diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/export-for-ncnn.py b/egs/librispeech/ASR/lstm_transducer_stateless3/export-for-ncnn.py
new file mode 120000
index 000000000..d56cff73f
--- /dev/null
+++ b/egs/librispeech/ASR/lstm_transducer_stateless3/export-for-ncnn.py
@@ -0,0 +1 @@
+../lstm_transducer_stateless2/export-for-ncnn.py
\ No newline at end of file
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py b/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py
index 6e51b85e4..cb67fffe4 100644
--- a/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py
@@ -135,6 +135,7 @@ class RNN(EncoderInterface):
         dropout: float = 0.1,
         layer_dropout: float = 0.075,
         aux_layer_period: int = 0,
+        is_pnnx: bool = False,
     ) -> None:
         super(RNN, self).__init__()
 
@@ -176,6 +177,8 @@ class RNN(EncoderInterface):
             else None,
         )
 
+        self.is_pnnx = is_pnnx
+
     def forward(
         self,
         x: torch.Tensor,
@@ -216,7 +219,14 @@ class RNN(EncoderInterface):
         # lengths = ((x_lens - 3) // 2 - 1) // 2 # issue an warning
         #
         # Note: rounding_mode in torch.div() is available only in torch >= 1.8.0
-        lengths = (((x_lens - 3) >> 1) - 1) >> 1
+        if not self.is_pnnx:
+            lengths = (((x_lens - 3) >> 1) - 1) >> 1
+        else:
+            lengths1 = torch.floor((x_lens - 3) / 2)
+            lengths = torch.floor((lengths1 - 1) / 2)
+            lengths = lengths.to(x_lens)
+
+
         if not torch.jit.is_tracing():
             assert x.size(0) == lengths.max().item()
 
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/train.py b/egs/librispeech/ASR/lstm_transducer_stateless3/train.py
index f56b4fd83..6ef4c9860 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless3/train.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless3/train.py
@@ -102,7 +102,28 @@ def add_model_arguments(parser: argparse.ArgumentParser):
         "--encoder-dim",
         type=int,
         default=512,
-        help="Encoder output dimesion.",
+        help="Encoder output dimension.",
+    )
+
+    parser.add_argument(
+        "--decoder-dim",
+        type=int,
+        default=512,
+        help="Decoder output dimension.",
+    )
+
+    parser.add_argument(
+        "--joiner-dim",
+        type=int,
+        default=512,
+        help="Joiner output dimension.",
+    )
+
+    parser.add_argument(
+        "--dim-feedforward",
+        type=int,
+        default=2048,
+        help="Dimension of feed forward.",
     )
 
     parser.add_argument(
@@ -395,14 +416,10 @@ def get_params() -> AttributeDict:
             # parameters for conformer
             "feature_dim": 80,
             "subsampling_factor": 4,
-            "dim_feedforward": 2048,
-            # parameters for decoder
-            "decoder_dim": 512,
-            # parameters for joiner
-            "joiner_dim": 512,
             # parameters for Noam
             "model_warm_step": 3000,  # arg given to model, not for lrate
             "env_info": get_env_info(),
+            "is_pnnx": False,
         }
     )
 
@@ -419,6 +436,7 @@ def get_encoder_model(params: AttributeDict) -> nn.Module:
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
         aux_layer_period=params.aux_layer_period,
+        is_pnnx=params.is_pnnx,
     )
     return encoder