diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py
index b807a3452..8265fc590 100644
--- a/funasr/bin/asr_inference_paraformer.py
+++ b/funasr/bin/asr_inference_paraformer.py
@@ -42,6 +42,7 @@ from funasr.utils import asr_utils, wav_utils, postprocess_utils
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
 from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
+from funasr.utils.timestamp_tools import time_stamp_lfr6_pl, time_stamp_sentence
 
 
 class Speech2Text:
@@ -190,7 +191,8 @@ class Speech2Text:
 
     @torch.no_grad()
     def __call__(
-            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
+            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
+            begin_time: int = 0, end_time: int = None,
     ):
         """Inference
 
@@ -242,6 +244,10 @@ class Speech2Text:
             decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
+        if isinstance(self.asr_model, BiCifParaformer):
+            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+                                                                                   pre_token_length)  # test no bias cif2
+
         results = []
         b, n, d = decoder_out.size()
         for i in range(b):
@@ -284,7 +290,11 @@ class Speech2Text:
                 else:
                     text = None
 
-                results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
+                if isinstance(self.asr_model, BiCifParaformer):
+                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
+                    results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
+                else:
+                    results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
 
         # assert check_return_type(results)
         return results
@@ -683,6 +693,11 @@ def inference_modelscope(
             inference=True,
         )
 
+        if param_dict is not None:
+            use_timestamp = param_dict.get('use_timestamp', True)
+        else:
+            use_timestamp = True
+
         forward_time_total = 0.0
         length_total = 0.0
         finish_count = 0
@@ -724,7 +739,9 @@ def inference_modelscope(
                 result = [results[batch_id][:-2]]
 
                 key = keys[batch_id]
-                for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result):
+                for n, result in zip(range(1, nbest + 1), result):
+                    text, token, token_int, hyp = result[0], result[1], result[2], result[3]
+                    time_stamp = None if len(result) < 5 else result[4]
                     # Create a directory: outdir/{n}best_recog
                     if writer is not None:
                         ibest_writer = writer[f"{n}best_recog"]
@@ -736,8 +753,20 @@ def inference_modelscope(
                         ibest_writer["rtf"][key] = rtf_cur
 
                     if text is not None:
-                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
+                        if use_timestamp and time_stamp is not None:
+                            postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
+                        else:
+                            postprocessed_result = postprocess_utils.sentence_postprocess(token)
+                        time_stamp_postprocessed = ""
+                        if len(postprocessed_result) == 3:
+                            text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
+                                                                                       postprocessed_result[1], \
+                                                                                       postprocessed_result[2]
+                        else:
+                            text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
                         item = {'key': key, 'value': text_postprocessed}
+                        if time_stamp_postprocessed != "":
+                            item['time_stamp'] = time_stamp_postprocessed
                         asr_result_list.append(item)
                         finish_count += 1
                         # asr_utils.print_progress(finish_count / file_count)