def input_types(self):
return {
"x": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
"mag": NeuralType(('B', 'any', 'D', 'T'), SpectrogramType()),
"max_length": NeuralType(None, LengthsType()),
"repeat": NeuralType(None, IntType()),
}
def output_types(self):
if self.mode == OperationMode.training or self.mode == OperationMode.validation:
return {
"out_repeats":
NeuralType(('B', 'any', 'C', 'D', 'T'), SpectrogramType()),
"final_out":
NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
"residual":
NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
}
else:
return {
"final_out": NeuralType(('B', 'C', 'D', 'T'),
SpectrogramType()),
}
def input_types(self):
return {
'mel_true': NeuralType(('B', 'D', 'T'), SpectrogramType()),
'len_true': NeuralType(('B',), LengthsType()),
'mel_pred': NeuralType(('B', 'D', 'T'), SpectrogramType()),
}
def output_types(self):
return {
"spec": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
}
def input_types(self):
return {
"mel": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
}
def input_types(self):
return {
"x_mag": NeuralType(('B', 'T', 'D'), SpectrogramType()),
"y_mag": NeuralType(('B', 'T', 'D'), SpectrogramType()),
}
def input_types(self):
return {
"x_mag": NeuralType(('B', 'T', 'D'), SpectrogramType()),
"y_mag": NeuralType(('B', 'T', 'D'), SpectrogramType()),
"input_lengths": NeuralType(('B'), LengthsType(), optional=True),
}
