export

游雁

2023-03-30 85b8628dbf3020e73580b73240804d587ead4eb6

 funasr/export/models/vad_realtime_transformer.py

@@ -11,7 +11,7 @@
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 @@



    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 @@
        """
        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 @@
        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']