diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 6a79e7541..6d9b03505 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -50,6 +50,7 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
         self.eos = kwargs.get("eos", "<|endoftext|>")
         self.batch_size = kwargs.get("batch_size")
         self.batch_type = kwargs.get("batch_type")
+        self.prompt_ids_len = 0
 
     def get_source_len(self, index):
         item = self.index_ds[index]
@@ -73,6 +74,9 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
         speech, speech_lengths = extract_fbank(
             data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
         )  # speech: [b, T, d]
+
+        if speech_lengths > self.batch_size:
+            return None
         speech = speech.permute(0, 2, 1)
         target = item["target"]
         if self.preprocessor_text:
@@ -84,9 +88,12 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
         prompt = f"{self.sos}{task}{text_language}"
         prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
         prompt_ids_len = len(prompt_ids) - 1  # [sos, task]
+        self.prompt_ids_len = prompt_ids_len
 
         target_ids = self.tokenizer.encode(target, allowed_special="all")
         target_ids_len = len(target_ids) + 1  # [lid, text]
+        if target_ids_len > 200:
+            return None
 
         eos = self.tokenizer.encode(self.eos, allowed_special="all")  # [eos]
 
@@ -108,16 +115,30 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
             "text": text,
             "text_lengths": text_lengths,
             "target_mask": target_mask,
+            "target_mask_lengths": target_mask_lengths,
         }
 
     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])
 
+        if len(outputs) < 1:
+            logging.info(f"ERROR: data is empty!")
+            outputs = {
+                "speech": torch.rand((10, 128), dtype=torch.float32),
+                "speech_lengths": torch.tensor([10], dtype=torch.int32),
+                "text": torch.tensor([58836], dtype=torch.int32),
+                "text_lengths": torch.tensor([1], dtype=torch.int32),
+                "target_mask": torch.tensor([[0] * (self.prompt_ids_len) + [1] * (1) + [1]]),
+            }
+            return outputs
+
         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:
@@ -132,25 +153,29 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
 
         if self.batch_type != "example":
             for i in range(3):
-                outputs = self._filter_badcase(outputs)
+                outputs = self._filter_badcase(outputs, i=i)
 
         return outputs
 
     def _filter_badcase(self, outputs, i=0):
         b, t, _ = outputs["speech"].shape
-        if b * t > self.batch_size:
+        
+        if b * t > self.batch_size * 1.25:
             beg = torch.randint(0, 2, ()).item()
+            if b < 2:
+                beg = 0
             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()
+                
+            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"].max().item()
+            target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
             outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
 
         return outputs
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index b731bb677..07fb4eb58 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -309,7 +309,7 @@ class SenseVoiceRWKV(nn.Module):
         if len(speech_lengths.size()) > 1:
             speech_lengths = speech_lengths[:, 0]
 
-        batch_size = speech.shape[0]
+        batch_size, frames, _ = speech.shape
 
         if self.activation_checkpoint:
             from torch.utils.checkpoint import checkpoint
@@ -328,6 +328,7 @@ class SenseVoiceRWKV(nn.Module):
         stats["acc"] = acc_att
         stats["loss"] = torch.clone(loss.detach())
         stats["batch_size"] = batch_size
+        stats["batch_size_x_frames"] = frames * batch_size
 
         # force_gatherable: to-device and to-tensor if scalar for DataParallel
         if self.length_normalized_loss: