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), }