def synthesize(self, id_list, synth_output, hparams):
"""
Synthesise LF0 from atoms. The run_atom_synth function either loads the original acoustic features or uses an
acoustic model to predict them.
"""
full_output = self.run_atom_synth(id_list, synth_output, hparams)
for id_name, labels in full_output.items():
lf0 = labels[:, -3]
lf0, _ = interpolate_lin(lf0)
vuv = synth_output[id_name][:, 0, 1]
len_diff = len(labels) - len(vuv)
labels = WorldFeatLabelGen.trim_end_sample(labels, int(len_diff / 2), reverse=True)
labels = WorldFeatLabelGen.trim_end_sample(labels, len_diff - int(len_diff / 2))
labels[:, -2] = vuv
# Run the vocoder.
ModelTrainer.synthesize(self, id_list, full_output, hparams)