def training(nfiltbank, orderLPC):
nSpeaker = 2
nCentroid = 16
codebooks_mfcc = np.empty((nSpeaker, nfiltbank, nCentroid))
codebooks_lpc = np.empty((nSpeaker, orderLPC, nCentroid))
directory = os.getcwd() + '/train'
fname = str()
for i in range(nSpeaker):
fname = '/speaker' + str(i + 1) + '.wav'
print('Now speaker ', str(i + 1), 'features are being trained')
(fs, s) = read(directory + fname)
# read the wav file specified as first command line arg
s = wav_to_floats(directory + fname)
s = s[:48000]
sd.play(s, fs)
mel_coeff = mfcc(s, fs, nfiltbank)
lpc_coeff = lpc(s, fs, orderLPC)
codebooks_mfcc[i, :, :] = lbg(mel_coeff, nCentroid)
codebooks_lpc[i, :, :] = lbg(lpc_coeff, nCentroid)
codebooks = np.empty((2, nfiltbank, nCentroid))
mel_coeff = np.empty((2, nfiltbank, 68))
for i in range(2):
fname = '/speaker' + str(i + 1) + '.wav'
(fs, s) = read(directory + fname)
s = s[:48000]
mel_coeff[i, :, :] = mfcc(s, fs, nfiltbank)[:, 0:68]
codebooks[i, :, :] = lbg(mel_coeff[i, :, :], nCentroid)
plt.figure(nSpeaker + 1)
s1 = plt.scatter(mel_coeff[0, 6, :],
mel_coeff[0, 4, :],
s=100,
color='r',
marker='o')
c1 = plt.scatter(codebooks[0, 6, :],
codebooks[0, 4, :],
s=100,
color='r',
marker='+')
s2 = plt.scatter(mel_coeff[1, 6, :],
mel_coeff[1, 4, :],
s=100,
color='b',
marker='o')
c2 = plt.scatter(codebooks[1, 6, :],
codebooks[1, 4, :],
s=100,
color='b',
marker='+')
plt.grid()
plt.legend((s1, s2, c1, c2),
('Child', 'Parent', 'Child centroids', 'Parent centroids'),
scatterpoints=1,
loc='upper left')
plt.show()
return (codebooks_mfcc, codebooks_lpc)
def training(nfiltbank, orderLPC):
#Get directory and list of *.wav files
directory = os.getcwd() + '/train_all_speakers'
wave_files = [f for f in os.listdir(directory)]
nSpeaker = len(wave_files)
nCentroid = 32 # original is 16
codebooks_mfcc = np.empty((nSpeaker, nfiltbank, nCentroid))
codebooks_lpc = np.empty((nSpeaker, orderLPC, nCentroid))
for i, wave_file in enumerate(wave_files):
fname = '/' + wave_file
print 'Speaker [' + str(
i) + '] File:' + wave_file + ' Training features...'
(fs, s) = read(directory + fname)
mel_coeff = mfcc_p(s, fs)
mel_coeff = mel_coeff.transpose()
mel_coeff[0, :] = np.zeros(mel_coeff.shape[1])
lpc_coeff = lpc(s, fs, orderLPC)
codebooks_mfcc[i, :, :] = lbg(mel_coeff, nCentroid)
codebooks_lpc[i, :, :] = lbg(lpc_coeff, nCentroid)
print('Training finished\n')
return (codebooks_mfcc, codebooks_lpc)
def training(nfiltbank, orderLPC):
nSpeaker = 8
nCentroid = 16
codebooks_mfcc = np.empty((nSpeaker,nfiltbank,nCentroid))
codebooks_lpc = np.empty((nSpeaker, orderLPC, nCentroid))
directory = os.getcwd() + '/train';
fname = str()
for i in range(nSpeaker):
fname = '/s' + str(i+1) + '.wav'
print('Now speaker ', str(i+1), 'features are being trained' )
(fs,s) = read(directory + fname)
mel_coeff = mfcc(s, fs, nfiltbank)
lpc_coeff = lpc(s, fs, orderLPC)
codebooks_mfcc[i,:,:] = lbg(mel_coeff, nCentroid)
codebooks_lpc[i,:,:] = lbg(lpc_coeff, nCentroid)
plt.figure(i)
plt.title('Codebook for speaker ' + str(i+1) + ' with ' + str(nCentroid) + ' centroids')
