def __init__(self, idim, odim, args, flag_return=True):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
chainer.Chain.__init__(self)
self.mtlalpha = args.mtlalpha
assert 0 <= self.mtlalpha <= 1, "mtlalpha must be [0,1]"
self.etype = args.etype
self.verbose = args.verbose
self.char_list = args.char_list
self.outdir = args.outdir
# below means the last number becomes eos/sos ID
# note that sos/eos IDs are identical
self.sos = odim - 1
self.eos = odim - 1
# subsample info
# +1 means input (+1) and layers outputs (args.elayer)
subsample = np.ones(args.elayers + 1, dtype=np.int)
if args.etype.endswith("p") and not args.etype.startswith("vgg"):
ss = args.subsample.split("_")
for j in range(min(args.elayers + 1, len(ss))):
subsample[j] = int(ss[j])
else:
logging.warning(
'Subsampling is not performed for vgg*. It is performed in max pooling layers at CNN.')
logging.info('subsample: ' + ' '.join([str(x) for x in subsample]))
self.subsample = subsample
# label smoothing info
if args.lsm_type:
logging.info("Use label smoothing with " + args.lsm_type)
labeldist = label_smoothing_dist(odim, args.lsm_type, transcript=args.train_json)
else:
labeldist = None
with self.init_scope():
# encoder
self.enc = encoder_for(args, idim, self.subsample)
# ctc
self.ctc = ctc_for(args, odim)
# attention
self.att = att_for(args)
# decoder
self.dec = decoder_for(args, odim, self.sos, self.eos, self.att, labeldist)
self.acc = None
self.loss = None
self.flag_return = flag_return
def __init__(self, idim, odim, args, flag_return=True):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
chainer.Chain.__init__(self)
self.mtlalpha = args.mtlalpha
assert 0 <= self.mtlalpha <= 1, "mtlalpha must be [0,1]"
self.etype = args.etype
self.verbose = args.verbose
self.char_list = args.char_list
self.outdir = args.outdir
# below means the last number becomes eos/sos ID
# note that sos/eos IDs are identical
self.sos = odim - 1
self.eos = odim - 1
# subsample info
self.subsample = get_subsample(args, mode="asr", arch="rnn")
# label smoothing info
if args.lsm_type:
logging.info("Use label smoothing with " + args.lsm_type)
labeldist = label_smoothing_dist(odim,
args.lsm_type,
transcript=args.train_json)
else:
labeldist = None
with self.init_scope():
# encoder
self.enc = encoder_for(args, idim, self.subsample)
# ctc
self.ctc = ctc_for(args, odim)
# attention
self.att = att_for(args)
# decoder
self.dec = decoder_for(args, odim, self.sos, self.eos, self.att,
labeldist)
self.acc = None
self.loss = None
self.flag_return = flag_return