diff --git a/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727/infer.py b/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727/infer.py
new file mode 100644
index 000000000..02859c2b5
--- /dev/null
+++ b/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727/infer.py
@@ -0,0 +1,26 @@
+
+##################text二进制数据#####################
+inputs = "跨境河流是养育沿岸|人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员|在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险|向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流问题上的关切|愿意进一步完善双方联合工作机制|凡是|中方能做的我们|都会去做而且会做得更好我请印度朋友们放心中国在上游的|任何开发利用都会经过科学|规划和论证兼顾上下游的利益"
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipline = pipeline(
+    task=Tasks.punctuation,
+    model='damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727',
+    model_revision="v1.0.0",
+    output_dir="./tmp/"
+)
+
+vads = inputs.split("|")
+
+cache_out = []
+rec_result_all="outputs:"
+for vad in vads:
+    rec_result = inference_pipline(text_in=vad, cache=cache_out)
+    #print(rec_result)
+    cache_out = rec_result['cache']
+    rec_result_all += rec_result['text']
+
+print(rec_result_all)
+
diff --git a/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/infer.py b/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/infer.py
index 8dac29299..0da8d25a1 100644
--- a/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/infer.py
+++ b/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/infer.py
@@ -15,7 +15,7 @@ from modelscope.utils.constant import Tasks
 inference_pipline = pipeline(
     task=Tasks.punctuation,
     model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
-    model_revision="v1.1.6",
+    model_revision="v1.1.7",
     output_dir="./tmp/"
 )
 
diff --git a/funasr/bin/punc_inference_launch.py b/funasr/bin/punc_inference_launch.py
index 53db1dfca..e7e3f1558 100755
--- a/funasr/bin/punc_inference_launch.py
+++ b/funasr/bin/punc_inference_launch.py
@@ -75,6 +75,9 @@ def inference_launch(mode, **kwargs):
     if mode == "punc":
         from funasr.bin.punctuation_infer import inference_modelscope
         return inference_modelscope(**kwargs)
+    if mode == "punc_VadRealtime":
+        from funasr.bin.punctuation_infer_vadrealtime import inference_modelscope
+        return inference_modelscope(**kwargs)
     else:
         logging.info("Unknown decoding mode: {}".format(mode))
         return None
diff --git a/funasr/bin/punctuation_infer_vadrealtime.py b/funasr/bin/punctuation_infer_vadrealtime.py
new file mode 100644
index 000000000..d6cc15332
--- /dev/null
+++ b/funasr/bin/punctuation_infer_vadrealtime.py
@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+import argparse
+import logging
+from pathlib import Path
+import sys
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+from typing import Any
+from typing import List
+
+import numpy as np
+import torch
+from typeguard import check_argument_types
+
+from funasr.datasets.preprocessor import CodeMixTokenizerCommonPreprocessor
+from funasr.utils.cli_utils import get_commandline_args
+from funasr.tasks.punctuation import PunctuationTask
+from funasr.torch_utils.device_funcs import to_device
+from funasr.torch_utils.forward_adaptor import ForwardAdaptor
+from funasr.torch_utils.set_all_random_seed import set_all_random_seed
+from funasr.utils import config_argparse
+from funasr.utils.types import str2triple_str
+from funasr.utils.types import str_or_none
+from funasr.punctuation.text_preprocessor import split_to_mini_sentence
+
+
+class Text2Punc:
+
+    def __init__(
+        self,
+        train_config: Optional[str],
+        model_file: Optional[str],
+        device: str = "cpu",
+        dtype: str = "float32",
+    ):
+        #  Build Model
+        model, train_args = PunctuationTask.build_model_from_file(train_config, model_file, device)
+        self.device = device
+        # Wrape model to make model.nll() data-parallel
+        self.wrapped_model = ForwardAdaptor(model, "inference")
+        self.wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
+        # logging.info(f"Model:\n{model}")
+        self.punc_list = train_args.punc_list
+        self.period = 0
+        for i in range(len(self.punc_list)):
+            if self.punc_list[i] == ",":
+                self.punc_list[i] = "，"
+            elif self.punc_list[i] == "?":
+                self.punc_list[i] = "？"
+            elif self.punc_list[i] == "。":
+                self.period = i
+        self.preprocessor = CodeMixTokenizerCommonPreprocessor(
+            train=False,
+            token_type=train_args.token_type,
+            token_list=train_args.token_list,
+            bpemodel=train_args.bpemodel,
+            text_cleaner=train_args.cleaner,
+            g2p_type=train_args.g2p,
+            text_name="text",
+            non_linguistic_symbols=train_args.non_linguistic_symbols,
+        )
+        print("start decoding!!!")
