def main(args):
if args.use_ivectors:
args.input_dim = args.input_dim + args.ivector_dim
input_data = T.ftensor3('input_data')
input_mask = T.fmatrix('input_mask')
network = models.deep_bidir_lstm_alex(input_var=input_data,
mask_var=input_mask,
input_dim=args.input_dim,
num_units_list=[args.num_nodes] *
args.num_layers,
output_dim=args.output_dim)
network_params = get_all_params(network, trainable=True)
print('Loading Parameters...', file=sys.stderr)
if args.model:
with open(args.model, 'rb') as f:
pretrain_network_params_val, pretrain_update_params_val, \
pretrain_total_epoch_cnt = pickle.load(f)
set_model_param_value(network_params, pretrain_network_params_val)
else:
print('Must specfiy network to load', file=sys.stderr)
sys.exit(1)
ff_fn = ff(input_data=input_data, input_mask=input_mask, network=network)
feat_stream = fuel_utils.get_feat_stream(args.data_path,
args.dataset,
args.batch_size,
use_ivectors=args.use_ivectors)
uttid_stream = fuel_utils.get_uttid_stream(args.data_path, args.dataset,
args.batch_size)
writer = kaldi_io.BaseFloatMatrixWriter(args.wxfilename)
for batch_idx, (feat_batch, uttid_batch) in enumerate(
zip(feat_stream.get_epoch_iterator(),
uttid_stream.get_epoch_iterator())):
input_data, input_mask = feat_batch
feat_lens = input_mask.sum(axis=1)
print('Feed-forwarding...', file=sys.stderr)
net_output = ff_fn(input_data, input_mask)
print('Writing outputs...', file=sys.stderr)
for out_idx, (output,
uttid) in enumerate(zip(net_output[0], uttid_batch[0])):
valid_len = feat_lens[out_idx]
writer.write(uttid.encode('ascii'), numpy.log(output[:valid_len]))
writer.close()
with open(args.model, 'rb') as f:
[
pretrain_network_params_val, pretrain_update_params_val,
pretrain_total_epoch_cnt
] = pickle.load(f)
set_model_param_value(network_params, pretrain_network_params_val)
else:
print('Must specfiy network to load', file=sys.stderr)
sys.exit(1)
ff_fn = ff(network, input_data, input_mask)
test_datastream = get_feat_stream(path=args.data_path,
which_set=args.dataset,
batch_size=args.batch_size)
uttid_datastream = get_uttid_stream(path=args.data_path,
which_set=args.dataset,
batch_size=args.batch_size)
writer = kaldi_io.BaseFloatMatrixWriter(args.wxfilename)
for batch_idx, (feat_batch, uttid_batch) in enumerate(
zip(test_datastream.get_epoch_iterator(),
uttid_datastream.get_epoch_iterator())):
input_data, input_mask = feat_batch
feat_lens = input_mask.sum(axis=1)
print('Feed-forwarding...', file=sys.stderr)
net_output = ff_fn(input_data, input_mask)
print('Writing outputs...', file=sys.stderr)
for out_idx, (output,
print('Loading Parameters...', file=sys.stderr)
if args.model:
with open(args.model, 'rb') as f:
pretrain_network_params_val, pretrain_update_params_val, \
pretrain_total_epoch_cnt = pickle.load(f)
set_model_param_value(network_params, pretrain_network_params_val)
else:
print('Must specfiy network to load', file=sys.stderr)
sys.exit(1)
ff_fn = ff(network, input_data, input_mask, ivector_data)
test_ds = create_ivector_test_datastream(args.data_path, args.dataset,
args.batch_size)
uttid_stream = get_uttid_stream(args.data_path, args.dataset,
args.batch_size)
writer = kaldi_io.BaseFloatMatrixWriter(args.wxfilename)
for batch_idx, (feat_batch, uttid_batch) in enumerate(
zip(test_ds.get_epoch_iterator(),
uttid_stream.get_epoch_iterator())):
input_data, input_mask, ivector_data, ivector_mask = feat_batch
feat_lens = input_mask.sum(axis=1)
print('Feed-forwarding...', file=sys.stderr)
if args.use_ivector_input or args.use_ivector_model:
net_output = ff_fn(input_data, input_mask, ivector_data)
else:
net_output = ff_fn(input_data, input_mask)