action='store_true',
help="Temporary files will be removed")
parser.add_argument("-q",
"--quiet",
dest="quiet",
action='store_true',
help="Nothing will be printed in STDERR")
args = parser.parse_args()
from candidatesreader import NBestList
import codecs
import numpy as np
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
L.set_logger(os.path.abspath(args.out_dir), 'train_log.txt')
L.print_args(args)
output_nbest_path = args.out_dir + '/augmented.nbest'
shutil.copy(args.input_nbest, output_nbest_path)
with open(args.weights, 'r') as input_weights:
lines = input_weights.readlines()
if len(lines) > 1:
L.warning(
"Weights file has more than one line. I'll read the 1st and ignore the rest."
)
weights = np.asarray(lines[0].strip().split(" "), dtype=float)
prefix = os.path.basename(args.input_nbest)
input_aug_nbest = NBestList(output_nbest_path, mode='r')
required=True,
help="Input n-best file")
parser.add_argument("-o",
"--output-nbest",
dest="output_nbest_path",
required=True,
help="Output n-best file")
parser.add_argument(
"-f",
"--feature",
dest="feature_string",
required=True,
help=
"feature initializer, e.g. LM('LM0','/path/to/lm_file', normalize=True)")
args = parser.parse_args()
L.set_logger(os.path.abspath(os.path.dirname(args.output_nbest_path)),
'augment_log.txt')
L.print_args(args)
print(
'+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
)
print('[' + args.feature_string + ']')
print(
'+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
)
features = eval('[' + args.feature_string + ']')
augment(features, args.source_path, args.input_nbest_path,
args.output_nbest_path)
logger.info(L.green('Augmenting done.'))