diff --git a/tests/benchmark/conformer/README.md b/tests/benchmark/conformer/README.md
index 71d5f91b..22e0009d 100644
--- a/tests/benchmark/conformer/README.md
+++ b/tests/benchmark/conformer/README.md
@@ -43,16 +43,6 @@ bash prepare.sh
 bash run.sh
 ```
 
-### Analyse the sp
-```
-bash run_analysis_sp.sh
-```
-
-### Analyse the mp
-```
-bash run_analysis_mp.sh
-```
-
 ### The log
 ```
 {"log_file": "recoder_sp_bs16_fp32_ngpu1.txt",
diff --git a/tests/benchmark/conformer/analysis.py b/tests/benchmark/conformer/analysis.py
deleted file mode 100644
index 610791c8..00000000
--- a/tests/benchmark/conformer/analysis.py
+++ /dev/null
@@ -1,345 +0,0 @@
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import print_function
-
-import argparse
-import json
-import re
-import traceback
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--filename", type=str, help="The name of log which need to analysis.")
-    parser.add_argument(
-        "--log_with_profiler",
-        type=str,
-        help="The path of train log with profiler")
-    parser.add_argument(
-        "--profiler_path", type=str, help="The path of profiler timeline log.")
-    parser.add_argument(
-        "--keyword", type=str, help="Keyword to specify analysis data")
-    parser.add_argument(
-        "--separator",
-        type=str,
-        default=None,
-        help="Separator of different field in log")
-    parser.add_argument(
-        '--position', type=int, default=None, help='The position of data field')
-    parser.add_argument(
-        '--range',
-        type=str,
-        default="",
-        help='The range of data field to intercept')
-    parser.add_argument(
-        '--base_batch_size', type=int, help='base_batch size on gpu')
-    parser.add_argument(
-        '--skip_steps',
-        type=int,
-        default=0,
-        help='The number of steps to be skipped')
-    parser.add_argument(
-        '--model_mode',
-        type=int,
-        default=-1,
-        help='Analysis mode, default value is -1')
-    parser.add_argument('--ips_unit', type=str, default=None, help='IPS unit')
-    parser.add_argument(
-        '--model_name',
-        type=str,
-        default=0,
-        help='training model_name, transformer_base')
-    parser.add_argument(
-        '--mission_name', type=str, default=0, help='training mission name')
-    parser.add_argument(
-        '--direction_id', type=int, default=0, help='training direction_id')
-    parser.add_argument(
-        '--run_mode',
-        type=str,
-        default="sp",
-        help='multi process or single process')
-    parser.add_argument(
-        '--index',
-        type=int,
-        default=1,
-        help='{1: speed, 2:mem, 3:profiler, 6:max_batch_size}')
-    parser.add_argument(
-        '--gpu_num', type=int, default=1, help='nums of training gpus')
-    parser.add_argument(
-        '--use_num', type=int, default=1, help='nums of used recoders')
-    args = parser.parse_args()
-    args.separator = None if args.separator == "None" else args.separator
-    return args
-
-
-def _is_number(num):
-    pattern = re.compile(r'^[-+]?[-0-9]\d*\.\d*|[-+]?\.?[0-9]\d*$')
-    result = pattern.match(num)
-    if result:
-        return True
-    else:
-        return False
-
-
-class TimeAnalyzer(object):
-    def __init__(self,
-                 filename,
-                 keyword=None,
-                 separator=None,
-                 position=None,
-                 range="-1"):
-        if filename is None:
-            raise Exception("Please specify the filename!")
-
-        if keyword is None:
-            raise Exception("Please specify the keyword!")
-
-        self.filename = filename
-        self.keyword = keyword
-        self.separator = separator
-        self.position = position
-        self.range = range
-        self.records = None
-        self._distil()
-
-    def _distil(self):
-        self.records = []
-        with open(self.filename, "r") as f_object:
-            lines = f_object.readlines()
-            for line in lines:
-                if self.keyword not in line:
-                    continue
-                try:
-                    result = None
-
-                    # Distil the string from a line.
-                    line = line.strip()
-                    line_words = line.split(
-                        self.separator) if self.separator else line.split()
-                    print("line_words", line_words)
-                    if args.position:
-                        result = line_words[self.position]
-                    else:
-                        # Distil the string following the keyword.
-                        for i in range(len(line_words) - 1):
-                            if line_words[i] == self.keyword:
-                                result = line_words[i + 1]
-                                break
-