for j in range(nCentroid):
plt.subplot(211)
plt.stem(codebooks_mfcc[i,:,j])
plt.ylabel('MFCC')
plt.subplot(212)
markerline, stemlines, baseline = plt.stem(codebooks_lpc[i,:,j])
plt.setp(markerline,'markerfacecolor','r')
plt.setp(baseline,'color', 'k')
plt.ylabel('LPC')
plt.axis(ymin = -1, ymax = 1)
plt.xlabel('Number of features')
plt.show()
print('Training complete')
#plotting 5th and 6th dimension MFCC features on a 2D plane
#comment lines 54 to 71 if you don't want to see codebook
codebooks = np.empty((2, nfiltbank, nCentroid))
mel_coeff = np.empty((2, nfiltbank, 68))
for i in range(2):
fname = '/s' + str(i+2) + '.wav'
(fs,s) = read(directory + fname)
mel_coeff[i,:,:] = mfcc(s, fs, nfiltbank)[:,0:68]
codebooks[i,:,:] = lbg(mel_coeff[i,:,:], nCentroid)
plt.figure(nSpeaker + 1)
s1 = plt.scatter(mel_coeff[0,6,:], mel_coeff[0,4,:],s = 100, color = 'r', marker = 'o')
c1 = plt.scatter(codebooks[0,6,:], codebooks[0,4,:], s = 100, color = 'r', marker = '+')
s2 = plt.scatter(mel_coeff[1,6,:], mel_coeff[1,4,:],s = 100, color = 'b', marker = 'o')
c2 = plt.scatter(codebooks[1,6,:], codebooks[1,4,:], s = 100, color = 'b', marker = '+')
plt.grid()
plt.legend((s1, s2, c1, c2), ('Sp1','Sp2','Sp1 centroids', 'Sp2 centroids'), scatterpoints = 1, loc = 'upper left')
plt.show()
return (codebooks_mfcc, codebooks_lpc)
def training(nfiltbank, orderLPC):
nSpeaker = 8
nCentroid = 16
codebooks_mfcc = np.empty((nSpeaker,nfiltbank,nCentroid))
codebooks_lpc = np.empty((nSpeaker, orderLPC, nCentroid))
directory = os.getcwd() + '/train';
fname = str()
print('Speakers features are being trained.Please wait....')
for i in range(nSpeaker):
fname = '/s' + str(i+1) + '.wav'
#print('Now speaker ', str(i+1), 'features are being trained' )
(fs,s) = read(directory + fname)
mel_coeff = mfcc(s, fs, nfiltbank)
lpc_coeff = lpc(s, fs, orderLPC)
codebooks_mfcc[i,:,:] = lbg(mel_coeff, nCentroid)
codebooks_lpc[i,:,:] = lbg(lpc_coeff, nCentroid)
plt.figure(i)
plt.title('Codebook for speaker ' + str(i+1) + ' with ' + str(nCentroid) + ' centroids')
for j in range(nCentroid):
plt.subplot(211)
plt.stem(codebooks_mfcc[i,:,j])
plt.ylabel('MFCC')
plt.subplot(212)
markerline, stemlines, baseline = plt.stem(codebooks_lpc[i,:,j])
plt.setp(markerline,'markerfacecolor','r')
plt.setp(baseline,'color', 'k')
plt.ylabel('LPC')
plt.axis(ymin = -1, ymax = 1)
plt.xlabel('Number of features')
plt.show()
print('Training complete')
#plotting 5th and 6th dimension MFCC features on a 2D plane
#comment lines 54 to 71 if you don't want to see codebook
codebooks = np.empty((2, nfiltbank, nCentroid))
mel_coeff = np.empty((2, nfiltbank, 68))
for i in range(2):
fname = '/s' + str(i+2) + '.wav'
(fs,s) = read(directory + fname)
mel_coeff[i,:,:] = mfcc(s, fs, nfiltbank)[:,0:68]
codebooks[i,:,:] = lbg(mel_coeff[i,:,:], nCentroid)
plt.figure(nSpeaker + 1)
s1 = plt.scatter(mel_coeff[0,6,:], mel_coeff[0,4,:],s = 100, color = 'r', marker = 'o')
c1 = plt.scatter(codebooks[0,6,:], codebooks[0,4,:], s = 100, color = 'r', marker = '+')
