def __call__(self, hs, ys):
"""CTC forward.
Args:
hs (list of chainer.Variable | N-dimension array): Input variable from encoder.
ys (list of chainer.Variable | N-dimension array): Input variable of decoder.
Returns:
chainer.Variable: A variable holding a scalar value of the CTC loss.
"""
self.loss = None
ilens = [x.shape[0] for x in hs]
olens = [x.shape[0] for x in ys]
# zero padding for hs
y_hat = linear_tensor(self.ctc_lo, F.dropout(
F.pad_sequence(hs), ratio=self.dropout_rate))
y_hat = F.separate(y_hat, axis=1) # ilen list of batch x hdim
# zero padding for ys
y_true = F.pad_sequence(ys, padding=-1) # batch x olen
# get length info
input_length = chainer.Variable(self.xp.array(ilens, dtype=np.int32))
label_length = chainer.Variable(self.xp.array(olens, dtype=np.int32))
logging.info(self.__class__.__name__ + ' input lengths: ' + str(input_length.data))
logging.info(self.__class__.__name__ + ' output lengths: ' + str(label_length.data))
# get ctc loss
self.loss = F.connectionist_temporal_classification(
y_hat, y_true, 0, input_length, label_length)
logging.info('ctc loss:' + str(self.loss.data))
return self.loss
def __call__(self, hs, ys):
"""Core function of the Warp-CTC layer.
Args:
hs (iterable of chainer.Variable | N-dimention array): Input variable from encoder.
ys (iterable of chainer.Variable | N-dimension array): Input variable of decoder.
Returns:
chainer.Variable: A variable holding a scalar value of the CTC loss.
"""
self.loss = None
ilens = [x.shape[0] for x in hs]
olens = [x.shape[0] for x in ys]
# zero padding for hs
y_hat = linear_tensor(self.ctc_lo, F.dropout(
F.pad_sequence(hs), ratio=self.dropout_rate))
y_hat = F.transpose(y_hat, (1, 0, 2)) # batch x frames x hdim
# get length info
logging.info(self.__class__.__name__ + ' input lengths: ' + str(ilens))
logging.info(self.__class__.__name__ + ' output lengths: ' + str(olens))
# get ctc loss
from chainer_ctc.warpctc import ctc as warp_ctc
self.loss = warp_ctc(y_hat, ilens, [cuda.to_cpu(l.data) for l in ys])[0]
logging.info('ctc loss:' + str(self.loss.data))
return self.loss
def __call__(self, hs, ys):
"""CTC forward
:param hs:
:param ys:
:return:
"""
self.loss = None
ilens = [x.shape[0] for x in hs]
olens = [x.shape[0] for x in ys]
# zero padding for hs
y_hat = linear_tensor(self.ctc_lo, F.dropout(
F.pad_sequence(hs), ratio=self.dropout_rate))
y_hat = F.transpose(y_hat, (1, 0, 2)) # batch x frames x hdim
# get length info
logging.info(self.__class__.__name__ + ' input lengths: ' + str(ilens))
logging.info(self.__class__.__name__ + ' output lengths: ' + str(olens))
# get ctc loss
from chainer_ctc.warpctc import ctc as warp_ctc
self.loss = warp_ctc(y_hat, ilens, [cuda.to_cpu(l.data) for l in ys])[0]
logging.info('ctc loss:' + str(self.loss.data))
return self.loss
def log_softmax(self, hs):
"""log_softmax of frame activations
:param hs:
:return:
"""
y_hat = linear_tensor(self.ctc_lo, F.pad_sequence(hs))
return F.log_softmax(y_hat.reshape(-1, y_hat.shape[-1])).reshape(y_hat.shape)
def log_softmax(self, hs):
"""Log_softmax of frame activations.
Args:
hs (list of chainer.Variable | N-dimension array): Input variable from encoder.
Returns:
chainer.Variable: A n-dimension float array.
"""
y_hat = linear_tensor(self.ctc_lo, F.pad_sequence(hs))
return F.log_softmax(y_hat.reshape(-1, y_hat.shape[-1])).reshape(y_hat.shape)
def __call__(self, enc_hs, dec_z, att_prev, scaling=2.0):
"""Compute AttDot forward layer.
