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
])