diff --git a/funasr/models/sense_voice/model_small.py b/funasr/models/sense_voice/model_small.py
index 42a5c0338..d2a19993b 100644
--- a/funasr/models/sense_voice/model_small.py
+++ b/funasr/models/sense_voice/model_small.py
@@ -1631,6 +1631,7 @@ class SenseVoiceQuantizedEncoder(SenseVoiceEncoder):
         name = vq_config.pop("name", "costume_quantizer")
         if name == "costume_quantizer":
             from funasr.models.sense_voice.quantizer.costume_quantizer import CostumeQuantizer
+
             quantizer = CostumeQuantizer(
                 input_size=self.linear_units,
                 **vq_config,
@@ -1639,6 +1640,7 @@ class SenseVoiceQuantizedEncoder(SenseVoiceEncoder):
             return quantizer
         elif name == "lookup_free_quantizer":
             from funasr.models.sense_voice.quantizer.lookup_free_quantizer import LFQ
+
             quantizer = LFQ(
                 input_size=self.linear_units,
                 **vq_config,
@@ -1647,6 +1649,7 @@ class SenseVoiceQuantizedEncoder(SenseVoiceEncoder):
             return quantizer
         elif name == "finite_scalar_quantizer":
             from funasr.models.sense_voice.quantizer.finite_scalar_quantizer import FSQ
+
             quantizer = FSQ(
                 input_size=self.linear_units,
                 **vq_config,
@@ -1716,8 +1719,11 @@ class SenseVoiceQuantizedEncoder(SenseVoiceEncoder):
             x = block(x, mask=padding_mask, position_ids=position_ids)
             if self.quantize_layer_idx is not None and self.quantizer is not None:
                 if layer == self.quantize_layer_idx:
-                    hint_once(f"Quantization at layer {layer} wit {self.quantizer}",
-                              "normalize_quant_enc_out", rank=0)
+                    hint_once(
+                        f"Quantization at layer {layer} wit {self.quantizer}",
+                        "normalize_quant_enc_out",
+                        rank=0,
+                    )
                     x, ret_dict = self.quantize_enc_outs(x)
                     if only_extract_tokens:
                         return (x, ret_dict), olens
@@ -2026,8 +2032,10 @@ class SenseVoiceL(nn.Module):
             if data_lengths is None:
                 data_lengths = [x.shape[0] for x in audio_sample_list]
             speech, speech_lengths = extract_fbank(
-                audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend,
-                data_len=data_lengths
+                audio_sample_list,
+                data_type=kwargs.get("data_type", "sound"),
+                frontend=frontend,
+                data_len=data_lengths,
             )
             time3 = time.perf_counter()
             meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
@@ -2039,9 +2047,10 @@ class SenseVoiceL(nn.Module):
         speech_lengths = speech_lengths.to(device=kwargs["device"])
 
         (outs, ret_dict), out_lens = self.model.encoder(
-            speech, speech_lengths,
-            only_extract_tokens=True
+            speech, speech_lengths, only_extract_tokens=True
         )
+        time4 = time.perf_counter()
+        meta_data["extract_tokens"] = f"{time4 - time3:0.3f}"
         tokens = ret_dict["indices"]
 
         text = "extract_token"
@@ -2057,12 +2066,15 @@ class SenseVoiceL(nn.Module):
             if not hasattr(self, "writer"):
                 out_path = os.path.join(out_dir, f"enc_token")
                 self.writer = kaldiio.WriteHelper(f"ark,scp,f:{out_path}.ark,{out_path}.scp")
-                self.len_writer = open(out_path+"_len.txt", "wt")
+                self.len_writer = open(out_path + "_len.txt", "wt")
             ark_writer = self.writer
             len_writer = self.len_writer
+
         if ark_writer is not None:
             for k, v, l in zip(key, tokens.detach().cpu().numpy(), out_lens):
                 ark_writer(k, v[:l])
                 len_writer.write(f"{k}\t{l}\n")
+            time5 = time.perf_counter()
+            meta_data["write_tokens"] = f"{time5 - time4:0.3f}"
 
         return results, meta_data