1.0)
kerId = np.arange(histKer.shape[0])+1
kerId = np.reshape(kerId,(kerId.shape[0],1))
teKer = map(list,np.hstack((kerId,histKer)))
telax = [0]*len(teKer)
plb,acc,probab = svm_predict(telax,teKer,svmMod,'-b 1 -q')
lbs = svmMod.get_labels()
#print str(probab) + str(lbs)
probab = np.array(probab)
if lbs[0] == 1:
prob_f=probab[:,0]
elif lbs[1] == 1:
prob_f=probab[:,1]
else:
print 'Not possible'
sys.exit()
prob_f.reshape(prob_f.shape[0],1)
if pOrc == 1:
allOut = prob_f
elif pOrc == 0:
allOut = np.array(map(int,(prob_f > opPoint))).reshape(prob_f.shape[0],1)
#print np.hstack((allOut,segTimes))
np.savetxt(fileIn.rstrip('.wav')+'_res'+'.txt',allOut)
return np.hstack((allOut,segTimes))
def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97,
winfunc=lambda x:numpy.ones((x,))):
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate, winfunc)
pspec = sigproc.powspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
energy = numpy.where(energy == 0,numpy.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = get_filterbanks(nfilt,nfft,samplerate,lowfreq,highfreq)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
feat = numpy.where(feat == 0,numpy.finfo(float).eps,feat) # if feat is zero, we get problems with log
return feat,energy
def get_lpcc(filename):
"""
gets lpccs for each frame in a wav file
filename: name of wav file with .wav
returns the lpcc features in each frame as a list of lists
"""
print "Getting LPCC"
(rate, sig) = wav.read(filename)
frames = sigproc.framesig(sig, 0.025 * rate, 0.01 * rate)
lpccs = [[]] * len(frames)
for x in xrange(0, len(frames)):
lpcc_feat = lpc(frames[x], 12)
for feature in lpcc_feat[0]:
feature = float(feature)
lpccs[x] = lpcc_feat[0]
return numpy.asarray(lpccs)
def mfcc(signal,samplerate=16000,winlen=0.025,winstep=0.01,numcep=13,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97,ceplifter=22,appendEnergy=True):
"""Compute MFCC features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param numcep: the number of cepstrum to return, default 13
:param nfilt: the number of filters in the filterbank, default 26.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:param ceplifter: apply a lifter to final cepstral coefficients. 0 is no lifter. Default is 22.
:param appendEnergy: if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy.
:returns: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
"""
# In fbank changed to do things on unique part of spectrum only i.e from frequency bins 1 to nfft/2+1
# change in sigproc to use hamming window by default
#MAKE SURE THAT nfft is even or next power of two after window length...in particular use something as NFFT=2^(ceil(log(winpts)/log(2)));
#feat,energy = fbank(signal,samplerate,winlen,winstep,nfilt,nfft,lowfreq,highfreq,preemph)
#K = nfft/2 + 1 # unique part of spectrum 0 to nfft/2 -- Already taken care of by numpy.fft.rfft -- returns unique part only
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate,'hamm')
pspec = sigproc.powspec(frames,nfft) # in this power spectrum computation normalization has been done..check 1/nfft factor..removed as of now
mspec = sigproc.magspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
energy = numpy.where(energy == 0,numpy.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = get_filterbanks(nfilt,nfft,samplerate,lowfreq,highfreq) # filter bank returned here is nfilt by nfft/2 + 1
featx = numpy.dot(pspec,fb.T) # compute the filterbank energies
featx = numpy.where(featx == 0,numpy.finfo(float).eps,featx) # if feat is zero, we get problems with log
feat = numpy.log(featx)
logmelspec = feat
feat = dct(feat, type=2, axis=1, norm='ortho')[:,:numcep]
feat = lifter(feat,ceplifter)
if appendEnergy: feat[:,0] = numpy.log(energy) # replace first cepstral coefficient with log of frame energy
return feat,mspec,logmelspec
def get_words(audio):
winlen = 0.01
winstep = 0.01
sample_rate = 44100
# in terms of number of 10 ms frames
start_speech = 10
end_silence = 5
speech_leader = 5
speech_trailer = 5
(rate, sig) = wav.read(audio)
frames = framesig(sig, winlen * sample_rate, winstep * sample_rate,
lambda x: numpy.ones((1, x)))
word_list = []
#calculate energy per frame and zcr per frame
frame_energy = []
zcr = []
for i in range(0, len(frames)):
energy = sum(1.0 * x * x for x in frames[i])
zc = 0
for j in range(1, len(frames[i])):
if (frames[i][j] < 0
and frames[i][j - 1] > 0) or (frames[i][j] > 0
and frames[i][j - 1] < 0):
zc = zc + 1
frame_energy.append(energy)
zcr.append(zc)
#calculate final noise value
avg_energy = sum(1.0 * x for x in frame_energy[0:9])
avg_energy = avg_energy / 10
avg_zcr = sum(1.0 * x for x in frame_energy[0:9])
avg_zcr = avg_zcr / 10
#calculate threshold
upper_energy_threshold = 2 * avg_energy
upper_zcr_threshold = 2 * avg_zcr
lower_energy_threshold = 0.75 * avg_energy
lower_zcr_threshold = 0.75 * avg_zcr
'''
print upper_energy_threshold
print upper_zcr_threshold
print lower_energy_threshold
print lower_zcr_threshold
'''
started = False
start_index = 0
start_cnt = 0
stop_index = 0
stop_cnt = 0
words = 0
#print ">> START_FRAME END_FRAME LENGTH_IN_SECONDS"
for i in range(0, len(frames[10:])):
if not started:
if frame_energy[i] > upper_energy_threshold or zcr[
i] > upper_zcr_threshold:
start_cnt += 1
else:
start_cnt = 0
if start_cnt == start_speech:
started = True
start_index = i - start_speech + 1 - speech_leader
start_index = max(0, start_index)
else:
if frame_energy[i] > upper_energy_threshold or zcr[
i] > upper_zcr_threshold:
stop_cnt = 0
else:
stop_cnt += 1
if stop_cnt == end_silence:
stop_index = i - end_silence + 1 + speech_trailer
stop_index = min(len(frames) - 1, stop_index)
#print ">>", start_index, stop_index, (stop_index - start_index + 1 ) * 10
wav.write("word" + str(words) + ".wav", rate,
sig[441 * start_index:441 * (stop_index + 1)])
words += 1
started = False
start_index = start_cnt = 0
stop_index = stop_cnt = 0
if started:
stop_index = len(frames) - 1
#print ">>", start_index, stop_index, (stop_index - start_index + 1 ) * 10
wav.write("word" + str(words) + ".wav", rate,
sig[441 * start_index:441 * (stop_index + 1)])
words += 1
return words
def chop(output_folder='chopped-words', audio_file='recording.wav'):
global voiced, sig, lrms, min_signal
(rate, sig) = wav.read(audio_file)
frames = framesig(sig, winlen * sample_rate, winstep * sample_rate,
lambda x: np.ones((1, x)))
lrms = [log_root_mean_square(x) for x in frames]
min_signal = np.mean(lrms) / 2
# min_signal = 0
# print min_signal
# dtype=int16 must for writing to wav file
# result = np.array([], dtype=np.int16)
for i in range(0, len(frames)):
if classify(i):
#result = np.append(result, sig[441*i : 441*(i+1)])
voiced.append(True)
else:
voiced.append(False)
started = False
start_index = 0
start_cnt = 0
stop_index = 0
stop_cnt = 0
words = 0
print ">> START_FRAME END_FRAME LENGTH_IN_SECONDS"
for i in range(0, len(frames)):
if not started:
if voiced[i]:
start_cnt += 1
else:
start_cnt = 0
if start_cnt == start_speech:
started = True
start_index = i - start_speech + 1 - speech_leader
start_index = max(0, start_index)
else:
if voiced[i]:
stop_cnt = 0
else:
stop_cnt += 1
if stop_cnt == end_silence:
stop_index = i - end_silence + 1 + speech_trailer
stop_index = min(len(frames) - 1, stop_index)
print ">>", start_index, stop_index, (stop_index -
start_index + 1) * 10
wav.write(output_folder + "/word" + str(words) + ".wav", rate,
sig[441 * start_index:441 * (stop_index + 1)])
#wav.write("word" + str(words) + ".wav",rate, get_signal(start_index , stop_index))
words += 1
started = False
start_index = start_cnt = 0
stop_index = stop_cnt = 0
if started:
stop_index = len(frames) - 1
print ">>", start_index, stop_index, (stop_index - start_index +
1) * 10
wav.write(output_folder + "/word" + str(words) + ".wav", rate,
sig[441 * start_index:441 * (stop_index + 1)])
#wav.write("word" + str(words) + ".wav",rate, get_signal(start_index , stop_index))
words += 1
return words
def logFilterbankFeatures(signal,
samplerate=16000,
winlen=0.0255,
winstep=0.01,
nfilt=40,
nfft=512,
lowfreq=133.3333,
highfreq=6855.4976,
preemph=0.97,
winSzForDelta=2):
'''
Computes log filterbank energies on a mel scale + total energy using
with the code taken from features.fbank, which does not accept
window function as a param.
function from package 'python_speech_features', see
http://python-speech-features.readthedocs.org/en/latest/ or
https://github.com/jameslyons/python_speech_features
Therefore it calculates the FFT of the signal and sums the the weighted
bins, distributed on a mel scale. Weighting is done with tri-angular filters.
For these filter energies + total energy, deltas are calculated.
:parameters:
- signal : np.ndarray, dtype=float
input vector of the speech signal
- samplerate : int
- winlen: float
length of analysis window in seconds
- winstep: float
step size between successive windows in seconds
- nfilt: int
number of filter energies to compute (total energy not included).
e.g. 40 --> Output dim = (40+1)*3
- nfft: int
FFT size
- lowfreq: int
lower end on mel frequency scale, on which filter banks are distributed
- highfreq: int
upper end on mel frequency scale, on which filter banks are distributed
- preemph: float
pre-emphasis coefficient
- deltafeat: np.ndarray, dtype=float
deltas of the input features
- winSzForDelta: int
window size for computing deltas. E.g. 2 --> t-2, t-1, t+1 and t+2 are
for calculating the deltas
:returns:
- features: numpy.array: float
feature-matrix. 1st dimension: time steps of 'winstep',
2nd dim: feature dimension: (nfilt + 1)*3,
+1 for energy, *3 because of deltas
'''
# Part of the following code is copied from function features.fbank
# Unfortunately, one can't specify the window function in features.fbank
# Hamming window is used here
highfreq = highfreq or samplerate / 2
signal = sigproc.preemphasis(signal, preemph)
frames = sigproc.framesig(signal,
winlen * samplerate,
winstep * samplerate,
winfunc=hamming)
pspec = sigproc.powspec(frames, nfft)
energy = np.sum(pspec, 1) # this stores the total energy in each frame
energy = np.where(energy == 0,
np.finfo(float).eps,
energy) # if energy is zero, we get problems with log
fb = features.get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)
feat = np.dot(pspec, fb.T) # compute the filterbank energies
feat = np.where(feat == 0,
np.finfo(float).eps,
feat) # if feat is zero, we get problems with log
# Use log feature bank and log energy
feat = np.column_stack((np.log(energy), np.log(feat)))
# calculate delta and acceleration
deltaFeat = delta(feat, winSzForDelta)
accFeat = delta(deltaFeat, winSzForDelta)
# stack features + delta + acceleration
return np.concatenate((feat, deltaFeat, accFeat), axis=1)
def get_words(audio):
(rate,sig) = wav.read(audio)
frames = framesig(sig, winlen*sample_rate, winstep*sample_rate,lambda x:numpy.ones((1,x)))
word_list = []
#calculate energy per frame and zcr per frame
frame_energy = []
zcr = []
for i in range(0,len(frames)):
energy = sum(1.0*x*x for x in frames[i])
zc = 0
for j in range(1,len(frames[i])):
if (frames[i][j]<0 and frames[i][j-1]>0) or (frames[i][j]>0 and frames[i][j-1]<0):
zc = zc + 1
frame_energy.append(energy)
zcr.append(zc)
#calculate final noise value
avg_energy = sum(1.0*x for x in frame_energy[0:9])
avg_energy = avg_energy/10
avg_zcr = sum(1.0*x for x in frame_energy[0:9])
avg_zcr = avg_zcr/10
#calculate threshold
upper_energy_threshold = 2*avg_energy
upper_zcr_threshold = 2*avg_zcr
lower_energy_threshold = 0.75*avg_energy
lower_zcr_threshold = 0.75*avg_zcr
print upper_energy_threshold
print upper_zcr_threshold
print lower_energy_threshold
print lower_zcr_threshold
started = False
start_index = 0
start_cnt = 0
stop_index = 0
stop_cnt = 0
words = 0
print ">> START_FRAME END_FRAME LENGTH_IN_SECONDS"
for i in range(0,len(frames[10:])):
if not started:
if frame_energy[i]>upper_energy_threshold or zcr[i]>upper_zcr_threshold:
start_cnt += 1
else:
start_cnt = 0
if start_cnt == start_speech:
started = True
start_index = i - start_speech + 1 - speech_leader
start_index = max(0, start_index)
else:
if frame_energy[i]>upper_energy_threshold or zcr[i]>upper_zcr_threshold:
stop_cnt = 0
else:
stop_cnt += 1
if stop_cnt == end_silence:
stop_index = i - end_silence + 1 + speech_trailer
stop_index = min(len(frames)-1, stop_index)
print ">>", start_index, stop_index, (stop_index - start_index + 1 ) * 10
wav.write("word" + str(words) + ".wav",rate,sig[441*start_index:441*(stop_index+1)])
words += 1
started = False
start_index = start_cnt = 0
stop_index = stop_cnt = 0
if started:
stop_index = len(frames)-1
print ">>", start_index, stop_index, (stop_index - start_index + 1 ) * 10
wav.write("word" + str(words) + ".wav",rate,sig[441*start_index:441*(stop_index+1)])
words+=1
return words
def chop(output_folder = 'chopped-words', audio_file = 'recording.wav'):
global voiced, sig, lrms, min_signal
(rate,sig) = wav.read(audio_file)
frames = framesig(sig, winlen*sample_rate, winstep*sample_rate,lambda x:np.ones((1,x)))
lrms = [log_root_mean_square(x) for x in frames]
min_signal = np.mean(lrms) / 2
# min_signal = 0
# print min_signal
# dtype=int16 must for writing to wav file
# result = np.array([], dtype=np.int16)
for i in range(0,len(frames)):
if classify(i):
#result = np.append(result, sig[441*i : 441*(i+1)])
voiced.append(True)
else:
voiced.append(False)
started = False
start_index = 0
start_cnt = 0
stop_index = 0
stop_cnt = 0
words = 0
print ">> START_FRAME END_FRAME LENGTH_IN_SECONDS"
for i in range(0,len(frames)):
if not started:
if voiced[i]:
start_cnt += 1
else:
start_cnt = 0
if start_cnt == start_speech:
started = True
start_index = i - start_speech + 1 - speech_leader
start_index = max(0, start_index)
else:
if voiced[i]:
stop_cnt = 0
else:
stop_cnt += 1
if stop_cnt == end_silence:
stop_index = i - end_silence + 1 + speech_trailer
stop_index = min(len(frames)-1, stop_index)
print ">>", start_index, stop_index, (stop_index - start_index + 1 ) * 10
wav.write(output_folder + "/word" + str(words) + ".wav",rate, sig[441*start_index : 441*(stop_index+1)])
#wav.write("word" + str(words) + ".wav",rate, get_signal(start_index , stop_index))
words += 1
started = False
start_index = start_cnt = 0
stop_index = stop_cnt = 0
if started:
stop_index = len(frames)-1
print ">>", start_index, stop_index, (stop_index - start_index + 1 ) * 10
wav.write(output_folder + "/word" + str(words) + ".wav",rate, sig[441*start_index : 441*(stop_index+1)])
#wav.write("word" + str(words) + ".wav",rate, get_signal(start_index , stop_index))
words += 1
return words
# Inicia extracción de caracteristicas a nivel de segmento
start = time.time()
window = 2
allUt = []
allLabels = []
utteranceByFeat = []
labelsByFeat = []
portionSelection = 1.0
for utterance in sessionTrain:
features = []
labels = []
if np.random.uniform(1, 0) < portionSelection:
label = utterance[1]
channel1, channel2 = zip(*utterance[0])
samplesSize = 1600
