| | |
| | | def with_vad(self): |
| | | return True |
| | | |
| | | def get_dummy_inputs(self): |
| | | length = 120 |
| | | text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)) |
| | | # def get_dummy_inputs(self): |
| | | # length = 120 |
| | | # text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)) |
| | | # 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).type(torch.float32) |
| | | # return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :]) |
| | | |
| | | def get_dummy_inputs(self, txt_dir=None): |
| | | from funasr.modules.mask import vad_mask |
| | | length = 10 |
| | | text_indexes = torch.tensor([[266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757]], dtype=torch.int32) |
| | | text_lengths = torch.tensor([length], dtype=torch.int32) |
| | | vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :] |
| | | vad_masks = vad_mask(10, 2, dtype=torch.float32)[None, None, :, :] |
| | | sub_masks = torch.ones(length, length, dtype=torch.float32) |
| | | sub_masks = torch.tril(sub_masks).type(torch.float32) |
| | | return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :]) |
| | | return (text_indexes, text_lengths, vad_masks, sub_masks[None, None, :, :]) |
| | | |
| | | def get_input_names(self): |
| | | return ['input', 'text_lengths', 'vad_mask', 'sub_masks'] |
| | | return ['input', 'text_lengths', 'vad_masks', 'sub_masks'] |
| | | |
| | | def get_output_names(self): |
| | | return ['logits'] |
| | |
| | | 'input': { |
| | | 1: 'feats_length' |
| | | }, |
| | | 'vad_mask': { |
| | | 'vad_masks': { |
| | | 2: 'feats_length1', |
| | | 3: 'feats_length2' |
| | | }, |