-                    # Distil the result from the picked string.
-                    if not self.range:
-                        result = result[0:]
-                    elif _is_number(self.range):
-                        result = result[0:int(self.range)]
-                    else:
-                        result = result[int(self.range.split(":")[0]):int(
-                            self.range.split(":")[1])]
-                    self.records.append(float(result))
-                except Exception as exc:
-                    pass
-                    #print("line is: {}; separator={}; position={}".format(line, self.separator, self.position))
-        self.records.sort()
-        self.records = self.records[:args.use_num]
-        print("records", self.records)
-        print("Extract {} records: separator={}; position={}".format(
-            len(self.records), self.separator, self.position))
-
-    def _get_fps(self,
-                 mode,
-                 batch_size,
-                 gpu_num,
-                 avg_of_records,
-                 run_mode,
-                 unit=None):
-        if mode == -1 and run_mode == 'sp':
-            assert unit, "Please set the unit when mode is -1."
-            fps = gpu_num * avg_of_records
-        elif mode == -1 and run_mode == 'mp':
-            assert unit, "Please set the unit when mode is -1."
-            fps = gpu_num * avg_of_records  #temporarily, not used now
-            print("------------this is mp")
-        elif mode == 0:
-            # s/step -> samples/s
-            fps = (batch_size * gpu_num) / avg_of_records
-            unit = "samples/s"
-        elif mode == 1:
-            # steps/s -> steps/s
-            fps = avg_of_records
-            unit = "steps/s"
-        elif mode == 2:
-            # s/step -> steps/s
-            fps = 1 / avg_of_records
-            unit = "steps/s"
-        elif mode == 3:
-            # steps/s -> samples/s
-            fps = batch_size * gpu_num * avg_of_records
-            unit = "samples/s"
-        elif mode == 4:
-            # s/epoch -> s/epoch
-            fps = avg_of_records
-            unit = "s/epoch"
-        else:
-            ValueError("Unsupported analysis mode.")
-
-        return fps, unit
-
-    def analysis(self,
-                 batch_size,
-                 gpu_num=1,
-                 skip_steps=0,
-                 mode=-1,
-                 run_mode='sp',
-                 unit=None):
-        if batch_size <= 0:
-            print("base_batch_size should larger than 0.")
-            return 0, ''
-
-        if len(
-                self.records
-        ) <= skip_steps:  # to address the condition which item of log equals to skip_steps
-            print("no records")
-            return 0, ''
-
-        sum_of_records = 0
-        sum_of_records_skipped = 0
-        skip_min = self.records[skip_steps]
-        skip_max = self.records[skip_steps]
-
-        count = len(self.records)
-        for i in range(count):
-            sum_of_records += self.records[i]
-            if i >= skip_steps:
-                sum_of_records_skipped += self.records[i]
-                if self.records[i] < skip_min:
-                    skip_min = self.records[i]
-                if self.records[i] > skip_max:
-                    skip_max = self.records[i]
-
-        avg_of_records = sum_of_records / float(count)
-        avg_of_records_skipped = sum_of_records_skipped / float(count -
-                                                                skip_steps)
-
-        fps, fps_unit = self._get_fps(mode, batch_size, gpu_num, avg_of_records,
-                                      run_mode, unit)
-        fps_skipped, _ = self._get_fps(mode, batch_size, gpu_num,
-                                       avg_of_records_skipped, run_mode, unit)
-        if mode == -1:
-            print("average ips of %d steps, skip 0 step:" % count)
-            print("\tAvg: %.3f %s" % (avg_of_records, fps_unit))
-            print("\tFPS: %.3f %s" % (fps, fps_unit))
-            if skip_steps > 0:
-                print("average ips of %d steps, skip %d steps:" %
-                      (count, skip_steps))
-                print("\tAvg: %.3f %s" % (avg_of_records_skipped, fps_unit))
-                print("\tMin: %.3f %s" % (skip_min, fps_unit))
-                print("\tMax: %.3f %s" % (skip_max, fps_unit))
-                print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
-        elif mode == 1 or mode == 3:
-            print("average latency of %d steps, skip 0 step:" % count)
-            print("\tAvg: %.3f steps/s" % avg_of_records)
-            print("\tFPS: %.3f %s" % (fps, fps_unit))
-            if skip_steps > 0:
-                print("average latency of %d steps, skip %d steps:" %
-                      (count, skip_steps))
-                print("\tAvg: %.3f steps/s" % avg_of_records_skipped)
