diff --git a/ACL_PyTorch/contrib/audio/rawnet2/LICENSE b/ACL_PyTorch/contrib/audio/rawnet2/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/ACL_PyTorch/contrib/audio/rawnet2/README.md b/ACL_PyTorch/contrib/audio/rawnet2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4ead1dd821661c9e1067ef9d47ad53c94bbf6ed2
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/README.md
@@ -0,0 +1,50 @@
+### 1.环境准备
+
+1.安装必要的依赖，测试环境可能已经安装其中的一些不同版本的库了，故手动测试时不推荐使用该命令安装
+
+```
+pip3 install -r requirements.txt
+```
+
+2.获取，开源模型代码
+
+```
+git clone https://github.com/Jungjee/RawNet.git
+cd RawNet
+patch -p1 < ../rawnet2.patch
+cd ..
+```
+
+3.获取权重文件
+
+通过2获得代码仓后，权重文件位置：RawNet\python\RawNet2\Pre-trained_model\rawnet2_best_weights.pt，将其放到当前目录
+
+4.获取数据集 [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) ，下载Audio files测试集，重命名VoxCeleb1，确保VoxCeleb1下全部为id1xxxx文件夹，放到/root/datasets目录,注：该路径为绝对路径
+
+5.获取 [msame工具](https://gitee.com/ascend/tools/tree/master/msame) 和 [benchmark工具](https://gitee.com/ascend/cann-benchmark/tree/master/infer)
+
+将msame和benchmark.x86_64放到与test文件夹同级目录下。
+
+### 2.离线推理
+
+310上执行，执行时使npu-smi info查看设备状态，确保device空闲
+
+备注：
+
+1.需要对onnx模型进行onnxsim优化，否则无法达到精度要求，pth2om.sh脚本首先将pth文件转换为onnx模型，然后分别对bs1和bs16进行onnxsim优化，最后分别转化为om模型
+
+2.eval_acc_perf.sh脚本逐步完成数据前处理bin文件输出、bs1和bs16模型推理、bs1和bs16精度测试，以及bs1和bs16benchmark的性能测试
+
+```
+bash test/pth2om.sh  
+
+bash test/eval_acc_perf.sh --datasets_path=/root/datasets/VoxCeleb1/
+```
+
+评测结果：
+
+| 模型                  | 官网pth精度                                     | 310精度   | 基准性能 | 310性能 |
+| --------------------- | ----------------------------------------------- | --------- | -------- | ------- |
+| Baseline-RawNet2 bs1  | [EER 2.49%](https://github.com/Jungjee/RawNet/) | EER 2.50% | 285.7fps | 72.8fps |
+| Baseline-RawNet2 bs16 | [EER 2.49%](https://github.com/Jungjee/RawNet/) | EER 2.50% | 489.3fps | 77.6fps |
+
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_postprocess.py b/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_postprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0263c606aaa5356b020b795568a12e6a0e8d7aa
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_postprocess.py
@@ -0,0 +1,121 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+
+
+import os
+import sys
+import numpy as np
+from sklearn.metrics import roc_curve
+from scipy.optimize import brentq
+from scipy.interpolate import interp1d
+from tqdm import tqdm
+import argparse
+
+sys.path.append('RawNet/python/RawNet2/')
+from utils import cos_sim
+
+def get_l_embeddings(list_embeddings,bs,path="def"):
+    temp = ""
+    l_embeddings = []
+    index = 0
+    l_utt = []
+    l_code = []
+    with tqdm(total=len(list_embeddings), ncols=70) as pbar:
+        if bs==1:
+            files = sorted(list_embeddings)
+        else:
+            files = list_embeddings.keys()
+        for f in files:
+            if bs==1:
+                t = np.loadtxt(path + "/" + f)
+                t = t.astype(np.float32)
+            else:
+                t = list_embeddings[f]
+            index += 1
+            key = f.replace("$", "/", 2).split("$")[0]
+            if (temp == ""):
+                temp = key
+                l_utt.append(key)
+            if (key == temp):
+                l_code.append(t)
+            else:
+                l_embeddings.append(np.mean(l_code, axis=0))
+                temp = key
+                l_utt.append(key)
+                l_code = []
+                l_code.append(t)
+            if (index == len(files)):
+                l_embeddings.append(np.mean(l_code, axis=0))
+            pbar.update(1)
+    if not len(l_utt) == len(l_embeddings):
+        print(len(l_utt), len(l_embeddings))
+        exit()
+    d_embeddings = {}
+    for k, v in zip(l_utt, l_embeddings):
+        d_embeddings[k] = v
+    return d_embeddings
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', help='bin path', default="", required=True)
+    parser.add_argument('--batch_size', help='batch size', required=True)
+    parser.add_argument('--output', help='result path', default="result/")
+    args = parser.parse_args()
+    batch_size = int(args.batch_size)
+    base = args.input
+    save_dir = args.output
+    d_embeddings = {}
+    if batch_size == 1:
+        for path, dirs, files in os.walk(base):
+            d_embeddings = get_l_embeddings(files,batch_size,path);
+    else:
+        with open('bs16_key.txt', 'r') as f:
+            l_val = f.readlines()
+        bs16_out = []
+        for path, dirs, files in os.walk(base):
+            files = sorted(files, key=lambda x: [int(x.split('_')[0])])
+            for f in files:
+                t = np.loadtxt(path + "/" + f)
+                for i in t:
+                    i.reshape(1024, )
+                    bs16_out.append(i)
+        bs16_out_embeddings = {}
+        if not len(l_val) == len(bs16_out):
+            print(len(l_val), len(bs16_out))
+            exit()
+        for k, v in zip(l_val, bs16_out):
+            bs16_out_embeddings[k] = v
+        d_embeddings = get_l_embeddings(bs16_out_embeddings,batch_size);
+
+    with open('RawNet/trials/vox_original.txt', 'r') as f:
+        l_val_trial = f.readlines()
+    y_score = []
+    y = []
+    f_res = open(save_dir + 'result_detail_bs{}.txt'.format(batch_size), 'w')
+    for line in l_val_trial:
+        trg, utt_a, utt_b = line.strip().split(' ')