s2 = plt.scatter(mel_coeff[1,6,:], mel_coeff[1,4,:],s = 100, color = 'b', marker = 'o')
c2 = plt.scatter(codebooks[1,6,:], codebooks[1,4,:], s = 100, color = 'b', marker = '+')
plt.grid()
plt.legend((s1, s2, c1, c2), ('Sp1','Sp2','Sp1 centroids', 'Sp2 centroids'), scatterpoints = 1, loc = 'upper left')
plt.show()
return (codebooks_mfcc, codebooks_lpc)
def training(nfiltbank, orderLPC):
trainingSet = 4
nCentroid = 16
cbMfcc = np.empty((trainingSet,nfiltbank,nCentroid))
cbLpc = np.empty((trainingSet, orderLPC, nCentroid))
directory = os.getcwd() + '/train';
fname = str()
for i in range(trainingSet):
fname = '/s' + str(i+1) + '.wav'
print 'Voice ', str(i+1), 'is trained'
(fs,s) = read(directory + fname)
MFCC = MFCC_Coeff(s, fs, nfiltbank)
lpc_coeff = lpc(s, fs, orderLPC)
cbMfcc[i,:,:] = lbg(MFCC, nCentroid)
cbLpc[i,:,:] = lbg(lpc_coeff, nCentroid)
plt.figure(i)
plt.title('Codebook for speaker ' + str(i+1) + ' with ' + str(nCentroid) + ' centroids')
for j in range(nCentroid):
plt.subplot(211)
plt.stem(cbMfcc[i,:,j])
plt.ylabel('MFCC')
plt.subplot(212)
markerline, stemlines, baseline = plt.stem(cbLpc[i,:,j])
plt.setp(markerline,'markerfacecolor','r')
plt.setp(baseline,'color', 'k')
plt.ylabel('LPC')
plt.axis(ymin = -1, ymax = 1)
plt.xlabel('Number of features')
plt.show()
print 'Training has been performed '
codebooks = np.empty((2, nfiltbank, nCentroid))
MFCC = np.empty((2, nfiltbank, 68))
for i in range(2):
fname = '/s' + str(i+2) + '.wav'
(fs,s) = read(directory + fname)
MFCC[i,:,:] = MFCC_Coeff(s, fs, nfiltbank)[:,0:68]
codebooks[i,:,:] = lbg(MFCC[i,:,:], nCentroid)
plt.figure(trainingSet + 1)
s1 = plt.scatter(MFCC[0,6,:], MFCC[0,4,:],s = 100, color = 'r', marker = 'o')
c1 = plt.scatter(codebooks[0,6,:], codebooks[0,4,:], s = 100, color = 'r', marker = '+')
s2 = plt.scatter(MFCC[1,6,:], MFCC[1,4,:],s = 100, color = 'b', marker = 'o')
c2 = plt.scatter(codebooks[1,6,:], codebooks[1,4,:], s = 100, color = 'b', marker = '+')
plt.grid()
plt.legend((s1, s2, c1, c2), ('Sp1','Sp2','Sp1 centroids', 'Sp2 centroids'), scatterpoints = 1, loc = 'upper left')
plt.show()
return (cbMfcc, cbLpc)
def training(filtbankN):
Centroids = 16
SpeakerN = 12
file = str()
codebooks_org = np.empty(
(SpeakerN, filtbankN, Centroids)) #np.empty((층, 행, 열), dtype)
#12개의 13행 16열 배열 생성
for i in range(SpeakerN):
file = 'Speaker' + str(i + 1) + '.wav' #file 읽어오기
(fs, sig) = read(file) # fs = sample rate, sig = array data
melcoeffs = MFCC(sig, fs, 0.025, 0.01, 13, 40) # --------TESTING
#mel feature을 생성하여 배열로 저장
#winlen=0.025,winstep=0.01,numcep=13,nfilt=26,nfft=512
# DCT 연산 수행
# Filter Bank는 모두 Overlapping 되어 있기 때문에 Filter Bank 에너지들 사이에 상관관계가 존재하기 때문이다. DCT는 에너지들 사이에 이러한 상관관계를 분리 해주는 역할
#26개 DCT Coefficient 들 중 12만 남겨야 하는데, 그 이유는 DCT Coefficient 가 많으면, Filter Bank 에너지의 빠른 변화를 나타내게 되고, 이것은 음성인식의 성능을 낮추게 되기 때문이다.
print("---after mfcc---")
print(np.shape(melcoeffs))
print()
melcoeffs = np.transpose(melcoeffs) # --------TESTING
#계산 편의를 위한 전치 행렬
codebooks_org[i, :, :] = lbg(melcoeffs, Centroids)
#codebooks배열에 저장
print('\nTraining of model is complete!')
