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()
log('> ... the saved features are %s' % (out_ark_file))
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)
remainder = in_matrix.shape[0] % cfg.batch_size
for index in xrange(in_matrix.shape[0] / cfg.batch_size):
final_matrix[index * cfg.batch_size:(index + 1) *
cfg.batch_size] = out_function(
in_matrix[index * cfg.batch_size:(index + 1) *
cfg.batch_size])
if remainder > 0:
print '\tremainder:' + str(remainder)
final_matrix[-remainder:] = out_function(in_matrix[-remainder:])
print 'final_matrix:' + str(final_matrix.shape)
kaldiwrite.write_kaldi_mat(uttid, final_matrix)
kaldiwrite.close()
log('> ... the saved features are %s' % (out_ark_file))
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)
#print cnn.conv_layers[1].filter_shape
model = DNNV(numpy_rng = rng, theano_rng = theano_rng, cfg = cfg, input=cnn.conv_layers[1].output)
_file2nnet(model.layers, set_layer_num = len(model.layers)+len(conv_configs), start_layer=len(conv_configs), filename=cnn_param_file)
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)
extract_function = model.build_extract_feat_function(layer_index)
mid_matrix = out_function(in_matrix)
out_matrix = extract_function(mid_matrix)
kaldiwrite.write_kaldi_mat(uttid, out_matrix)
kaldiwrite.close()
log('> ... the saved features are %s' % (out_ark_file))
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)
remainder = in_matrix.shape[0]%cfg.batch_size
for index in xrange(in_matrix.shape[0]/cfg.batch_size):
final_matrix[index*cfg.batch_size:(index+1)*cfg.batch_size] = out_function(in_matrix[index*cfg.batch_size:(index+1)*cfg.batch_size])
if remainder > 0:
print '\tremainder:'+str(remainder)
final_matrix[-remainder:] = out_function(in_matrix[-remainder:])
print 'final_matrix:'+str(final_matrix.shape)
kaldiwrite.write_kaldi_mat(uttid, final_matrix)
kaldiwrite.close()
log('> ... the saved features are %s' % (out_ark_file))