+
+    @torch.no_grad()
+    def __call__(self, text: Union[list, str], cache: list, split_size=20):
+        if cache is not None and len(cache) > 0:
+            precache = "".join(cache)
+        else:
+            precache = ""
+        data = {"text": precache + text}
+        result = self.preprocessor(data=data, uid="12938712838719")
+        split_text = self.preprocessor.pop_split_text_data(result)
+        mini_sentences = split_to_mini_sentence(split_text, split_size)
+        mini_sentences_id = split_to_mini_sentence(data["text"], split_size)
+        assert len(mini_sentences) == len(mini_sentences_id)
+        cache_sent = []
+        cache_sent_id = torch.from_numpy(np.array([], dtype='int32'))
+        sentence_punc_list = []
+        sentence_words_list= []
+        cache_pop_trigger_limit = 200
+        skip_num = 0
+        for mini_sentence_i in range(len(mini_sentences)):
+            mini_sentence = mini_sentences[mini_sentence_i]
+            mini_sentence_id = mini_sentences_id[mini_sentence_i]
+            mini_sentence = cache_sent + mini_sentence
+            mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
+            data = {
+                "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
+                "text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
+                "vad_indexes": torch.from_numpy(np.array([len(cache)-1], dtype='int32')),
+            }
+            data = to_device(data, self.device)
+            y, _ = self.wrapped_model(**data)
+            _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
+            punctuations = indices
+            if indices.size()[0] != 1:
+                punctuations = torch.squeeze(indices)
+            assert punctuations.size()[0] == len(mini_sentence)
+
+            # Search for the last Period/QuestionMark as cache
+            if mini_sentence_i < len(mini_sentences) - 1:
+                sentenceEnd = -1
+                last_comma_index = -1
+                for i in range(len(punctuations) - 2, 1, -1):
+                    if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
+                        sentenceEnd = i
+                        break
+                    if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
+                        last_comma_index = i
+    
+                if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
+                    # The sentence it too long, cut off at a comma.
+                    sentenceEnd = last_comma_index
+                    punctuations[sentenceEnd] = self.period
+                cache_sent = mini_sentence[sentenceEnd + 1:]
+                cache_sent_id = mini_sentence_id[sentenceEnd + 1:]
+                mini_sentence = mini_sentence[0:sentenceEnd + 1]
+                punctuations = punctuations[0:sentenceEnd + 1]
+
+            punctuations_np = punctuations.cpu().numpy()
+            sentence_punc_list += [self.punc_list[int(x)] for x in punctuations_np]
+            sentence_words_list += mini_sentence
+
+        assert len(sentence_punc_list) == len(sentence_words_list)
+        words_with_punc = []
+        sentence_punc_list_out = []
+        for i in range(0, len(sentence_words_list)):
+            if i > 0:
+                if len(sentence_words_list[i][0].encode()) == 1 and len(sentence_words_list[i - 1][-1].encode()) == 1:
+                    sentence_words_list[i] = " " + sentence_words_list[i]
+            if skip_num < len(cache):
+                skip_num += 1
+            else:
+                words_with_punc.append(sentence_words_list[i])
+            if skip_num >= len(cache):
+                sentence_punc_list_out.append(sentence_punc_list[i])
+                if sentence_punc_list[i] != "_":
+                    words_with_punc.append(sentence_punc_list[i])
+        sentence_out = "".join(words_with_punc)
+
+        sentenceEnd = -1
+        for i in range(len(sentence_punc_list) - 2, 1, -1):
+            if sentence_punc_list[i] == "。" or sentence_punc_list[i] == "？":
+               sentenceEnd = i
+               break
+        cache_out = sentence_words_list[sentenceEnd + 1 :]
+        if sentence_out[-1] in self.punc_list:
+            sentence_out = sentence_out[:-1]
+            sentence_punc_list_out[-1] = "_"
+        return sentence_out, sentence_punc_list_out, cache_out
+
+
+def inference(
+    batch_size: int,
+    dtype: str,
+    ngpu: int,
+    seed: int,
+    num_workers: int,
+    output_dir: str,
+    log_level: Union[int, str],
+    train_config: Optional[str],
+    model_file: Optional[str],
+    key_file: Optional[str] = None,
+    data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
+    raw_inputs: Union[List[Any], bytes, str] = None,
+    cache: List[Any] = None,
+    param_dict: dict = None,
+    **kwargs,
+):
+    inference_pipeline = inference_modelscope(
+        output_dir=output_dir,
+        batch_size=batch_size,
+        dtype=dtype,
+        ngpu=ngpu,
