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 = 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):
"""Train the speaker recognition model
using .wav files from the Speaker folder.
Feature extraction is done in two ways:
MFCC and LPC
Parameters
----------
nfiltbank: number of filter banks for mfcc (default 13)
orderLPC: number or order for LPC (default 15)
Return
------
Returns a pair (codebooks_mfcc, codebooks_lpc)
which are the encoded clusters for trained speakers"""
# Get directory and list of speaker *.wav files
original_dir = os.getcwd()
directory = os.getcwd() + '/static/speakers'
os.chdir(directory)
wave_files = filter(os.path.isfile, os.listdir(directory + "/"))
wave_files = [os.path.join(directory, f)
for f in wave_files] # add path to each file
wave_files.sort(key=lambda x: os.path.getmtime(x))
os.chdir(original_dir)
def cutDirectoryFromName(file):
if os.name == 'nt':
for i, c in enumerate(file):
if file[-(i + 1)] == "\\":
return file[-(i):]
else:
for i, c in enumerate(file):
if file[-(i + 1)] == "/":
return file[-(i):]
wave_files = list(map(lambda x: cutDirectoryFromName(x), wave_files))
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
(fs, s) = read(directory + fname)
mel_coeff = mfcc(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)
class test():
def minDistance(features, codebooks):
speaker = 0
distmin = np.inf
for k in range(np.shape(codebooks)[0]):
D = EUDistance(features, codebooks[k, :, :])
dist = np.sum(np.min(D, axis=1)) / (np.shape(D)[0])
if dist < distmin:
distmin = dist
speaker = k
return speaker
for i in range(nSpeaker):
fname = '/s' + str(i + 1) + '.wav'
print 'Now speaker ', str(i + 1), 'features are being tested'
(fs, s) = read(directory + fname)
mel_coefs = mfcc(s, fs, nfiltbank)
lpc_coefs = lpc(s, fs, orderLPC)
sp_mfcc = minDistance(mel_coefs, codebooks_mfcc)
sp_lpc = minDistance(lpc_coefs, codebooks_lpc)
if ((sp_mfcc + 1) <= 5):
b = 'vesa'
elif ((sp_mfcc + 1) > 5) and ((sp_mfcc + 1) <= 10):
b = 'belva'
elif ((sp_mfcc + 1) > 10) and ((sp_mfcc + 1) <= 15):
b = 'ary'
print 'Speaker ', (i + 1), ' in test matches with speaker ', b, (
sp_mfcc + 1), ' in train for training with MFCC'
print 'Speaker ', (
i + 1
), ' in test matches with speaker ', b, ' in train for training with 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 recognize_speaker(codebooks_mfcc, codebooks_lpc, wav):
"""
Predicts speaker for .wav file using trained clusters
Parameters
---------
codebooks_mfcc: trained clusters for MFCC
codebooks_lpc: trained clusters for LPC
Returns
-------
A pair of strings for the speaker prediction of both methods MFCC and LPC
"""
def minDistance(features, codebooks):
speaker = 0
distmin = np.inf
for k in range(np.shape(codebooks)[0]):
D = EUDistance(features, codebooks[k, :, :])
dist = np.sum(np.min(D, axis=1)) / (np.shape(D)[0])
# print "DISTANCE!!!" + str(dist)
if dist < distmin:
distmin = dist
speaker = k
return speaker
#Read .wave file
(fs, s) = read(wav)
# Passing test file to MFCC
mel_coefs = mfcc(s, fs)
mel_coefs = mel_coefs.transpose()
mel_coefs[0, :] = np.zeros(
mel_coefs.shape[1]
) # 0th coefficient does not carry significant information
# Passing test file to LPC
lpc_coefs = lpc(s, fs, 15)
sp_mfcc = minDistance(mel_coefs, codebooks_mfcc)
sp_lpc = minDistance(lpc_coefs, codebooks_lpc)
#Find the speaker stored in the textfile
with open("Speaker_Names.txt") as f:
content = f.readlines()
# you may also want to remove whitespace characters like `\n` at the end of each line
speaker_names = [x.strip() for x in content]
return ("You are " + speaker_names[sp_mfcc] + '\n',
"You are " + speaker_names[sp_lpc] + '\n')
def test1234():
list_of_files = glob.glob('D:/FTP/speaker/*.wav')
latest_file = max(list_of_files, key=os.path.getmtime)
#print latest_file
stamp = latest_file
#print (latest_file)
print(stamp)
(fs, s) = read(stamp)
mel_coefs = mfcc(s, fs, nfiltbank)
lpc_coefs = lpc(s, fs, orderLPC)
sp_mfcc = minDistance(mel_coefs, codebooks_mfcc)
sp_lpc = minDistance(lpc_coefs, codebooks_lpc)
b = namaorang(sp_mfcc)
print('Speaker ', (stamp), ' in test matches with speaker ', b,
(sp_mfcc + 1), ' in train for training with MFCC')
for k in range(np.shape(codebooks)[0]):
D = EUDistance(features, codebooks[k, :, :])
dist = np.sum(np.min(D, axis=1)) / (np.shape(D)[0])
if dist < distmin:
distmin = dist
speaker = k
return speaker
for i in range(nSpeaker):
fname = '/s' + str(i + 1) + '.wav'
print('Now speaker ', str(i + 1), 'features are being tested')
(fs, s) = read(directory + fname)
mel_coefs = mfcc(s, fs, nfiltbank)
lpc_coefs = lpc(s, fs, orderLPC)
sp_mfcc = minDistance(mel_coefs, codebooks_mfcc)
sp_lpc = minDistance(lpc_coefs, codebooks_lpc)
print('Speaker ', (i + 1), ' in test matches with speaker ', (sp_mfcc + 1),
' in train for training with MFCC')
print('Speaker ', (i + 1), ' in test matches with speaker ', (sp_lpc + 1),
' in train for training with LPC')
if i == sp_mfcc:
nCorrect_MFCC += 1
if i == sp_lpc:
nCorrect_LPC += 1
percentageCorrect_MFCC = (nCorrect_MFCC / nSpeaker) * 100
print('Accuracy of result for training with MFCC is ', percentageCorrect_MFCC,
