fix

paddlespeech/s2t/__init__.py

@@ -200,7 +200,7 @@ if not hasattr(paddle.Tensor, 'view'):
 
 
 def view_as(xs: paddle.Tensor, ys: paddle.Tensor) -> paddle.Tensor:
-    return xs.reshape(ys.size())
+    return xs.reshape(ys.shape)
 
 
 if not hasattr(paddle.Tensor, 'view_as'):

paddlespeech/s2t/models/lm/transformer.py

@@ -90,7 +90,7 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
 
     def _target_mask(self, ys_in_pad):
         ys_mask = ys_in_pad != 0
-        m = subsequent_mask(ys_mask.size(-1)).unsqueeze(0)
+        m = subsequent_mask(ys_mask.shape[-1])).unsqueeze(0)
         return ys_mask.unsqueeze(-2) & m
 
     def forward(self, x: paddle.Tensor, t: paddle.Tensor
@@ -112,7 +112,7 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
             in perplexity: p(t)^{-n} = exp(-log p(t) / n)
 
         """
-        batch_size = x.size(0)
+        batch_size = x.shape[0]
         xm = x != 0
         xlen = xm.sum(axis=1)
         if self.embed_drop is not None:

paddlespeech/s2t/modules/encoder.py

@@ -218,7 +218,7 @@ class BaseEncoder(nn.Layer):
         assert xs.shape[0] == 1  # batch size must be one
         # tmp_masks is just for interface compatibility
         # TODO(Hui Zhang): stride_slice not support bool tensor
-        # tmp_masks = paddle.ones([1, xs.size(1)], dtype=paddle.bool)
+        # tmp_masks = paddle.ones([1, xs.shape[1]], dtype=paddle.bool)
         tmp_masks = paddle.ones([1, xs.shape[1]], dtype=paddle.int32)
         tmp_masks = tmp_masks.unsqueeze(1)  #[B=1, C=1, T]