def supervision_masks( self, cuts: CutSet, use_alignment_if_exists: Optional[str] = None) -> torch.Tensor: """Returns the mask for supervised samples. :param use_alignment_if_exists: optional str, key for alignment type to use for generating the mask. If not exists, fall back on supervision time spans. """ return collate_vectors([ compute_supervisions_frame_mask( cut, frame_shift=self.extractor.frame_shift, use_alignment_if_exists=use_alignment_if_exists) for cut in cuts ])
def supervision_masks(self, cuts: CutSet) -> torch.Tensor: """Returns the mask for supervised samples.""" return collate_vectors([ compute_supervisions_frame_mask(cut, self.extractor.frame_shift) for cut in cuts ])