diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py
index a781fb74f92168..e38f05aeecda08 100644
--- a/python/paddle/nn/functional/loss.py
+++ b/python/paddle/nn/functional/loss.py
@@ -1911,6 +1911,7 @@ def ctc_loss(
     blank: int = 0,
     reduction: _ReduceMode = 'mean',
     norm_by_times: bool = False,
+    zero_infinity: bool = False,
 ) -> Tensor:
     """
 
@@ -1927,6 +1928,7 @@ def ctc_loss(
         blank (int, optional): The blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). The data type must be int32. Default: 0.
         reduction (str, optional): Indicate how to average the loss, the candidates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the output loss will be divided by the label_lengths, and then return the mean of quotient; If :attr:`reduction` is ``'sum'``, return the sum of loss; If :attr:`reduction` is ``'none'``, no reduction will be applied. Default: ``'mean'``.
         norm_by_times (bool, optional): Whether to normalize the gradients by the number of time-step, which is also the sequence's length. There is no need to normalize the gradients if reduction mode is 'mean'. Default: False.
+        zero_infinity (bool, optional): If True, set infinite loss to zero. Default: False.
 
     Returns:
         Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and  ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``log_probs``.
@@ -2041,8 +2043,17 @@ def warpctc(
     loss_out = warpctc(
         log_probs, labels, blank, norm_by_times, input_lengths, label_lengths
     )
-
     loss_out = paddle.squeeze(loss_out, [-1])
+
+    if zero_infinity:
+        inf_mask = paddle.isinf(loss_out)
+        zero_value = paddle.zeros_like(loss_out)
+        loss_out = paddle.where(
+            condition=inf_mask,
+            x=zero_value,
+            y=loss_out,
+        )
+
     assert reduction in ['mean', 'sum', 'none']
     if reduction == 'mean':
         loss_out = paddle.mean(loss_out / label_lengths.astype(loss_out.dtype))
diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py
index b27ef6725d9a49..ef2e22ce60624a 100644
--- a/python/paddle/nn/layer/loss.py
+++ b/python/paddle/nn/layer/loss.py
@@ -1352,6 +1352,7 @@ def forward(
         input_lengths: Tensor,
         label_lengths: Tensor,
         norm_by_times: bool = False,
+        zero_infinity: bool = False,
     ) -> Tensor:
         return paddle.nn.functional.ctc_loss(
             log_probs,
@@ -1361,6 +1362,7 @@ def forward(
             self.blank,
             self.reduction,
             norm_by_times=norm_by_times,
+            zero_infinity=zero_infinity,
         )
 
 
diff --git a/test/legacy_test/test_warpctc_op.py b/test/legacy_test/test_warpctc_op.py
index 982ccc21ff2b97..9c6f0dfa5b08a0 100644
--- a/test/legacy_test/test_warpctc_op.py
+++ b/test/legacy_test/test_warpctc_op.py
@@ -754,6 +754,94 @@ def test_functional_api():
 
         test_functional_api()
 
+    def test_ctc_loss_zero_infinity(self):
+        max_time = 1
+        batch = 1
+        n_class = 8
+        logits_np = np.random.randn(max_time, batch, n_class).astype("float32")
+        labels_np = np.random.randint(0, n_class - 1, (batch, 3)).astype(
+            "int32"
+        )
+        input_len_np = np.array([1], dtype=np.int64)
+        label_len_np = np.array([3], dtype=np.int64)
+
+        paddle.enable_static()
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+
+        with paddle.static.program_guard(main_program, startup_program):
+            logits = paddle.static.data(
+                name="logits_il",
+                shape=[max_time, batch, n_class],
+                dtype="float32",
+            )
+            labels = paddle.static.data(
+                name="labels_il", shape=[batch, 3], dtype="int32"
+            )
+            input_len = paddle.static.data(
+                name="input_len_il", shape=[batch], dtype="int64"
+            )
+            label_len = paddle.static.data(
+                name="label_len_il", shape=[batch], dtype="int64"
+            )
+
+            loss = paddle.nn.functional.ctc_loss(
+                log_probs=logits,
+                labels=labels,
+                input_lengths=input_len,
+                label_lengths=label_len,
+                reduction="none",
+                zero_infinity=True,
+                blank=n_class - 1,
+            )
+
+            exe = paddle.static.Executor()
+            loss_val = exe.run(
+                main_program,
+                feed={
+                    "logits_il": logits_np,
+                    "labels_il": labels_np,
+                    "input_len_il": input_len_np,
+                    "label_len_il": label_len_np,
+                },
+                fetch_list=[loss],
+            )[0]
+
+            # illegal sample -> 0
+            np.testing.assert_allclose(loss_val, [0.0], atol=1e-6)
+
+        paddle.disable_static()
+
+    def test_ctc_loss_zero_infinity_dygraph(self):
+        max_time = 1
+        batch = 1
+        n_class = 8
+
+        logits_np = np.random.randn(max_time, batch, n_class).astype("float32")
+        labels_np = np.random.randint(0, n_class - 1, (batch, 3)).astype(
+            "int32"
+        )
+        input_len_np = np.array([1], dtype=np.int64)
+        label_len_np = np.array([3], dtype=np.int64)
+
+        paddle.disable_static()
+        logits = paddle.to_tensor(logits_np)
+        labels = paddle.to_tensor(labels_np)
+        input_len = paddle.to_tensor(input_len_np)
+        label_len = paddle.to_tensor(label_len_np)
+
+        loss = paddle.nn.functional.ctc_loss(
+            log_probs=logits,
+            labels=labels,
+            input_lengths=input_len,
+            label_lengths=label_len,
+            reduction="none",
+            zero_infinity=True,
+            blank=n_class - 1,
+        )
+
+        np.testing.assert_allclose(loss.numpy(), [0.0], rtol=1e-6)
+
 
 if __name__ == "__main__":
     unittest.main()