return (codebooks_org)
def training(nfiltbank):
nSpeaker = 4
nCentroid = 4
codebooks_mfcc = np.empty((nSpeaker, nfiltbank, nCentroid))
directory = os.getcwd() + '/train'
fname = str()
for i in range(nSpeaker):
fname = '/s' + str(i + 1) + '.wav'
print('Now speaker ', str(i + 1), 'features are being trained')
(fs, s) = read(directory + fname)
#REMOVE COMMENTS TO PLOT THE INPUT SPEAKER WAVE
#audio = (fs,s)[1]
# plot the first 1024 samples
#plt.plot(audio[0:1024])
# label the axes
# plt.ylabel("Amplitude")
# plt.xlabel("Time")
# set the title
#plt.title("Sample Wav")
# display the plot
#plt.show()
mel_coeff = mfcc(s, fs, nfiltbank)
codebooks_mfcc[i, :, :] = lbg(mel_coeff, nCentroid)
plt.figure(i)
for j in range(nCentroid):
plt.subplot(211)
plt.stem(codebooks_mfcc[i, :, j])
plt.xlabel('Number of features')
plt.ylabel('MFCC')
plt.title('Codebook for speaker ' + str(i + 1))
plt.show()
print('Training completed')
return (codebooks_mfcc)
codebooks_mfcc = np.empty((nSpeaker, nfiltbank, nCentroid))
# Wskazanie katalogu gdzie przechowywane są pliki do trenowania.
directory = os.getcwd() + "/train"
# Pętla wczytania pliku, trenowania i pokazywania wykresu.
for i in range(nSpeaker):
fname = "/s" + str(i + 1) + ".wav"
print("Trenowany mówca nr: ", str(i + 1))
# Wczytanie pliku.
(fs, s) = read(directory + fname)
# Zastosowanie metody trenującej.
mel_coeff = mfcc(s, fs, nfiltbank)
codebooks_mfcc[i, :, :] = lbg(mel_coeff, nCentroid) ####Joanna changed
# Tworzenei wykresu.
plt.figure(i + 1)
plt.title("Codebook dla mówcy " + str(i + 1) + " z " + str(nCentroid) +
" centroidami")
plt.xlabel("Liczba cech")
ax1 = plt # dla kilku subplot(2, 1, 1)
plt.ylabel("MFCC")
for j in range(nCentroid):
ax1.stem(
codebooks_mfcc[i, :, j],
use_line_collection=True,
linefmt="C2-.",
markerfmt="C8o",
def training(filtbankN):
Centroids = 16
SpeakerN = 10
dir_train = 'D:/_python/SpeakerRecognition/3/Data/train'
file = str()
codebooks_org = np.empty((SpeakerN, filtbankN, Centroids))
for i in range(SpeakerN):
file = '/Speaker' + str(i + 1) + '.wav'
(fs, sig) = read(dir_train + file)
print('Training the features of speaker ', str(i + 1))
# melcoeffs = MFCC(fs, sig, filtbankN)#--------TESTING
melcoeffs = MFCC(sig, fs) #--------TESTING
melcoeffs = np.transpose(melcoeffs) #--------TESTING
codebooks_org[i, :, :] = lbg(melcoeffs, Centroids)
plt.figure(i)
plt.title('Speaker ' + str(i + 1) + ' codeword with ' +
str(Centroids) + ' centroids')
for j in range(Centroids):
plt.plot(211)
plt.stem(codebooks_org[i, :, j])
plt.show()
print('Training of model is complete!')
#ploting actual cook books for first two speakers
codebooks = np.empty((2, filtbankN, Centroids))
melcoeffs = np.empty((2, filtbankN, 68))
for i in range(2):
file = '/Speaker' + str(i + 1) + '.wav'
(fs, sig) = read(dir_train + file)
# melcoeffs[i,:,:] = MFCC(fs, sig, filtbankN)[:,0:68]#--------TESTING
# codebooks[i,:,:] = lbg(melcoeffs[i,:,:], Centroids)#--------TESTING
temp1 = MFCC(sig, fs) #--------TESTING
temp1 = np.transpose(temp1) #--------TESTING
melcoeffs[i, :, :] = temp1[:, 0:68] #--------TESTING
codebooks[i, :, :] = lbg(melcoeffs[i, :, :],
Centroids) #--------TESTING
plt.figure(SpeakerN + 1)
c1 = plt.scatter(codebooks[0, 4, :],
codebooks[1, 5, :],
s=100,
color='g',
marker='+')
s1 = plt.scatter(melcoeffs[0, 4, :],
melcoeffs[0, 5, :],
s=100,
color='g',
marker='o')
c2 = plt.scatter(codebooks[1, 4, :],
codebooks[1, 5, :],
s=100,
color='b',
marker='+')
s2 = plt.scatter(melcoeffs[1, 4, :],
melcoeffs[1, 5, :],
s=100,
color='b',
marker='o')
plt.grid()
plt.legend((s1, s2, c1, c2),
('Speak1', 'Speak2', 'Speak1 centroids', 'Speak2 centroids'),
scatterpoints=1,
loc='lower right')
plt.show()
return (codebooks_org)
