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游雁 2023-03-30 17:03:50 +08:00
parent c5acc04e2d
commit 85b8628dbf
3 changed files with 18 additions and 8 deletions
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5
funasr/export/models/encoder/sanm_encoder.py
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@ -163,9 +163,9 @@ class SANMVadEncoder(nn.Module):
self.num_heads = model.encoders[0].self_attn.h
self.hidden_size = model.encoders[0].self_attn.linear_out.out_features
def prepare_mask(self, mask):
def prepare_mask(self, mask, sub_masks):
mask_3d_btd = mask[:, :, None]
sub_masks = subsequent_mask(mask.size(-1)).type(torch.float32)
# sub_masks = subsequent_mask(mask.size(-1)).type(torch.float32)
if len(mask.shape) == 2:
mask_4d_bhlt = 1 - sub_masks[:, None, None, :]
elif len(mask.shape) == 3:
@ -178,6 +178,7 @@ class SANMVadEncoder(nn.Module):
speech: torch.Tensor,
speech_lengths: torch.Tensor,
vad_mask: torch.Tensor,
sub_masks: torch.Tensor,
):
speech = speech * self._output_size ** 0.5
mask = self.make_pad_mask(speech_lengths)

15
funasr/export/models/vad_realtime_transformer.py
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@ -11,7 +11,7 @@ from funasr.punctuation.abs_model import AbsPunctuation
from funasr.punctuation.sanm_encoder import SANMVadEncoder
from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
class VadRealtimeTransformer(AbsPunctuation):
class VadRealtimeTransformer(nn.Module):
def __init__(
self,
@ -36,8 +36,11 @@ class VadRealtimeTransformer(AbsPunctuation):
def forward(self, input: torch.Tensor, text_lengths: torch.Tensor,
vad_indexes: torch.Tensor) -> Tuple[torch.Tensor, None]:
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:
@ -47,7 +50,7 @@ class VadRealtimeTransformer(AbsPunctuation):
"""
x = self.embed(input)
# mask = self._target_mask(input)
h, _ = self.encoder(x, text_lengths, vad_indexes)
h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
y = self.decoder(h)
return y
@ -59,7 +62,9 @@ class VadRealtimeTransformer(AbsPunctuation):
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, :, :]
return (text_indexes, text_lengths, vad_mask)
sub_masks = torch.ones(length, length, dtype=torch.float32)
sub_masks = torch.tril(sub_masks)
return (text_indexes, text_lengths, vad_mask, sub_masks)
def get_input_names(self):
return ['input', 'text_lengths', 'vad_mask']

6
funasr/export/test/test_onnx_punc_vadrealtime.py
View File

@ -9,7 +9,11 @@ if __name__ == '__main__':
output_name = [nd.name for nd in sess.get_outputs()]
def _get_feed_dict(text_length):
return {'input': np.ones((1, text_length), dtype=np.int64), 'text_lengths': np.array([text_length,], dtype=np.int32), 'vad_mask': np.ones((1, 1, text_length, text_length), dtype=np.float32)}
return {'input': np.ones((1, text_length), dtype=np.int64),
'text_lengths': np.array([text_length,], dtype=np.int32),
'vad_mask': np.ones((1, 1, text_length, text_length), dtype=np.float32),
'sub_masks': np.tril(np.ones((text_length, text_length), dtype=np.float32))
}
def _run(feed_dict):
output = sess.run(output_name, input_feed=feed_dict)