diff --git a/funasr/datasets/audio_datasets/espnet_samplers.py b/funasr/datasets/audio_datasets/espnet_samplers.py
index b358fa379..004201e53 100644
--- a/funasr/datasets/audio_datasets/espnet_samplers.py
+++ b/funasr/datasets/audio_datasets/espnet_samplers.py
@@ -147,7 +147,9 @@ class EspnetStyleBatchSampler(DistributedSampler):
         start_idx = self.rank * batches_per_rank
         end_idx = start_idx + batches_per_rank
         rank_batches = buffer_batches[start_idx + self.start_step : end_idx]
+
         self.batch_num = len(rank_batches)
+
         logging.info(
             f"rank: {self.rank}, dataloader start from step: {self.start_step}, batch_num: {end_idx-start_idx}, batch_num_after_step: {len(rank_batches)}"
         )
diff --git a/funasr/models/sense_voice/decoder.py b/funasr/models/sense_voice/decoder.py
index 60af29ab8..ff933d77b 100644
--- a/funasr/models/sense_voice/decoder.py
+++ b/funasr/models/sense_voice/decoder.py
@@ -360,6 +360,7 @@ class SenseVoiceDecoder(nn.Module):
         """Score."""
         ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
         logp = self.forward(ys.unsqueeze(0), x.unsqueeze(0), cache=state)
+        logp = torch.log_softmax(logp, dim=-1)
         return logp.squeeze(0)[-1, :], state
 
 
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index 127d5a0a5..22272eefc 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -1264,15 +1264,29 @@ class SenseVoiceSANM(nn.Module):
         if isinstance(task, str):
             task = [task]
         task = "".join([f"<|{x}|>" for x in task])
-        initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
+        
+        sos = kwargs.get("model_conf").get("sos")
+        if isinstance(sos, str):
+            initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
 
-        language = DecodingOptions.get("language", None)
-        language = None if language == "auto" else language
+            language = DecodingOptions.get("language", None)
+            language = None if language == "auto" else language
 
-        sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
-        sos_int = tokenizer.encode(sos, allowed_special="all")
+            sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
+            sos_int = tokenizer.encode(sos, allowed_special="all")
+        else:
+            language = DecodingOptions.get("language", None)
+            language = None if language == "auto" else language
+            initial_prompt = kwargs.get("initial_prompt", f"{task}")
+            initial_prompt_lid = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
+            initial_prompt_lid_int = tokenizer.encode(initial_prompt_lid, allowed_special="all")
+            sos_int = [sos] + initial_prompt_lid_int
         eos = kwargs.get("model_conf").get("eos")
-        eos_int = tokenizer.encode(eos, allowed_special="all")
+        if isinstance(eos, str):
+            eos_int = tokenizer.encode(eos, allowed_special="all")
+        else:
+            eos_int = [eos]
+
         self.beam_search.sos = sos_int
         self.beam_search.eos = eos_int[0]
 
@@ -1298,7 +1312,7 @@ class SenseVoiceSANM(nn.Module):
         self.beam_search.event_score_ga = DecodingOptions.get("gain_tokens_score", [1, 1, 1, 1])
 
         encoder_out, encoder_out_lens = self.encode(
-            speech[None, :, :].permute(0, 2, 1), speech_lengths
+            speech[None, :, :], speech_lengths
         )
 
         if text_token_int is not None: