def encode(self, xs, task='all', streaming=False, lookback=False, lookahead=False):
"""Encode acoustic or text features.
Args:
xs (list): A list of length `[B]`, which contains Tensor of size `[T, input_dim]`
task (str): all/ys*/ys_sub1*/ys_sub2*
streaming (bool): streaming encoding
lookback (bool): truncate leftmost frames for lookback in CNN context
lookahead (bool): truncate rightmost frames for lookahead in CNN context
Returns:
eout_dict (dict):
"""
if self.input_type == 'speech':
# Frame stacking
if self.n_stacks > 1:
xs = [stack_frame(x, self.n_stacks, self.n_skips) for x in xs]
# Splicing
if self.n_splices > 1:
xs = [splice(x, self.n_splices, self.n_stacks) for x in xs]
xlens = torch.IntTensor([len(x) for x in xs])
xs = pad_list([np2tensor(x, self.device).float() for x in xs], 0.)
# SpecAugment
if self.specaug is not None and self.training:
xs = self.specaug(xs)
# Weight noise injection
if self.weight_noise_std > 0 and self.training:
self.add_weight_noise(std=self.weight_noise_std)
# Input Gaussian noise injection
if self.input_noise_std > 0 and self.training:
xs = add_input_noise(xs, std=self.input_noise_std)
# Sequence summary network
if self.ssn is not None:
xs = self.ssn(xs, xlens)
elif self.input_type == 'text':
xlens = torch.IntTensor([len(x) for x in xs])
xs = [np2tensor(np.fromiter(x, dtype=np.int64), self.device) for x in xs]
xs = pad_list(xs, self.pad)
xs = self.dropout_emb(self.embed(xs))
# TODO(hirofumi): fix for Transformer
# encoder
eout_dict = self.enc(xs, xlens, task.split('.')[0], streaming, lookback, lookahead)
if self.main_weight < 1 and self.enc_type in ['conv', 'tds', 'gated_conv']:
for sub in ['sub1', 'sub2']:
eout_dict['ys_' + sub]['xs'] = eout_dict['ys']['xs'].clone()
eout_dict['ys_' + sub]['xlens'] = eout_dict['ys']['xlens'][:]
return eout_dict
def test_forward():
batch_size = 4
xmax = 40
input_dim = 80
device = "cpu"
xs = np.random.randn(batch_size, xmax, input_dim).astype(np.float32)
xs = pad_list([np2tensor(x, device).float() for x in xs], 0.)
out = add_input_noise(xs, std=0.075)
assert out.size() == xs.size()