def decollate_network_output(output,
_,
seq_lengths=None,
permutation=None,
batch_first=True):
"""Split output into LF0, V/UV and command signals. Return command signals as hidden state."""
# Split pre-net output (command signals).
intern_amps, _ = ModelTrainer.split_batch(output[:, :, 2:], None,
seq_lengths, permutation,
batch_first)
# Split final LF0, V/UV.
output, _ = ModelTrainer.split_batch(output[:, :, :2], None,
seq_lengths, permutation,
batch_first)
return output, intern_amps
def decollate_network_output(output,
hidden,
seq_lengths=None,
permutation=None,
batch_first=True):
# Output of r9y9 Wavenet has batch first, thus output: B x C x T --transpose--> B x T x C
output = np.transpose(output, (0, 2, 1))
if not batch_first:
# output: B x T x C --transpose--> T x B x C
output = np.transpose(output, (1, 0, 2))
return ModelTrainer.split_batch(output,
hidden,
seq_length_output=seq_lengths,
permutation=permutation,
batch_first=batch_first)
