| | |
| | | text_lengths = torch.tensor([length], dtype=torch.int32) |
| | | vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :] |
| | | sub_masks = torch.ones(length, length, dtype=torch.float32) |
| | | sub_masks = torch.tril(sub_masks) |
| | | return (text_indexes, text_lengths, vad_mask, sub_masks) |
| | | sub_masks = torch.tril(sub_masks).type(torch.float32) |
| | | return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :]) |
| | | |
| | | def get_input_names(self): |
| | | return ['input', 'text_lengths', 'vad_mask'] |
| | | return ['input', 'text_lengths', 'vad_mask', 'sub_masks'] |
| | | |
| | | def get_output_names(self): |
| | | return ['logits'] |
| | |
| | | 2: 'feats_length1', |
| | | 3: 'feats_length2' |
| | | }, |
| | | 'sub_masks': { |
| | | 2: 'feats_length1', |
| | | 3: 'feats_length2' |
| | | }, |
| | | 'logits': { |
| | | 1: 'logits_length' |
| | | }, |