diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 4f14b35f9..6a79e7541 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -99,8 +99,9 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
         target_mask = (
             [0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
         )  # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
+        target_mask_lengths = len(target_mask)
         target_mask = torch.tensor(target_mask, dtype=torch.float32)
-
+        target_mask_lengths = torch.tensor([target_mask_lengths], dtype=torch.int32)
         return {
             "speech": speech[0, :, :],
             "speech_lengths": speech_lengths,
@@ -130,30 +131,26 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
                 )
 
         if self.batch_type != "example":
-            b, t, _ = outputs["speech"].shape
-            if b * t > self.batch_size:
-                beg = torch.randint(0, 2, ()).item()
-                logging.info(
-                    f"Warning, b * t: {b * t} > {self.batch_size}, drop half data 1st, beg:{beg}"
-                )
-                for key, data_list in outputs.items():
-                    outputs[key] = outputs[key][beg : beg + b : 2]
+            for i in range(3):
+                outputs = self._filter_badcase(outputs)
+
+        return outputs
+
+    def _filter_badcase(self, outputs, i=0):
+        b, t, _ = outputs["speech"].shape
+        if b * t > self.batch_size:
+            beg = torch.randint(0, 2, ()).item()
+            logging.info(
+                f"Warning, b * t: {b * t} > {self.batch_size}, 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"].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"].max().item()
+            outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
 
-            b, t, _ = outputs["speech"].shape
-            if b * t > self.batch_size:
-                beg = torch.randint(0, 2, ()).item()
-                logging.info(
-                    f"Warning, b * t: {b * t} > {self.batch_size}, drop half data 2nd, beg:{beg}"
-                )
-                for key, data_list in outputs.items():
-                    outputs[key] = outputs[key][beg : beg + b : 2]
-
-            b, t, _ = outputs["speech"].shape
-            if b * t > self.batch_size:
-                beg = torch.randint(0, 2, ()).item()
-                logging.info(
-                    f"Warning, b * t: {b * t} > {self.batch_size}, drop half data 3th, beg:{beg}"
-                )
-                for key, data_list in outputs.items():
-                    outputs[key] = outputs[key][beg : beg + b : 2]
         return outputs