rm to_static decarator; configure jit save for ctc_activation

paddlespeech/s2t/exps/u2/model.py

@@ -513,9 +513,9 @@ class U2Tester(U2Trainer):
             infer_model.forward_attention_decoder, input_spec=input_spec)
 
         ######################### infer_model.ctc_activation ########################
-        # TODO: 512(encoder_output) be configable
         input_spec = [
-            paddle.static.InputSpec(shape=[1, None, 512], dtype='float32')
+            # encoder_out, (B,T,D)
+            paddle.static.InputSpec(shape=[batch_size, None, model_size], dtype='float32')
         ]
         infer_model.ctc_activation = paddle.jit.to_static(
             infer_model.ctc_activation, input_spec=input_spec)

paddlespeech/s2t/models/u2/u2.py

@@ -599,12 +599,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
         """
         return self.eos
 
-    # @jit.to_static(input_spec=[
+    # @jit.to_static
-    #     paddle.static.InputSpec(shape=[1, None, 80], dtype='float32'), 
-    #     paddle.static.InputSpec(shape=[1], dtype='int32'), 
-    #     -1,
-    #     paddle.static.InputSpec(shape=[None, None, None, None], dtype='float32'), 
-    #     paddle.static.InputSpec(shape=[None, None, None, None], dtype='float32')])
     def forward_encoder_chunk(
             self,
             xs: paddle.Tensor,
@@ -658,10 +653,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
         """
         return self.ctc.log_softmax(xs)
 
-    # @jit.to_static(input_spec=[
+    # @jit.to_static
-    #     paddle.static.InputSpec(shape=[None, None], dtype='int64'), 
-    #     paddle.static.InputSpec(shape=[None], dtype='int64'), 
-    #     paddle.static.InputSpec(shape=[1, None, 512], dtype='float32')])
     def forward_attention_decoder(
             self,
             hyps: paddle.Tensor,