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('--outdir',
type=str,
required=True,
help='Output directory')
parser.add_argument('--debugmode', default=1, type=int, help='Debugmode')
parser.add_argument('--dict', required=True, help='Dictionary')
parser.add_argument('--seed', default=1, type=int, help='Random seed')
parser.add_argument('--debugdir',
type=str,
help='Output directory for debugging')
parser.add_argument('--resume',
'-r',
default='',
nargs='?',
help='Resume the training from snapshot')
parser.add_argument('--minibatches',
'-N',
type=int,
default='-1',
help='Process only N minibatches (for debug)')
parser.add_argument('--verbose',
'-V',
default=0,
type=int,
help='Verbose option')
# task related
parser.add_argument('--train-feat',
type=str,
required=True,
help='Filename of train feature data (Kaldi scp)')
parser.add_argument('--valid-feat',
type=str,
required=True,
help='Filename of validation feature data (Kaldi scp)')
parser.add_argument('--train-label',
type=str,
required=True,
help='Filename of train label data (json)')
parser.add_argument('--valid-label',
type=str,
required=True,
help='Filename of validation label data (json)')
# network archtecture
# encoder
parser.add_argument('--etype',
default='blstmp',
type=str,
choices=['blstm', 'blstmp', 'vggblstmp', 'vggblstm'],
help='Type of encoder network architecture')
parser.add_argument('--elayers',
default=4,
type=int,
help='Number of encoder layers')
parser.add_argument('--eunits',
'-u',
default=300,
type=int,
help='Number of encoder hidden units')
parser.add_argument('--eprojs',
default=320,
type=int,
help='Number of encoder projection units')
parser.add_argument(
'--subsample',
default=1,
type=str,
help=
'Subsample input frames x_y_z means subsample every x frame at 1st layer, '
'every y frame at 2nd layer etc.')
# loss
parser.add_argument('--ctc_type',
default='chainer',
type=str,
choices=['chainer', 'warpctc'],
help='Type of CTC implementation to calculate loss.')
# attention
parser.add_argument('--atype',
default='dot',
type=str,
choices=['dot', 'location', 'noatt'],
help='Type of attention architecture')
parser.add_argument('--adim',
default=320,
type=int,
help='Number of attention transformation dimensions')
parser.add_argument('--aconv-chans',
default=-1,
type=int,
help='Number of attention convolution channels \
(negative value indicates no location-aware attention)'
)
parser.add_argument('--aconv-filts',
default=100,
type=int,
help='Number of attention convolution filters \
(negative value indicates no location-aware attention)'
)
# decoder
parser.add_argument('--dtype',
default='lstm',
type=str,
choices=['lstm'],
help='Type of decoder network architecture')
parser.add_argument('--dlayers',
default=1,
type=int,
help='Number of decoder layers')
parser.add_argument('--dunits',
default=320,
type=int,
help='Number of decoder hidden units')
parser.add_argument(
'--mtlalpha',
default=0.5,
type=float,
help=
'Multitask learning coefficient, alpha: alpha*ctc_loss + (1-alpha)*att_loss '
)
parser.add_argument(
'--lsm-type',
const='',
default='',
type=str,
nargs='?',
choices=['', 'unigram'],
help='Apply label smoothing with a specified distribution type')
parser.add_argument('--lsm-weight',
default=0.0,
type=float,
help='Label smoothing weight')
# model (parameter) related
parser.add_argument('--dropout-rate',
default=0.0,
type=float,
help='Dropout rate')
# minibatch related
parser.add_argument('--batch-size',
'-b',
default=50,
type=int,
help='Batch size')
parser.add_argument(
