def main():
parser = argparse.ArgumentParser()
# general configuration
parser.add_argument('--gpu',
'-g',
default='-1',
type=str,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--backend',
default='chainer',
type=str,
choices=['chainer', 'pytorch'],
help='Backend library')
parser.add_argument('--debugmode', default=1, type=int, help='Debugmode')
parser.add_argument('--seed', default=1, type=int, help='Random seed')
parser.add_argument('--verbose',
'-V',
default=1,
type=int,
help='Verbose option')
# task related
parser.add_argument(
'--recog-feat',
type=str,
required=True,
help='Filename of recognition feature data (Kaldi scp)')
parser.add_argument('--recog-label',
type=str,
required=True,
help='Filename of recognition label data (json)')
parser.add_argument('--result-label',
type=str,
required=True,
help='Filename of result label data (json)')
# model (parameter) related
parser.add_argument('--model',
type=str,
required=True,
help='Model file parameters to read')
parser.add_argument('--model-conf',
type=str,
required=True,
help='Model config file')
# search related
parser.add_argument('--nbest',
type=int,
default=1,
help='Output N-best hypotheses')
parser.add_argument('--beam-size', type=int, default=1, help='Beam size')
parser.add_argument('--penalty',
default=0.0,
type=float,
help='Incertion penalty')
parser.add_argument(
'--maxlenratio',
default=0.0,
type=float,
help='Input length ratio to obtain max output length.' +
'If maxlenratio=0.0 (default), it uses a end-detect function' +
'to automatically find maximum hypothesis lengths')
parser.add_argument('--minlenratio',
default=0.0,
type=float,
help='Input length ratio to obtain min output length')
parser.add_argument('--ctc-weight',
default=0.0,
type=float,
help='CTC weight in joint decoding')
# rnnlm related
parser.add_argument('--rnnlm',
type=str,
default=None,
help='RNNLM model file to read')
parser.add_argument('--lm-weight',
default=0.1,
type=float,
help='RNNLM weight.')
args = parser.parse_args()
# logging info
if args.verbose == 1:
logging.basicConfig(
level=logging.INFO,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose == 2:
logging.basicConfig(
level=logging.DEBUG,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
logging.warning("Skip DEBUG/INFO messages")
# display PYTHONPATH
logging.info('python path = ' + os.environ['PYTHONPATH'])
# seed setting
random.seed(args.seed)
np.random.seed(args.seed)
logging.info('set random seed = %d' % args.seed)
# recog
logging.info('backend = ' + args.backend)
if args.backend == "chainer":
from asr_chainer import recog
recog(args)
elif args.backend == "pytorch":
from asr_pytorch import recog
recog(args)
else:
raise ValueError("chainer and pytorch are only supported.")
def main():
parser = argparse.ArgumentParser()
# general configuration
parser.add_argument('--ngpu', default=0, type=int, help='Number of GPUs')
parser.add_argument('--backend',
default='chainer',
type=str,
choices=['chainer', 'pytorch'],
help='Backend library')
parser.add_argument('--debugmode', default=1, type=int, help='Debugmode')
parser.add_argument('--seed', default=1, type=int, help='Random seed')
parser.add_argument('--verbose',
'-V',
default=1,
type=int,
help='Verbose option')
# task related
parser.add_argument('--recog-json',
type=str,
help='Filename of recognition data (json)')
parser.add_argument('--result-label',
type=str,
required=True,
help='Filename of result label data (json)')
# model (parameter) related
parser.add_argument('--model',
type=str,
required=True,
help='Model file parameters to read')
parser.add_argument('--model-conf',
type=str,
default=None,
help='Model config file')
# search related
parser.add_argument('--nbest',
type=int,
default=1,
help='Output N-best hypotheses')
parser.add_argument('--beam-size', type=int, default=1, help='Beam size')
parser.add_argument('--penalty',
default=0.0,
type=float,
help='Incertion penalty')
parser.add_argument('--maxlenratio',
default=0.0,
type=float,
help="""Input length ratio to obtain max output length.
If maxlenratio=0.0 (default), it uses a end-detect function
to automatically find maximum hypothesis lengths""")
parser.add_argument('--minlenratio',
default=0.0,
type=float,
help='Input length ratio to obtain min output length')
parser.add_argument('--ctc-weight',
default=0.0,
type=float,
help='CTC weight in joint decoding')
# rnnlm related
parser.add_argument('--rnnlm',
type=str,
default=None,
help='RNNLM model file to read')
parser.add_argument('--rnnlm-conf',
type=str,
default=None,
help='RNNLM model config file to read')
parser.add_argument('--word-rnnlm',
type=str,
default=None,
help='Word RNNLM model file to read')
parser.add_argument('--word-dict',
type=str,
default=None,
help='Word list to read')
parser.add_argument('--lm-weight',
default=0.1,
type=float,
help='RNNLM weight.')
args = parser.parse_args()
# logging info
if args.verbose == 1:
logging.basicConfig(
level=logging.INFO,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose == 2:
logging.basicConfig(
level=logging.DEBUG,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
logging.warning("Skip DEBUG/INFO messages")
# check CUDA_VISIBLE_DEVICES
if args.ngpu > 0:
cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
if cvd is None:
logging.warn("CUDA_VISIBLE_DEVICES is not set.")
elif args.ngpu != len(cvd.split(",")):
logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.")
sys.exit(1)
# display PYTHONPATH
logging.info('python path = ' + os.environ['PYTHONPATH'])
# seed setting
random.seed(args.seed)
np.random.seed(args.seed)
logging.info('set random seed = %d' % args.seed)
# recog
logging.info('backend = ' + args.backend)
if args.backend == "chainer":
from asr_chainer import recog
recog(args)
elif args.backend == "pytorch":
from asr_pytorch import recog
recog(args)
else:
raise ValueError("chainer and pytorch are only supported.")