def output_types(self):
return {
"outputs": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()),
"prednet_lengths": NeuralType(tuple('B'), LengthsType()),
"output-states-1": NeuralType((('D', 'B', 'D')), ElementType()),
"output-states-2": NeuralType((('D', 'B', 'D')), ElementType()),
}
def output_types(self):
"""Returns definitions of module output ports.
"""
if not self._fuse_loss_wer:
return {
"outputs": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()),
}
else:
return {
"loss": NeuralType(elements_type=LossType(), optional=True),
"wer": NeuralType(elements_type=ElementType(), optional=True),
"wer_numer": NeuralType(elements_type=ElementType(), optional=True),
"wer_denom": NeuralType(elements_type=ElementType(), optional=True),
}
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"targets": NeuralType(('B', 'T'), LabelsType()),
"target_length": NeuralType(tuple('B'), LengthsType()),
"states": [NeuralType(('D', 'B', 'D'), ElementType(), optional=True)], # must always be last
}
def output_types(self):
"""Returns definitions of module output ports.
"""
return {
"outputs": NeuralType(('B', 'D', 'T'), EmbeddedTextType()),
"prednet_lengths": NeuralType(tuple('B'), LengthsType()),
"states": [NeuralType((('D', 'B', 'D')), ElementType(), optional=True)], # must always be last
}
def input_types(self):
state_type = NeuralType(('D', 'B', 'D'), ElementType())
mytypes = {
'encoder_outputs': NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()),
"targets": NeuralType(('B', 'T'), LabelsType()),
"target_length": NeuralType(tuple('B'), LengthsType()),
'input-states-1': state_type,
'input-states-2': state_type,
}
return mytypes
