from libs.deep_lstm_builder import build_deep_lstm
from data.wsj.fuel_utils import create_ivector_test_datastream, get_uttid_stream
from libs.comp_graph_utils import ff
from hmrnn.hmlstm_builder import HMRNNModel
from hmrnn.mixer import reset_state
from libs.hmrnn_utils import add_hmrnn_graph_params
import kaldi_io
if __name__ == '__main__':
print(' '.join(sys.argv), file=sys.stderr)
parser = get_arg_parser()
add_hmrnn_graph_params(parser)
parser.add_argument('model')
parser.add_argument('hmrnn_model')
parser.add_argument('dataset')
parser.add_argument('wxfilename')
args = parser.parse_args()
print(args, file=sys.stderr)
if args.batch_size != args.n_batch:
print('--batch-size != --n-batch')
sys.exit(1)
from libs.deep_lstm_utils import *
import libs.deep_lstm_utils as deep_lstm_utils
from libs.lasagne_libs.updates import momentum
import libs.utils as utils
import models.deep_bidir_lstm as models
import data.wsj.fuel_utils as fuel_utils
import libs.param_utils as param_utils
import data.transformers as trans
import itertools
from fuel.transformers import Padding
if __name__ == '__main__':
parser = deep_lstm_utils.get_arg_parser()
deep_lstm_utils.add_lhuc_params(parser)
args = parser.parse_args()
args.save_path = get_save_path(args)
args.save_path = '{}_lhuc'.format(args.save_path)
if args.use_ivectors:
args.input_dim = args.input_dim + args.ivector_dim
print(args)
print('Load data stream {} from {}'.format(args.valid_dataset,
args.data_path))
if args.norm_path:
print('Use normalization data from {}'.format(args.norm_path))