Args:
enc_hs (chainer.Variable | N-dimensional array): Input variable from encoder.
dec_z (chainer.Variable | N-dimensional array): Input variable of decoder.
scaling (float): Scaling weight to make attention sharp.
Returns:
chainer.Variable: Weighted sum over flames.
chainer.Variable: Attention weight.
"""
batch = len(enc_hs)
# pre-compute all h outside the decoder loop
if self.pre_compute_enc_h is None:
self.enc_h = F.pad_sequence(enc_hs) # utt x frame x hdim
self.h_length = self.enc_h.shape[1]
# utt x frame x att_dim
self.pre_compute_enc_h = F.tanh(
linear_tensor(self.mlp_enc, self.enc_h))
if dec_z is None:
dec_z = chainer.Variable(
self.xp.zeros((batch, self.dunits), dtype=np.float32))
else:
dec_z = F.reshape(dec_z, (batch, self.dunits))
# <phi (h_t), psi (s)> for all t
u = F.broadcast_to(F.expand_dims(F.tanh(self.mlp_dec(dec_z)), 1),
self.pre_compute_enc_h.shape)
e = F.sum(self.pre_compute_enc_h * u, axis=2) # utt x frame
# Applying a minus-large-number filter to make a probability value zero for a padded area
# simply degrades the performance, and I gave up this implementation
# Apply a scaling to make an attention sharp
w = F.softmax(scaling * e)
# weighted sum over flames
# utt x hdim
c = F.sum(self.enc_h *
F.broadcast_to(F.expand_dims(w, 2), self.enc_h.shape),
axis=1)
return c, w
def __call__(self, enc_hs, dec_z, att_prev, scaling=2.0):
"""Compute AttLoc forward layer.
Args:
enc_hs (chainer.Variable | N-dimensional array): Input variable from encoders.
dec_z (chainer.Variable | N-dimensional array): Input variable of decoder.
att_prev (chainer.Variable | None): Attention weight.
scaling (float): Scaling weight to make attention sharp.
Returns:
chainer.Variable: Weighted sum over flames.
chainer.Variable: Attention weight.
"""
batch = len(enc_hs)
# pre-compute all h outside the decoder loop
if self.pre_compute_enc_h is None:
self.enc_h = F.pad_sequence(enc_hs) # utt x frame x hdim
self.h_length = self.enc_h.shape[1]
# utt x frame x att_dim
self.pre_compute_enc_h = linear_tensor(self.mlp_enc, self.enc_h)
if dec_z is None:
dec_z = chainer.Variable(
self.xp.zeros((batch, self.dunits), dtype=np.float32))
else:
dec_z = F.reshape(dec_z, (batch, self.dunits))
# initialize attention weight with uniform dist.
if att_prev is None:
att_prev = [
self.xp.full(hh.shape[0], 1.0 / hh.shape[0], dtype=np.float32)
for hh in enc_hs
]
att_prev = [chainer.Variable(att) for att in att_prev]
att_prev = F.pad_sequence(att_prev)
# TODO(watanabe) use <chainer variable>.reshpae(), instead of F.reshape()
# att_prev: utt x frame -> utt x 1 x 1 x frame -> utt x att_conv_chans x 1 x frame
att_conv = self.loc_conv(
F.reshape(att_prev, (batch, 1, 1, self.h_length)))
# att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans
att_conv = F.swapaxes(F.squeeze(att_conv, axis=2), 1, 2)
# att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim
att_conv = linear_tensor(self.mlp_att, att_conv)
# dec_z_tiled: utt x frame x att_dim
dec_z_tiled = F.broadcast_to(F.expand_dims(self.mlp_dec(dec_z), 1),
self.pre_compute_enc_h.shape)
# dot with gvec
# utt x frame x att_dim -> utt x frame
# TODO(watanabe) use batch_matmul
e = F.squeeze(linear_tensor(
self.gvec,
F.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled)),
axis=2)
# Applying a minus-large-number filter to make a probability value zero for a padded area
# simply degrades the performance, and I gave up this implementation
# Apply a scaling to make an attention sharp
w = F.softmax(scaling * e)
# weighted sum over flames
# utt x hdim
c = F.sum(self.enc_h *
F.broadcast_to(F.expand_dims(w, 2), self.enc_h.shape),
axis=1)
return c, w
