diff --git a/funasr/datasets/openai_datasets/datasets.py b/funasr/datasets/openai_datasets/datasets.py
index f6127b65a..8cb092639 100644
--- a/funasr/datasets/openai_datasets/datasets.py
+++ b/funasr/datasets/openai_datasets/datasets.py
@@ -180,51 +180,43 @@ class OpenAIDataset(torch.utils.data.Dataset):
         return output
 
     def collator(self, samples: list = None):
-        outputs = {}
-        for sample in samples:
-            if sample is None:
-                continue
-            for key in sample.keys():
-                if key not in outputs:
-                    outputs[key] = []
-                outputs[key].append(sample[key])
 
-        for key, data_list in outputs.items():
-            if isinstance(data_list[0], torch.Tensor):
-                if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
+        for idx in range(self.retry):
+            badcase_flag = False
 
-                    pad_value = self.int_pad_value
-                else:
-                    pad_value = self.float_pad_value
+            outputs = {}
+            for sample in samples:
+                if sample is None:
+                    continue
+                for key in sample.keys():
+                    if key not in outputs:
+                        outputs[key] = []
+                    outputs[key].append(sample[key])
 
-                outputs[key] = torch.nn.utils.rnn.pad_sequence(
-                    data_list, batch_first=True, padding_value=pad_value
-                )
-
-        if self.batch_type != "example":
-            for i in range(10):
-                outputs = self._filter_badcase(outputs, i=i)
-
-        return outputs
-
-    def _filter_badcase(self, outputs, i=0):
-        b, t = outputs["input_ids"].shape
-
-        if b * t > self.batch_size * 2:
-            beg = torch.randint(0, 2, ()).item()
-            if b < 2:
-                beg = 0
-            logging.info(
-                f"Warning, b * t: {b * t} > {self.batch_size}, b: {b}, t: {t}, drop half data {i}th, beg:{beg}"
-            )
             for key, data_list in outputs.items():
-                outputs[key] = outputs[key][beg : beg + b : 2]
-            #
-            # speech_lengths_max = outputs["speech_lengths"].max().item()
-            # outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
-            # text_lengths_max = outputs["text_lengths"].max().item()
-            # outputs["text"] = outputs["text"][:, :text_lengths_max]
-            # target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
-            # outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
+                if isinstance(data_list[0], torch.Tensor):
+                    if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
+
+                        pad_value = self.int_pad_value
+                    else:
+                        pad_value = self.float_pad_value
+
+                    outputs[key] = torch.nn.utils.rnn.pad_sequence(
+                        data_list, batch_first=True, padding_value=pad_value
+                    )
+
+            if self.batch_type != "example":
+                b, t = outputs["input_ids"].shape
+                if b * t > self.batch_size * 2:
+                    beg = torch.randint(0, 2, ()).item()
+                    if b < 2:
+                        beg = 0
+                    logging.info(
+                        f"Warning, b * t: {b * t} > {self.batch_size}, b: {b}, t: {t}, drop half data {idx}th, beg:{beg}"
+                    )
+                    samples = samples[beg : beg + b : 2]
+                    continue
+
+            break
 
         return outputs