samplesChn1 = sigproc.framesig(channel1, samplesSize, np.ceil(samplesSize * 0.1))
samplesChn2 = sigproc.framesig(channel2, samplesSize, np.ceil(samplesSize * 0.1))
allFeaturesVector = []
for i in range(0, len(samplesChn1)):
sampleLeft = samplesChn1[i]
sampleRight = samplesChn2[i]
currentFeaturesLeft = calcFeaturesVector(sampleLeft, 16000)
currentFeaturesRight = calcFeaturesVector(sampleRight, 16000)
allFeaturesVector.append(currentFeaturesLeft + currentFeaturesRight)
bound = 2 * window + 1
if i >= (bound):
features.append(np.concatenate(allFeaturesVector[(i - bound) : i], axis=0))
allUt.append(np.concatenate(allFeaturesVector[(i - bound) : i], axis=0))
labels.append(encodeLabels(label))
allLabels.append(encodeLabels(label))
def logFilterbankFeatures(signal,samplerate=16000,winlen=0.0255,winstep=0.01,
nfilt=40,nfft=512,lowfreq=133.3333,highfreq=6855.4976,preemph=0.97,
winSzForDelta=2):
'''
Computes log filterbank energies on a mel scale + total energy using
with the code taken from features.fbank, which does not accept
window function as a param.
function from package 'python_speech_features', see
http://python-speech-features.readthedocs.org/en/latest/ or
https://github.com/jameslyons/python_speech_features
Therefore it calculates the FFT of the signal and sums the the weighted
bins, distributed on a mel scale. Weighting is done with tri-angular filters.
For these filter energies + total energy, deltas are calculated.
:parameters:
- signal : np.ndarray, dtype=float
input vector of the speech signal
- samplerate : int
- winlen: float
length of analysis window in seconds
- winstep: float
step size between successive windows in seconds
- nfilt: int
number of filter energies to compute (total energy not included).
e.g. 40 --> Output dim = (40+1)*3
- nfft: int
FFT size
- lowfreq: int
lower end on mel frequency scale, on which filter banks are distributed
- highfreq: int
upper end on mel frequency scale, on which filter banks are distributed
- preemph: float
pre-emphasis coefficient
- deltafeat: np.ndarray, dtype=float
deltas of the input features
- winSzForDelta: int
window size for computing deltas. E.g. 2 --> t-2, t-1, t+1 and t+2 are
for calculating the deltas
:returns:
- features: numpy.array: float
feature-matrix. 1st dimension: time steps of 'winstep',
2nd dim: feature dimension: (nfilt + 1)*3,
+1 for energy, *3 because of deltas
'''
# Part of the following code is copied from function features.fbank
# Unfortunately, one can't specify the window function in features.fbank
# Hamming window is used here
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate,winfunc=hamming)
pspec = sigproc.powspec(frames,nfft)
energy = np.sum(pspec,1) # this stores the total energy in each frame
energy = np.where(energy == 0,np.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = features.get_filterbanks(nfilt,nfft,samplerate,lowfreq,highfreq)
feat = np.dot(pspec,fb.T) # compute the filterbank energies
feat = np.where(feat == 0,np.finfo(float).eps,feat) # if feat is zero, we get problems with log
# Use log feature bank and log energy
feat = np.column_stack((np.log(energy),np.log(feat)))
# calculate delta and acceleration
deltaFeat = delta(feat, winSzForDelta)
accFeat = delta(deltaFeat, winSzForDelta)
# stack features + delta + acceleration
return np.concatenate((feat,deltaFeat,accFeat),axis=1)
