def input_types(self): return { "input": NeuralType(('B', 'T', 'D'), EncodedRepresentation()), "seq_lens": NeuralType(('B'), LengthsType()), "conditioning": NeuralType(('B', 'T', 'D'), EncodedRepresentation(), optional=True), }
def input_types(self): return { "input": NeuralType(('B', 'T'), TokenIndex()), "conditioning": NeuralType(('B', 'T', 'D'), EncodedRepresentation(), optional=True), }
def output_types(self): return { "out": NeuralType(('B', 'T', 'D'), EncodedRepresentation()), "log_dur_preds": NeuralType(('B', 'T'), TokenDurationType()), "pitch_preds": NeuralType(('B', 'T'), RegressionValuesType()), "energy_preds": NeuralType(('B', 'T'), RegressionValuesType()), "spec_len": NeuralType(('B'), LengthsType()), }
def input_types(self): return { "x": NeuralType(('B', 'T', 'D'), EncodedRepresentation()), "x_len": NeuralType(('B'), LengthsType()), "dur_target": NeuralType(('B', 'T'), TokenDurationType(), optional=True), "pitch_target": NeuralType(('B', 'T'), RegressionValuesType(), optional=True), "energy_target": NeuralType(('B', 'T'), RegressionValuesType(), optional=True), "spec_len": NeuralType(('B'), LengthsType(), optional=True), }
def output_types(self): return { "out": NeuralType(('B', 'T'), EncodedRepresentation()), }
def input_types(self): return { "enc": NeuralType(('B', 'T', 'D'), EncodedRepresentation()), "enc_mask": NeuralType(('B', 'T', 1), TokenDurationType()), }
def input_types(self): return { "decoder_input": NeuralType(('B', 'T', 'D'), EncodedRepresentation()), "lengths": NeuralType(('B'), LengthsType()), }