print "Error: the argument %s has to be specified" % (arg)
exit(1)
# mandatory arguments
in_scp_file = arguments['in_scp_file']
out_ark_file = arguments['out_ark_file']
lstm_param_file = arguments['lstm_param_file']
lstm_cfg_file = arguments['lstm_cfg_file']
layer_index = int(arguments['layer_index'])
# network structure
cfg = cPickle.load(open(lstm_cfg_file, 'r'))
cfg.init_activation()
kaldiread = KaldiReadIn(in_scp_file)
kaldiwrite = KaldiWriteOut(out_ark_file)
log('> ... setting up the ATTEND LSTM layers')
rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(rng.randint(2**30))
lstm = ATTEND_LSTM(numpy_rng=rng, theano_rng=theano_rng, cfg=cfg)
_file2nnet(layers=lstm.layers,
set_layer_num=len(lstm.layers),
filename=lstm_param_file)
out_function = lstm.build_extract_feat_function()
while True:
uttid, in_matrix = kaldiread.read_next_utt()
if uttid == '':
break
# network structure
cfg = cPickle.load(open(cnn_cfg_file,'r'))
conv_configs = cfg.conv_layer_configs
conv_layer_number = len(conv_configs)
for i in xrange(conv_layer_number):
conv_configs[i]['activation'] = cfg.conv_activation
# whether to use the fast mode
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(89677)
theano_rng = RandomStreams(rng.randint(2 ** 30))
cfg.init_activation()
cnn = CNN_Forward(numpy_rng = rng, theano_rng=theano_rng, conv_layer_configs = conv_configs, use_fast = use_fast)
#cnn = CNNV(numpy_rng = rng, theano_rng=theano_rng, cfg=cfg)
_file2nnet(cnn.conv_layers, set_layer_num = len(conv_configs), filename=cnn_param_file)
out_function = cnn.build_out_function()
#out_function = cnn.build_extract_feat_function(-1)
if arguments.has_key(arg) == False:
print "Error: the argument %s has to be specified" % (arg); exit(1)
# mandatory arguments
in_scp_file = arguments['in_scp_file']
out_ark_file = arguments['out_ark_file']
lstm_param_file = arguments['lstm_param_file']
lstm_cfg_file = arguments['lstm_cfg_file']
layer_index = int(arguments['layer_index'])
# network structure
cfg = cPickle.load(open(lstm_cfg_file,'r'))
cfg.init_activation()
kaldiread = KaldiReadIn(in_scp_file)
kaldiwrite = KaldiWriteOut(out_ark_file)
log('> ... setting up the ATTEND LSTM layers')
rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(rng.randint(2 ** 30))
lstm = ATTEND_LSTM(numpy_rng=rng, theano_rng=theano_rng, cfg = cfg)
_file2nnet(layers = lstm.layers, set_layer_num = len(lstm.layers), filename=lstm_param_file)
out_function = lstm.build_extract_feat_function()
while True:
uttid, in_matrix = kaldiread.read_next_utt()
if uttid == '':
break
print 'in_matrix:'+str(in_matrix.shape)
final_matrix = numpy.zeros((in_matrix.shape[0],cfg.n_outs), dtype=theano.config.floatX)