+        seed=seed,
+        num_workers=num_workers,
+        log_level=log_level,
+        key_file=key_file,
+        train_config=train_config,
+        model_file=model_file,
+        param_dict=param_dict,
+        **kwargs,
+    )
+    return inference_pipeline(data_path_and_name_and_type, raw_inputs, cache)
+
+
+def inference_modelscope(
+    batch_size: int,
+    dtype: str,
+    ngpu: int,
+    seed: int,
+    num_workers: int,
+    log_level: Union[int, str],
+    #cache: list,
+    key_file: Optional[str],
+    train_config: Optional[str],
+    model_file: Optional[str],
+    output_dir: Optional[str] = None,
+    param_dict: dict = None,
+    **kwargs,
+):
+    assert check_argument_types()
+    logging.basicConfig(
+        level=log_level,
+        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+    )
+
+    if ngpu >= 1 and torch.cuda.is_available():
+        device = "cuda"
+    else:
+        device = "cpu"
+
+    # 1. Set random-seed
+    set_all_random_seed(seed)
+    text2punc = Text2Punc(train_config, model_file, device)
+
+    def _forward(
+        data_path_and_name_and_type,
+        raw_inputs: Union[List[Any], bytes, str] = None,
+        output_dir_v2: Optional[str] = None,
+        cache: List[Any] = None,
+        param_dict: dict = None,
+    ):
+        results = []
+        split_size = 10
+
+        if raw_inputs != None:
+            line = raw_inputs.strip()
+            key = "demo"
+            if line == "":
+                item = {'key': key, 'value': ""}
+                results.append(item)
+                return results
+            #import pdb;pdb.set_trace()
+            result, _, cache = text2punc(line, cache)
+            item = {'key': key, 'value': result, 'cache': cache}
+            results.append(item)
+            return results
+
+        for inference_text, _, _ in data_path_and_name_and_type:
+            with open(inference_text, "r", encoding="utf-8") as fin:
+                for line in fin:
+                    line = line.strip()
+                    segs = line.split("\t")
+                    if len(segs) != 2:
+                        continue
+                    key = segs[0]
+                    if len(segs[1]) == 0:
+                        continue
+                    result, _ = text2punc(segs[1])
+                    item = {'key': key, 'value': result}
+                    results.append(item)
+        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+        if output_path != None:
+            output_file_name = "infer.out"
+            Path(output_path).mkdir(parents=True, exist_ok=True)
+            output_file_path = (Path(output_path) / output_file_name).absolute()
+            with open(output_file_path, "w", encoding="utf-8") as fout:
+                for item_i in results:
+                    key_out = item_i["key"]
+                    value_out = item_i["value"]
+                    fout.write(f"{key_out}\t{value_out}\n")
+        return results
+
+    return _forward
+
+
+def get_parser():
+    parser = config_argparse.ArgumentParser(
+        description="Punctuation inference",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "--log_level",
+        type=lambda x: x.upper(),
+        default="INFO",
+        choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
+        help="The verbose level of logging",
+    )
+
+    parser.add_argument("--output_dir", type=str, required=False)
+    parser.add_argument(
+        "--ngpu",
+        type=int,
+        default=0,
+        help="The number of gpus. 0 indicates CPU mode",
+    )
+    parser.add_argument("--seed", type=int, default=0, help="Random seed")
+    parser.add_argument(
+        "--dtype",
+        default="float32",
+        choices=["float16", "float32", "float64"],
+        help="Data type",
+    )
+    parser.add_argument(
+        "--num_workers",
+        type=int,
+        default=1,
+        help="The number of workers used for DataLoader",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=1,
+        help="The batch size for inference",
+    )
+
+    group = parser.add_argument_group("Input data related")
+    group.add_argument("--data_path_and_name_and_type", type=str2triple_str, action="append", required=False)
+    group.add_argument("--raw_inputs", type=str, required=False)
+    group.add_argument("--cache", type=list, required=False)
+    group.add_argument("--param_dict", type=dict, required=False)
+    group.add_argument("--key_file", type=str_or_none)
+
+    group = parser.add_argument_group("The model configuration related")
+    group.add_argument("--train_config", type=str)
+    group.add_argument("--model_file", type=str)
+
+    return parser
+
+
+def main(cmd=None):
+    print(get_commandline_args(), file=sys.stderr)
+    parser = get_parser()
+    args = parser.parse_args(cmd)
+    kwargs = vars(args)
+    # kwargs.pop("config", None)
+    inference(**kwargs)
+
+
+if __name__ == "__main__":
+    main()