use_fast = cfg.use_fast
if arguments.has_key('use_fast'):
use_fast = string_2_bool(arguments['use_fast'])
kaldiread = KaldiReadIn(in_scp_file)
kaldiwrite = KaldiWriteOut(out_ark_file)
log('> ... setting up the CNN convolution layers')
input_shape_train = conv_configs[0]['input_shape']
input_shape_1 = (input_shape_train[1], input_shape_train[2], input_shape_train[3])
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2 ** 30))
cnn = CNN_Forward(numpy_rng = rng, theano_rng=theano_rng, conv_layer_configs = conv_configs, use_fast = use_fast)
_file2nnet(cnn.conv_layers, set_layer_num = len(conv_configs), filename=cnn_param_file)
out_function = cnn.build_out_function()
log('> ... processing the data')
while True:
uttid, in_matrix = kaldiread.read_next_utt()
if uttid == '':
break
in_matrix = numpy.reshape(in_matrix, (in_matrix.shape[0],) + input_shape_1)
out_matrix = out_function(in_matrix)
kaldiwrite.write_kaldi_mat(uttid, out_matrix)
kaldiwrite.close()
if arguments.has_key('use_fast'):
use_fast = string2bool(arguments['use_fast'])
kaldiread = KaldiReadIn(in_scp_file)
kaldiwrite = KaldiWriteOut(out_ark_file)
log('> ... setting up the CNN convolution layers')
input_shape_train = conv_configs[0]['input_shape']
input_shape_1 = (input_shape_train[1], input_shape_train[2],
input_shape_train[3])
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2**30))
cnn = CNN_Forward(numpy_rng=rng,
theano_rng=theano_rng,
conv_layer_configs=conv_configs,
use_fast=use_fast)
_file2nnet(cnn.conv_layers,
set_layer_num=len(conv_configs),
filename=cnn_param_file)
out_function = cnn.build_out_function()
log('> ... processing the data')
while True:
uttid, in_matrix = kaldiread.read_next_utt()
if uttid == '':
break
in_matrix = numpy.reshape(in_matrix,
(in_matrix.shape[0], ) + input_shape_1)
out_matrix = out_function(in_matrix)
uttID, feat_mat = kaldiIn.next()
if feat_mat == None:
break
kaldiIn.close()
input_shape_train = conv_configs[0]['input_shape']
input_shape_1 = (input_shape_train[1], input_shape_train[2], input_shape_train[3])
num_utt = len(feat_mats_np)
feat_mats = []
for i in xrange(num_utt):
feat_mats.append(numpy.reshape(feat_mats_np[i], (feat_rows[i],) + input_shape_1))
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2 ** 30))
cnn = CNN_Forward(numpy_rng = rng, theano_rng=theano_rng,
conv_layer_configs = conv_configs, use_fast = use_fast)
_file2cnn(cnn.conv_layers, filename=conv_net_file)
out_function = cnn.build_out_function(feat_mats)
log('> ... processing the data')
kaldiOut = KaldiWriteOut(output_scp,output_ark)
kaldiOut.open()
for i in xrange(num_utt):
feat_out = out_function(feat_mats[i])
kaldiOut.write(uttIDs[i], feat_out)
kaldiOut.close()
end_time = time.clock()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
