| | |
|---|
| | | self.pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)") |
|---|
| | | # self.kwargs = kwargs |
|---|
| | | self.max_token_length = kwargs.get("max_token_length", 1024) |
|---|
| | | self.batch_size_scale_ratio_max = kwargs.get("batch_size_scale_ratio_max", 1.5) |
|---|
| | | |
|---|
| | | def get_source_len(self, index): |
|---|
| | | item = self.index_ds[index] |
|---|
| | |
|---|
| | | badcase_flag = True |
|---|
| | | if badcase_flag: |
|---|
| | | continue |
|---|
| | | input_ids = torch.tensor(input_ids, dtype=torch.int64) |
|---|
| | | input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length] |
|---|
| | | attention_mask = torch.tensor([len(input_ids)], dtype=torch.int32) |
|---|
| | | labels = torch.tensor(labels, dtype=torch.int64) |
|---|
| | | labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length] |
|---|
| | | |
|---|
| | | fbank = speech[0, :, :] |
|---|
| | | fbank_lens = speech_lengths |
|---|
| | |
|---|
| | | |
|---|
| | | if self.batch_type != "example": |
|---|
| | | b, t = outputs["input_ids"].shape |
|---|
| | | if b * t > self.batch_size * 2: |
|---|
| | | if b * t > self.batch_size * self.batch_size_scale_ratio_max: |
|---|
| | | beg = torch.randint(0, 2, ()).item() |
|---|
| | | if b < 2: |
|---|
| | | beg = 0 |
|---|