-                print("\tMin: %.3f steps/s" % skip_min)
-                print("\tMax: %.3f steps/s" % skip_max)
-                print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
-        elif mode == 0 or mode == 2:
-            print("average latency of %d steps, skip 0 step:" % count)
-            print("\tAvg: %.3f s/step" % avg_of_records)
-            print("\tFPS: %.3f %s" % (fps, fps_unit))
-            if skip_steps > 0:
-                print("average latency of %d steps, skip %d steps:" %
-                      (count, skip_steps))
-                print("\tAvg: %.3f s/step" % avg_of_records_skipped)
-                print("\tMin: %.3f s/step" % skip_min)
-                print("\tMax: %.3f s/step" % skip_max)
-                print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
-
-        return round(fps_skipped, 3), fps_unit
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    run_info = dict()
-    run_info["log_file"] = args.filename
-    run_info["model_name"] = args.model_name
-    run_info["mission_name"] = args.mission_name
-    run_info["direction_id"] = args.direction_id
-    run_info["run_mode"] = args.run_mode
-    run_info["index"] = args.index
-    run_info["gpu_num"] = args.gpu_num
-    run_info["FINAL_RESULT"] = 0
-    run_info["JOB_FAIL_FLAG"] = 0
-
-    try:
-        if args.index == 1:
-            if args.gpu_num == 1:
-                run_info["log_with_profiler"] = args.log_with_profiler
-                run_info["profiler_path"] = args.profiler_path
-            analyzer = TimeAnalyzer(args.filename, args.keyword, args.separator,
-                                    args.position, args.range)
-            run_info["FINAL_RESULT"], run_info["UNIT"] = analyzer.analysis(
-                batch_size=args.base_batch_size,
-                gpu_num=args.gpu_num,
-                skip_steps=args.skip_steps,
-                mode=args.model_mode,
-                run_mode=args.run_mode,
-                unit=args.ips_unit)
-    #     if int(os.getenv('job_fail_flag')) == 1 or int(run_info["FINAL_RESULT"]) == 0:
-    #         run_info["JOB_FAIL_FLAG"] = 1
-        elif args.index == 3:
-            run_info["FINAL_RESULT"] = {}
-            records_fo_total = TimeAnalyzer(args.filename, 'Framework overhead',
-                                            None, 3, '').records
-            records_fo_ratio = TimeAnalyzer(args.filename, 'Framework overhead',
-                                            None, 5).records
-            records_ct_total = TimeAnalyzer(args.filename, 'Computation time',
-                                            None, 3, '').records
-            records_gm_total = TimeAnalyzer(args.filename,
-                                            'GpuMemcpy                Calls',
-                                            None, 4, '').records
-            records_gm_ratio = TimeAnalyzer(args.filename,
-                                            'GpuMemcpy                Calls',
-                                            None, 6).records
-            records_gmas_total = TimeAnalyzer(args.filename,
-                                              'GpuMemcpyAsync         Calls',
-                                              None, 4, '').records
-            records_gms_total = TimeAnalyzer(args.filename,
-                                             'GpuMemcpySync          Calls',
-                                             None, 4, '').records
-            run_info["FINAL_RESULT"]["Framework_Total"] = records_fo_total[
-                0] if records_fo_total else 0
-            run_info["FINAL_RESULT"]["Framework_Ratio"] = records_fo_ratio[
-                0] if records_fo_ratio else 0
-            run_info["FINAL_RESULT"][
-                "ComputationTime_Total"] = records_ct_total[
-                    0] if records_ct_total else 0
-            run_info["FINAL_RESULT"]["GpuMemcpy_Total"] = records_gm_total[
-                0] if records_gm_total else 0
-            run_info["FINAL_RESULT"]["GpuMemcpy_Ratio"] = records_gm_ratio[
-                0] if records_gm_ratio else 0
-            run_info["FINAL_RESULT"][
-                "GpuMemcpyAsync_Total"] = records_gmas_total[
-                    0] if records_gmas_total else 0
-            run_info["FINAL_RESULT"]["GpuMemcpySync_Total"] = records_gms_total[
-                0] if records_gms_total else 0
-        else:
-            print("Not support!")
-    except Exception:
-        traceback.print_exc()
-    print("{}".format(json.dumps(run_info))
-          )  # it's required, for the log file path  insert to the database
diff --git a/tests/benchmark/conformer/prepare.sh b/tests/benchmark/conformer/prepare.sh
index 8f03fd1b..29719074 100644
--- a/tests/benchmark/conformer/prepare.sh
+++ b/tests/benchmark/conformer/prepare.sh
@@ -1,4 +1,3 @@
-source ../../../tools/venv/bin/activate
 
