| | |
|---|
| | | data_list, batch_first=True, padding_value=pad_value |
|---|
| | | ) |
|---|
| | | |
|---|
| | | # if self.batch_type != "example": |
|---|
| | | # for i in range(10): |
|---|
| | | # outputs = self._filter_badcase(outputs, i=i) |
|---|
| | | if self.batch_type != "example": |
|---|
| | | for i in range(10): |
|---|
| | | outputs = self._filter_badcase(outputs, i=i) |
|---|
| | | |
|---|
| | | return outputs |
|---|
| | | |
|---|
| | | def _filter_badcase(self, outputs, i=0): |
|---|
| | | b, t, _ = outputs["speech"].shape |
|---|
| | | b, t = outputs["input_ids"].shape |
|---|
| | | |
|---|
| | | if b * t > self.batch_size * 1.25: |
|---|
| | | if b * t > self.batch_size * 2: |
|---|
| | | beg = torch.randint(0, 2, ()).item() |
|---|
| | | if b < 2: |
|---|
| | | beg = 0 |
|---|
| | |
|---|
| | | ) |
|---|
| | | for key, data_list in outputs.items(): |
|---|
| | | outputs[key] = outputs[key][beg : beg + b : 2] |
|---|
| | | |
|---|
| | | speech_lengths_max = outputs["speech_lengths"].max().item() |
|---|
| | | outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :] |
|---|
| | | text_lengths_max = outputs["text_lengths"].max().item() |
|---|
| | | outputs["text"] = outputs["text"][:, :text_lengths_max] |
|---|
| | | target_mask_lengths_max = outputs["target_mask_lengths"].max().item() |
|---|
| | | outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max] |
|---|
| | | # |
|---|
| | | # speech_lengths_max = outputs["speech_lengths"].max().item() |
|---|
| | | # outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :] |
|---|
| | | # text_lengths_max = outputs["text_lengths"].max().item() |
|---|
| | | # outputs["text"] = outputs["text"][:, :text_lengths_max] |
|---|
| | | # target_mask_lengths_max = outputs["target_mask_lengths"].max().item() |
|---|
| | | # outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max] |
|---|
| | | |
|---|
| | | return outputs |
|---|
