diff --git a/tests/unit/asr/reverse_pad_list.py b/tests/unit/asr/reverse_pad_list.py
new file mode 100644
index 000000000..60e768bcf
--- /dev/null
+++ b/tests/unit/asr/reverse_pad_list.py
@@ -0,0 +1,145 @@
+
+
+
+
+import paddle
+import numpy as np
+import unittest
+
+# from paddlespeech.audio.utils.tensor_utils import reverse_pad_list
+import paddlespeech.s2t
+from paddlespeech.audio.utils.tensor_utils import add_sos_eos
+from paddlespeech.audio.utils.tensor_utils import pad_sequence
+
+def reverse_pad_list(ys_pad: paddle.Tensor,
+                     ys_lens: paddle.Tensor,
+                     pad_value: float=-1.0) -> paddle.Tensor:
+    """Reverse padding for the list of tensors.
+    Args:
+        ys_pad (tensor): The padded tensor (B, Tokenmax).
+        ys_lens (tensor): The lens of token seqs (B)
+        pad_value (int): Value for padding.
+    Returns:
+        Tensor: Padded tensor (B, Tokenmax).
+    Examples:
+        >>> x
+        tensor([[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]])
+        >>> pad_list(x, 0)
+        tensor([[4, 3, 2, 1],
+                [7, 6, 5, 0],
+                [9, 8, 0, 0]])
+    """
+    r_ys_pad = pad_sequence([(paddle.flip(y[:i], [0]))
+                             for y, i in zip(ys_pad, ys_lens)], True, pad_value)
+    return r_ys_pad
+
+def naive_reverse_pad_list_with_sos_eos(r_hyps, r_hyps_lens, sos=5000, eos=5000, ignore_id=-1):
+    r_hyps = reverse_pad_list(r_hyps, r_hyps_lens, float(ignore_id))
+    r_hyps, _ = add_sos_eos(r_hyps, sos, eos, ignore_id)
+    return r_hyps
+
+def reverse_pad_list_with_sos_eos(r_hyps, r_hyps_lens, sos=5000, eos=5000, ignore_id=-1):
+        #   >>> r_hyps = reverse_pad_list(r_hyps, r_hyps_lens, float(self.ignore_id))
+        #   >>> r_hyps, _ = add_sos_eos(r_hyps, self.sos, self.eos, self.ignore_id)
+        max_len = paddle.max(r_hyps_lens)
+        index_range = paddle.arange(0, max_len, 1)
+        seq_len_expand = r_hyps_lens.unsqueeze(1)
+        seq_mask = seq_len_expand > index_range  # (beam, max_len)
+
+        index = (seq_len_expand - 1) - index_range  # (beam, max_len)
+        #   >>> index
+        #   >>> tensor([[ 2,  1,  0],
+        #   >>>         [ 2,  1,  0],
+        #   >>>         [ 0, -1, -2]])
+        index = index * seq_mask
+
+        #   >>> index
+        #   >>> tensor([[2, 1, 0],
+        #   >>>         [2, 1, 0],
+        #   >>>         [0, 0, 0]])
+        def paddle_gather(x, dim, index):
+            index_shape = index.shape
+            index_flatten = index.flatten()
+            if dim < 0:
+                dim = len(x.shape) + dim
+            nd_index = []
+            for k in range(len(x.shape)):
+                if k == dim:
+                    nd_index.append(index_flatten)
+                else:
+                    reshape_shape = [1] * len(x.shape)
+                    reshape_shape[k] = x.shape[k]
+                    x_arange = paddle.arange(x.shape[k], dtype=index.dtype)
+                    x_arange = x_arange.reshape(reshape_shape)
+                    dim_index = paddle.expand(x_arange, index_shape).flatten()
+                    nd_index.append(dim_index)
+            ind2 = paddle.transpose(paddle.stack(nd_index),
+                                    [1, 0]).astype("int64")
+            paddle_out = paddle.gather_nd(x, ind2).reshape(index_shape)
+            return paddle_out
+
+        r_hyps = paddle_gather(r_hyps, 1, index)
+        #   >>> r_hyps
+        #   >>> tensor([[3, 2, 1],
+        #   >>>         [4, 8, 9],
+        #   >>>         [2, 2, 2]])
+        r_hyps = paddle.where(seq_mask, r_hyps, eos)
+        #   >>> r_hyps
+        #   >>> tensor([[3, 2, 1],
+        #   >>>         [4, 8, 9],
+        #   >>>         [2, eos, eos]])
+        B = r_hyps.shape[0]
+        _sos = paddle.ones([B, 1], dtype=r_hyps.dtype) * sos
+        # r_hyps = paddle.concat([hyps[:, 0:1], r_hyps], axis=1)
+        r_hyps = paddle.concat([_sos, r_hyps], axis=1)
+        #   >>> r_hyps
+        #   >>> tensor([[sos, 3, 2, 1],
+        #   >>>         [sos, 4, 8, 9],
+        #   >>>         [sos, 2, eos, eos]])
+        return r_hyps
+
+
+class TestU2Model(unittest.TestCase):
+    def setUp(self):
+        paddle.set_device('cpu')
+
+        self.sos=5000
+        self.eos=5000
+        self.ignore_id=-1
+        self.reverse_hyps = paddle.to_tensor(
+            [[ 4,  3,  2,  1, -1],
+            [ 5,  4,  3,  2,  1]]
+        )
+        self.reverse_hyps_sos_eos = paddle.to_tensor(
+            [[self.sos, 4   , 3   , 2   , 1   , self.eos],
+            [self.sos, 5   , 4   , 3   , 2   , 1   ]]
+        )
+
+        self.hyps = paddle.to_tensor(
+            [
+                [1, 2, 3, 4, -1], 
+                [1, 2, 3, 4, 5]
+            ]
+            )
+
+
+        self.hyps_lens = paddle.to_tensor([4, 5], paddle.int32)
+
+    def test_reverse_pad_list(self):
+        r_hyps = reverse_pad_list(self.hyps, self.hyps_lens)
+        self.assertSequenceEqual(r_hyps.tolist(), self.reverse_hyps.tolist())
+
+    def test_naive_reverse_pad_list_with_sos_eos(self):
+        r_hyps_sos_eos = naive_reverse_pad_list_with_sos_eos(self.hyps, self.hyps_lens)
+        self.assertSequenceEqual(r_hyps_sos_eos.tolist(), self.reverse_hyps_sos_eos.tolist())
+
+    def test_static_reverse_pad_list_with_sos_eos(self):
+        r_hyps_sos_eos_static = reverse_pad_list_with_sos_eos(self.hyps, self.hyps_lens)
+        self.assertSequenceEqual(r_hyps_sos_eos_static.tolist(), self.reverse_hyps_sos_eos.tolist())
+
+
+
+if __name__ == '__main__':
+    unittest.main()
+
+