def getNextSplitData(self):
p1 = Popen([
'apply-cmvn', '--print-args=false', '--norm-vars=true',
'--utt2spk=ark:' + self.data + '/split' + str(self.numSplit) +
'utt/' + str(self.splitDataCounter) + '/utt2spk',
'scp:' + self.data + '/split' + str(self.numSplit) + 'utt/' +
str(self.splitDataCounter) + '/cmvn.scp',
'scp:' + self.data + '/split' + str(self.numSplit) + 'utt/' +
str(self.splitDataCounter) + '/feats.scp', 'ark:-'
],
stdout=PIPE,
stderr=DEVNULL)
p2 = Popen(['add-deltas', '--print-args=false', 'ark:-', 'ark:-'],
stdin=p1.stdout,
stdout=PIPE)
p1.stdout.close()
with open(
self.labelDir.name + '/' + str(self.splitDataCounter) +
'.pickle', 'rb') as f:
labels = pickle.load(f)
featList = []
labelList = []
while True:
uid, featMat = kaldiIO.readUtterance(p2.stdout)
if uid == None:
return (numpy.vstack(featList), numpy.hstack(labelList))
if uid in labels:
labelMat = self.getBinaryLabels(labels[uid])
featList.append(featMat)
labelList.append(labels[uid])
def getNextSplitData (self):
p1 = Popen (['apply-cmvn','--print-args=false','--norm-vars=true',
'--utt2spk=ark:' + self.data + '/split' + str(self.numSplit) + '/' + str(self.splitDataCounter) + '/utt2spk',
'scp:' + self.data + '/split' + str(self.numSplit) + '/' + str(self.splitDataCounter) + '/cmvn.scp',
'scp:' + self.data + '/split' + str(self.numSplit) + '/' + str(self.splitDataCounter) + '/feats.scp','ark:-'],
stdout=PIPE, stderr=DEVNULL)
p2 = Popen (['splice-feats','--print-args=false','--left-context=5','--right-context=5',
'ark:-','ark:-'], stdin=p1.stdout, stdout=PIPE)
p1.stdout.close()
p3 = Popen (['add-deltas','--print-args=false','ark:-','ark:-'], stdin=p2.stdout, stdout=PIPE)
p2.stdout.close()
with open (self.labelDir.name + '/' + str(self.splitDataCounter) + '.pickle', 'rb') as f:
labels = pickle.load (f)
featList = []
labelList = []
while True:
uid, featMat = kaldiIO.readUtterance (p3.stdout)
if uid == None:
return (numpy.vstack(featList), numpy.vstack(labelList))
if uid in labels:
labelMat = self.getBinaryLabels(labels[uid])
featList.append (featMat)
labelList.append (labelMat)
def getNextSplitData(self):
p = Popen([
'splice-feats', '--print-args=false', '--left-context=' +
self.context, '--right-context=' + self.context,
'scp:' + self.data + '/split' + str(self.numSplit) + 'utt/' +
str(self.splitDataCounter) + '/feats.scp', 'ark:-'
],
stdout=PIPE)
with open(
self.labelDir.name + '/' + str(self.splitDataCounter) +
'.pickle', 'rb') as f:
labels = pickle.load(f)
##ipdb.set_trace()
featList = []
labelList = []
while True:
uid, featMat = kaldiIO.readUtterance(p.stdout)
if uid == None:
return (numpy.vstack(featList), numpy.hstack(labelList))
if uid in labels:
