| | |
|---|
| | | self.decoder.embed = None |
|---|
| | | |
|---|
| | | self.use_1st_decoder_loss = use_1st_decoder_loss |
|---|
| | | self.length_normalized_loss = length_normalized_loss |
|---|
| | | |
|---|
| | | def forward( |
|---|
| | | self, |
|---|
| | |
|---|
| | | stats["loss"] = torch.clone(loss.detach()) |
|---|
| | | |
|---|
| | | # force_gatherable: to-device and to-tensor if scalar for DataParallel |
|---|
| | | if self.length_normalized_loss: |
|---|
| | | batch_size = (text_lengths + self.predictor_bias).sum() |
|---|
| | | loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) |
|---|
| | | return loss, stats, weight |
|---|
| | | |
|---|
| | |
|---|
| | | 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) |
|---|