'--maxlen-in',
default=800,
type=int,
metavar='ML',
help='Batch size is reduced if the input sequence length > ML')
parser.add_argument(
'--maxlen-out',
default=150,
type=int,
metavar='ML',
help='Batch size is reduced if the output sequence length > ML')
# optimization related
parser.add_argument('--opt',
default='adadelta',
type=str,
choices=['adadelta', 'adam'],
help='Optimizer')
parser.add_argument('--eps',
default=1e-8,
type=float,
help='Epsilon constant for optimizer')
parser.add_argument('--eps-decay',
default=0.01,
type=float,
help='Decaying ratio of epsilon')
parser.add_argument('--criterion',
default='acc',
type=str,
choices=['loss', 'acc'],
help='Criterion to perform epsilon decay')
parser.add_argument('--threshold',
default=1e-4,
type=float,
help='Threshold to stop iteration')
parser.add_argument('--epochs',
'-e',
default=30,
type=int,
help='Number of maximum epochs')
parser.add_argument('--grad-clip',
default=5,
type=float,
help='Gradient norm threshold to clip')
args = parser.parse_args()
# logging info
if args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
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'])
# set random seed
logging.info('random seed = %d' % args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
# load dictionary for debug log
if args.dict is not None:
with open(args.dict, 'rb') as f:
dictionary = f.readlines()
char_list = [
entry.decode('utf-8').split(' ')[0] for entry in dictionary
]
char_list.insert(0, '<blank>')
char_list.append('<eos>')
args.char_list = char_list
else:
args.char_list = None
# train
logging.info('backend = ' + args.backend)
if args.backend == "chainer":
from asr_chainer import train
train(args)
elif args.backend == "pytorch":
from asr_pytorch import train
train(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('--outdir', type=str, required=True,
help='Output directory')
parser.add_argument('--debugmode', default=1, type=int,
help='Debugmode')
parser.add_argument('--dict', required=True,
help='Dictionary')
parser.add_argument('--seed', default=1, type=int,
help='Random seed')
parser.add_argument('--debugdir', type=str,
help='Output directory for debugging')
parser.add_argument('--resume', '-r', default='', nargs='?',
help='Resume the training from snapshot')
parser.add_argument('--minibatches', '-N', type=int, default='-1',
help='Process only N minibatches (for debug)')
parser.add_argument('--verbose', '-V', default=0, type=int,
help='Verbose option')
# task related
parser.add_argument('--train-json', type=str, default=None,
help='Filename of train label data (json)')
parser.add_argument('--valid-json', type=str, default=None,
help='Filename of validation label data (json)')
# network archtecture
# encoder
parser.add_argument('--etype', default='blstmp', type=str,
choices=['blstm', 'blstmp', 'vggblstmp', 'vggblstm'],
help='Type of encoder network architecture')
parser.add_argument('--elayers', default=4, type=int,
help='Number of encoder layers')
parser.add_argument('--eunits', '-u', default=300, type=int,
help='Number of encoder hidden units')
parser.add_argument('--eprojs', default=320, type=int,
help='Number of encoder projection units')
parser.add_argument('--subsample', default=1, type=str,
help='Subsample input frames x_y_z means subsample every x frame at 1st layer, '
'every y frame at 2nd layer etc.')
# loss
parser.add_argument('--ctc_type', default='warpctc', type=str,
choices=['chainer', 'warpctc'],
help='Type of CTC implementation to calculate loss.')