## NOTE: The featMat may contain more frames if the labels were generated from
## MFCCs extracted on 25ms frames (and here we are using 10ms frames). Using
## these labels is fairly OK because the frame rate (10ms) is unchanged. We
## discard the last few frames that have no corresponding labels.
featList.append(featMat[0:len(labels[uid])])
labelList.append(labels[uid])
def getNextSplitData(self):
p1 = Popen([
'apply-cmvn', '--print-args=false', '--norm-vars=true',
'--utt2spk=ark:' + self.data + '/split' + str(self.numSplit) +
'utt/' + str(self.splitDataCounter) + '/utt2spk',
'scp:' + self.data + '/split' + str(self.numSplit) + 'utt/' +
str(self.splitDataCounter) + '/cmvn.scp',
'scp:' + self.data + '/split' + str(self.numSplit) + 'utt/' +
str(self.splitDataCounter) + '/feats.scp', 'ark:-'
],
stdout=PIPE,
stderr=DEVNULL)
p2 = Popen([
'splice-feats', '--print-args=false',
'--left-context=' + str(self.left),
'--right-context=' + str(self.right), 'ark:-', 'ark:-'
],
stdin=p1.stdout,
stdout=PIPE)
p1.stdout.close()
with open(
self.labelDir.name + '/' + str(self.splitDataCounter) +
'.pickle', 'rb') as f:
labels = pickle.load(f)
#print(labels)
featList = []
labelList = []
while True:
uid, featMat = kaldiIO.readUtterance(p2.stdout)
if uid == None:
self.lable_list = labelList
return (numpy.vstack(featList), numpy.hstack(labelList))
if uid in labels:
row, col = featMat.shape
fillNum = self.timeSteps - (row % self.timeSteps)
fillRight = fillNum // 2
fillLeft = fillNum - fillRight
featMat = numpy.concatenate([
numpy.tile(featMat[0], (fillLeft, 1)), featMat,
numpy.tile(featMat[-1], (fillRight, 1))
])
#print(featMat.shape)
labels4uid = labels[uid]
labels4uid = numpy.concatenate([
numpy.tile(labels4uid[0], (fillLeft, )), labels4uid,
numpy.tile(labels4uid[-1], (fillRight, ))
])
featList.append(featMat)
labelList.append(labels4uid)
## You should have received a copy of the GNU General Public License
## along with this program. If not, see <http://www.gnu.org/licenses/>.
import sys
import numpy
import keras
import kaldiIO
from signal import signal, SIGPIPE, SIG_DFL
if __name__ == '__main__':
model = sys.argv[1]
if not model.endswith('.h5'):
raise TypeError(
'Unsupported model type. Please use h5 format. Update Keras if needed'
)
## Load model
m = keras.models.load_model(model)
arkIn = sys.stdin.buffer
arkOut = sys.stdout.buffer
encoding = sys.stdout.encoding
signal(SIGPIPE, SIG_DFL)
uttId, featMat = kaldiIO.readUtterance(arkIn)
while uttId:
logProbMat = numpy.log(m.predict(featMat))
kaldiIO.writeUtterance(uttId, logProbMat, arkOut, encoding)
uttId, featMat = kaldiIO.readUtterance(arkIn)
## You should have received a copy of the GNU General Public License
## along with this program. If not, see <http://www.gnu.org/licenses/>.
import sys
import numpy
import keras
import kaldiIO
from signal import signal, SIGPIPE, SIG_DFL
if __name__ == '__main__':
model = sys.argv[1]
if not model.endswith('.h5'):
raise TypeError ('Unsupported model type. Please use h5 format. Update Keras if needed')
## Load model
m = keras.models.load_model (model)
arkIn = sys.stdin.buffer
arkOut = sys.stdout.buffer
encoding = sys.stdout.encoding
signal (SIGPIPE, SIG_DFL)
uttId, featMat = kaldiIO.readUtterance(arkIn)
while uttId:
logProbMat = numpy.log (m.predict (featMat))
kaldiIO.writeUtterance(uttId, logProbMat, arkOut, encoding)
uttId, featMat = kaldiIO.readUtterance(arkIn)
]
model_list = [keras.models.load_model(m) for m in model_list]
weights = list(np.ones(len(model_list)))
## Save teacher predictions on disk
st = time.time()
outfile = data_tr + '/teacher_predictions.ark'
print('Writing teacher predictions...', outfile)
p1 = Popen([
'splice-feats', '--print-args=false', '--left-context=5',
'--right-context=5', 'scp:' + data_tr + '/feats.scp', 'ark:-'
],
stdout=PIPE)
with open('temp_teacher.ark', 'wb') as f:
while True:
uid, featMat = kaldiIO.readUtterance(p1.stdout)
if uid == None:
break
avg_prediction = geometric_mean(featMat, model_list, weights)
kaldiIO.writeUtterance(uid, avg_prediction, f, sys.stdout.encoding)
p1.stdout.close()
et = time.time()
os.rename('temp_teacher.ark', outfile)
print('Done writing teacher predictions for ', data_tr, '. Time taken: ',
et - st)
## Initialize data generator
trGen = dataGenerator_student_tr(data_tr, ali_tr, sgmm,
learning['batchSize'])
cvGen = dataGenerator_student_cv(data_cv, ali_cv, sgmm,
learning['batchSize'])
