| | |
| | | for li in range(bsz): |
| | | target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long() |
| | | if target_num > 0: |
| | | input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].cuda(), value=0) |
| | | input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0) |
| | | input_mask = input_mask.eq(1) |
| | | input_mask = input_mask.masked_fill(~nonpad_positions, False) |
| | | input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device) |
| | |
| | | for li in range(bsz): |
| | | target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long() |
| | | if target_num > 0: |
| | | input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num], value=0) |
| | | input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0) |
| | | input_mask = input_mask.eq(1) |
| | | input_mask = input_mask.masked_fill(~nonpad_positions, False) |
| | | input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device) |