# attention
parser.add_argument('--atype', default='dot', type=str,
choices=['noatt', 'dot', 'add', 'location', 'coverage',
'coverage_location', 'location2d', 'location_recurrent',
'multi_head_dot', 'multi_head_add', 'multi_head_loc',
'multi_head_multi_res_loc'],
help='Type of attention architecture')
parser.add_argument('--adim', default=320, type=int,
help='Number of attention transformation dimensions')
parser.add_argument('--awin', default=5, type=int,
help='Window size for location2d attention')
parser.add_argument('--aheads', default=4, type=int,
help='Number of heads for multi head attention')
parser.add_argument('--aconv-chans', default=-1, type=int,
help='Number of attention convolution channels \
(negative value indicates no location-aware attention)')
parser.add_argument('--aconv-filts', default=100, type=int,
help='Number of attention convolution filters \
(negative value indicates no location-aware attention)')
# decoder
parser.add_argument('--dtype', default='lstm', type=str,
choices=['lstm'],
help='Type of decoder network architecture')
parser.add_argument('--dlayers', default=1, type=int,
help='Number of decoder layers')
parser.add_argument('--dunits', default=320, type=int,
help='Number of decoder hidden units')
parser.add_argument('--mtlalpha', default=0.5, type=float,
help='Multitask learning coefficient, alpha: alpha*ctc_loss + (1-alpha)*att_loss ')
parser.add_argument('--lsm-type', const='', default='', type=str, nargs='?', choices=['', 'unigram'],
help='Apply label smoothing with a specified distribution type')
parser.add_argument('--lsm-weight', default=0.0, type=float,
help='Label smoothing weight')
parser.add_argument('--sampling-probability', default=0.0, type=float,
help='Ratio of predicted labels fed back to decoder')
# model (parameter) related
parser.add_argument('--dropout-rate', default=0.0, type=float,
help='Dropout rate')
# minibatch related
parser.add_argument('--batch-size', '-b', default=50, type=int,
help='Batch size')
parser.add_argument('--maxlen-in', default=800, type=int, metavar='ML',
help='Batch size is reduced if the input sequence length > ML')
parser.add_argument('--maxlen-out', default=150, type=int, metavar='ML',
help='Batch size is reduced if the output sequence length > ML')
parser.add_argument('--n_iter_processes', default=0, type=int,
help='Number of processes of iterator')
# optimization related
parser.add_argument('--opt', default='adadelta', type=str,
choices=['adadelta', 'adam'],
help='Optimizer')
parser.add_argument('--eps', default=1e-8, type=float,
help='Epsilon constant for optimizer')
parser.add_argument('--eps-decay', default=0.01, type=float,
help='Decaying ratio of epsilon')
parser.add_argument('--criterion', default='acc', type=str,
choices=['loss', 'acc'],
help='Criterion to perform epsilon decay')
parser.add_argument('--threshold', default=1e-4, type=float,
help='Threshold to stop iteration')
parser.add_argument('--epochs', '-e', default=30, type=int,
help='Number of maximum epochs')
parser.add_argument('--grad-clip', default=5, type=float,
help='Gradient norm threshold to clip')
parser.add_argument('--num-save-attention', default=3, type=int,
help='Number of samples of attention to be saved')
args = parser.parse_args()
# logging info
if args.verbose > 0:
logging.basicConfig(
level=logging.INFO, 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:
# python 2 case
if platform.python_version_tuple()[0] == '2':
if "clsp.jhu.edu" in subprocess.check_output(["hostname", "-f"]):
cvd = subprocess.check_output(["/usr/local/bin/free-gpu", "-n", str(args.ngpu)]).strip()
logging.info('CLSP: use gpu' + cvd)
os.environ['CUDA_VISIBLE_DEVICES'] = cvd
# python 3 case
else:
if "clsp.jhu.edu" in subprocess.check_output(["hostname", "-f"]).decode():
cvd = subprocess.check_output(["/usr/local/bin/free-gpu", "-n", str(args.ngpu)]).decode().strip()
logging.info('CLSP: use gpu' + cvd)
os.environ['CUDA_VISIBLE_DEVICES'] = cvd
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'])
# set random seed
logging.info('random seed = %d' % args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
# load dictionary for debug log
if args.dict is not None:
with open(args.dict, 'rb') as f:
dictionary = f.readlines()
char_list = [entry.decode('utf-8').split(' ')[0]
for entry in dictionary]
char_list.insert(0, '<blank>')
char_list.append('<eos>')
args.char_list = char_list
else:
args.char_list = None
# train
logging.info('backend = ' + args.backend)
if args.backend == "chainer":
from asr_chainer import train
train(args)
elif args.backend == "pytorch":
from asr_pytorch import train
train(args)
else:
raise ValueError("chainer and pytorch are only supported.")