 #Enter the example dir
 pushd ../../../examples/aishell/s1
diff --git a/tests/benchmark/conformer/run.sh b/tests/benchmark/conformer/run.sh
index c09bbf09..0ae2c8e2 100644
--- a/tests/benchmark/conformer/run.sh
+++ b/tests/benchmark/conformer/run.sh
@@ -1,8 +1,13 @@
 
 # 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7  paddle=2.1.2  py=37
 # 执行目录：需说明
-CUR_DIR=${PWD}
-source ../../../tools/venv/bin/activate
+CUR_DIR=${PWD} # PaddleSpeech/tests/benchmark/conformer
+cd ../../../
+pip install -e .   # 安装pdspeech
+log_path=${LOG_PATH_INDEX_DIR:-$(pwd)}  #  benchmark系统指定该参数,不需要跑profile时,log_path指向存speed的目录
+cd ${CUR_DIR}
+sed -i '/set\ -xe/d' run_benchmark.sh
+
 #cd **
 pushd ../../../examples/aishell/s1
 # 1 安装该模型需要的依赖 (如需开启优化策略请注明)
@@ -11,26 +16,33 @@ pushd ../../../examples/aishell/s1
 
 source path.sh
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
-
+mkdir -p conf/benchmark
+#yq e ".training.accum_grad=1" conf/conformer.yaml > conf/benchmark/conformer.yaml
+cp conf/conformer.yaml  conf/benchmark/conformer.yaml
+sed -i
 fp_item_list=(fp32)
 bs_item=(16 30)
-config_path=conf/conformer.yaml
+config_path=conf/benchmark/conformer.yaml
 seed=0
 output=exp/conformer
 profiler_options=None
+model_item=conformer
 for fp_item in ${fp_item_list[@]}; do
-    for batch_size in ${bs_item[@]}
+    for bs_item in ${bs_item[@]}
         do
         rm exp -rf
+        log_name=speech_${model_item}_bs${bs_item}_${fp_item}   # 如:clas_MobileNetv1_mp_bs32_fp32_8
         echo "index is speed, 8gpus, run_mode is multi_process, begin, conformer"
         run_mode=mp
         ngpu=8
-        CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${batch_size} ${fp_item} ${CUR_DIR}
-        rm exp -rf
-        echo "index is speed, 1gpus, begin, conformer"
+        CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
+        sleep 60
+        log_name=speech_${model_item}_bs${bs_item}_${fp_item}   # 如:clas_MobileNetv1_mp_bs32_fp32_8
+        echo "index is speed, 1gpus, begin, ${log_name}"
         run_mode=sp
         ngpu=1
-        CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${batch_size} ${fp_item} ${CUR_DIR}
+        CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_1gpus 2>&1   #  (5min)
+        sleep 60
     done
 done
 
diff --git a/tests/benchmark/conformer/run_benchmark.sh b/tests/benchmark/conformer/run_benchmark.sh
index c03a08f3..a6503117 100644
--- a/tests/benchmark/conformer/run_benchmark.sh
+++ b/tests/benchmark/conformer/run_benchmark.sh
@@ -9,20 +9,27 @@ function _set_params(){
     output=${3:-"exp/conformer"}
     seed=${4:-"0"}
     ngpu=${5:-"1"}
-    profiler_options=${6:-"None"}
+    profiler_options=${6:-"1"}
     batch_size=${7:-"32"}
     fp_item=${8:-"fp32"}
-    TRAIN_LOG_DIR=${9:-$(pwd)}
-
+    model_item=${9:-"conformer"}
     benchmark_max_step=0
-
     run_log_path=${TRAIN_LOG_DIR:-$(pwd)}  # TRAIN_LOG_DIR 后续QA设置该参数
+# 添加日志解析需要的参数
+    base_batch_size=${batch_size}
+    mission_name="语音合成"
+    direction_id="1"
+    ips_unit="sequences/sec"
+    skip_steps=10                     # 解析日志，有些模型前几个step耗时长，需要跳过                                    (必填)
+    keyword="ips:"                 # 解析日志，筛选出数据所在行的关键字                                             (必填)
+    index="1"
+    model_name=${model_item}_bs${batch_size}_${fp_item}
 
 #   以下不用修改
     device=${CUDA_VISIBLE_DEVICES//,/ }
     arr=(${device})
     num_gpu_devices=${#arr[*]}
-    log_file=${run_log_path}/recoder_${run_mode}_bs${batch_size}_${fp_item}_ngpu${ngpu}.txt
+    log_file=${run_log_path}/recoder_${model_item}_${run_mode}_bs${batch_size}_${fp_item}_ngpu${ngpu}
 }
 
 function _train(){
@@ -36,11 +43,9 @@ function _train(){
                --benchmark-batch-size ${batch_size}
                --benchmark-max-step ${benchmark_max_step} "
 
-    echo "run_mode "${run_mode}
-
     case ${run_mode} in
-    sp) train_cmd="python3 -u ${BIN_DIR}/train.py "${train_cmd} ;;
-    mp) train_cmd="python3 -u ${BIN_DIR}/train.py "${train_cmd} ;;
+    sp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
+    mp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
     *) echo "choose run_mode(sp or mp)"; exit 1;
     esac
     echo ${train_cmd}
@@ -61,5 +66,8 @@ function _train(){
     fi
 }
 
+source ${BENCHMARK_ROOT}/scripts/run_model.sh   # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
 _set_params $@
-_train
+# _train       # 如果只想产出训练log,不解析,可取消注释
+_run     # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开
+