1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
| from typing import Tuple
|
| import torch
| import torch.nn as nn
|
| from funasr.models.encoder.sanm_encoder import SANMVadEncoder
| from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
|
| class VadRealtimeTransformer(nn.Module):
|
| def __init__(
| self,
| model,
| max_seq_len=512,
| model_name='punc_model',
| **kwargs,
| ):
| super().__init__()
| onnx = False
| if "onnx" in kwargs:
| onnx = kwargs["onnx"]
|
| self.embed = model.embed
| if isinstance(model.encoder, SANMVadEncoder):
| self.encoder = SANMVadEncoder_export(model.encoder, onnx=onnx)
| else:
| assert False, "Only support samn encode."
| # self.encoder = model.encoder
| self.decoder = model.decoder
| self.model_name = model_name
|
|
|
| def forward(self, input: torch.Tensor,
| text_lengths: torch.Tensor,
| vad_indexes: torch.Tensor,
| sub_masks: torch.Tensor,
| ) -> Tuple[torch.Tensor, None]:
| """Compute loss value from buffer sequences.
|
| Args:
| input (torch.Tensor): Input ids. (batch, len)
| hidden (torch.Tensor): Target ids. (batch, len)
|
| """
| x = self.embed(input)
| # mask = self._target_mask(input)
| h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
| y = self.decoder(h)
| return y
|
| def with_vad(self):
| return True
|
| 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_input_names(self):
| return ['input', 'text_lengths', 'vad_mask', 'sub_masks']
|
| def get_output_names(self):
| return ['logits']
|
| def get_dynamic_axes(self):
| return {
| 'input': {
| 1: 'feats_length'
| },
| 'vad_mask': {
| 2: 'feats_length1',
| 3: 'feats_length2'
| },
| 'sub_masks': {
| 2: 'feats_length1',
| 3: 'feats_length2'
| },
| 'logits': {
| 1: 'logits_length'
| },
| }
|
|