+        y.append(int(trg))
+        y_score.append(cos_sim(d_embeddings[utt_a], d_embeddings[utt_b]))
+        f_res.write('{score} {target}\n'.format(score=y_score[-1], target=y[-1]))
+    f_res.close()
+    fpr, tpr, _ = roc_curve(y, y_score, pos_label=1)
+    eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
+    f_eer_301 = open(save_dir + 'result_eer_{}.txt'.format(batch_size), 'w')
+    f_eer_301.write('bs{dir} evaluation EER: {eer}\n'.format(dir=batch_size, eer=eer))
+    f_eer_301.close()
+    print('bs{dir} evaluation EER: {eer}\n'.format(dir=batch_size, eer=eer))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_preprocess.py b/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0e421fb775828d7a58efc6a9d9100e3e3b06471
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_preprocess.py
@@ -0,0 +1,84 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+
+
+import os
+import numpy as np
+import argparse
+import sys
+sys.path.append('RawNet/python/RawNet2/')
+from dataloader import TA_Dataset_VoxCeleb2
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', help='dataset path', default="/root/datasets/VoxCeleb1/")
+    parser.add_argument('--batch_size', help='batch size', default=1)
+    parser.add_argument('--output', help='out bin path', default="bin_out_bs1/")
+    args = parser.parse_args()
+    base_dir = args.input
+    out_dir = args.output
+    batch_size = int(args.batch_size)
+
+    def get_utt_list(src_dir):
+        l_utt = []
+        for path, dirs, files in os.walk(src_dir):
+            path = path.replace('\\', '/')
+            base = '/'.join(path.split('/')[-2:]) + '/'
+            for f in files:
+                if f[-3:] != 'wav':
+                    continue
+                l_utt.append(base + f)
+        return l_utt
+
+    l_val = sorted(get_utt_list(base_dir))
+    TA_evalset = TA_Dataset_VoxCeleb2(list_IDs=l_val,
+                                      return_label=True,
+                                      window_size=11810,
+                                      nb_samp=59049,
+                                      base_dir=base_dir)
+    if batch_size == 1:
+        for item in TA_evalset:
+            n = 0
+            for i in item[0]:
+                i.tofile(out_dir + item[1].replace('/', '$') + "$" + str(n) + ".bin")
+                n += 1
+    else:
+        bs16_key = open('bs16_key.txt', mode='w')
+        bs16 = []
+        n = 0
+        i = 0
+        for item in TA_evalset:
+            l = 0
+            for t in item[0]:
+                bs16_key.write(item[1].replace('/', '$') + "$" + str(n) + ".bin" + "$" + str(l) + "\n")
+                l += 1
+                n += 1
+                bs16.append(t)
+                if n == batch_size:
+                    np.vstack(bs16).tofile(out_dir + str(i) + ".bin")
+                    i += 1
+                    bs16 = []
+                    n = 0
+        if n % batch_size == 0:
+            return
+        for j in range(batch_size - (n % batch_size)):
+            bs16_key.write("temp$" + str(j) + "\n")
+            bs16.append(np.empty((59049,), dtype='float32'))
+        bs16_key.close()
+        np.vstack(bs16).tofile(out_dir + str(i) + ".bin")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_pth2onnx.py b/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_pth2onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cdefee1d34c7c0d35e9c1c58554041471cb548a
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/RawNet2_pth2onnx.py
@@ -0,0 +1,34 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+
+
+import sys
+import torch
+
+sys.path.append('RawNet/python/RawNet2/Pre-trained_model')
+from RawNet.python.RawNet2.parser import get_args
+from model_RawNet2_original_code import RawNet
+
+ptfile = "rawnet2_best_weights.pt"
+args = get_args()
+args.model['nb_classes'] = 6112
+model = RawNet(args.model, device="cpu")
+model.load_state_dict(torch.load(ptfile, map_location=torch.device('cpu')))
+input_names = ["wav"]
+output_names = ["class"]
+dynamic_axes = {'wav': {0: '-1'}, 'class': {0: '-1'}}
+dummy_input = torch.randn(1, 59049)
+export_onnx_file = "RawNet2.onnx"
+torch.onnx.export(model, dummy_input, export_onnx_file, input_names=input_names, dynamic_axes=dynamic_axes,
+                  output_names=output_names, opset_version=11, verbose=True)
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/env.sh b/ACL_PyTorch/contrib/audio/rawnet2/env.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ea514e10c5375ef01ed40d5e7e612e000606e074
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/env.sh
@@ -0,0 +1,6 @@
+export install_path=/usr/local/Ascend/ascend-toolkit/latest
+export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
+export PYTHONPATH=${install_path}/atc/python/site-packages:$PYTHONPATH
+export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
+export ASCEND_OPP_PATH=${install_path}/opp
+export ASCEND_AICPU_PATH=/usr/local/Ascend/ascend-toolkit/latest
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/fusion_switch.cfg b/ACL_PyTorch/contrib/audio/rawnet2/fusion_switch.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..ece7f48ff26239ec868a76a38d6f6b3c5577a4f9
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/fusion_switch.cfg
@@ -0,0 +1,7 @@
+{
+    "Switch":{
+        "UBFusion":{
+            "TbeEltwiseFusionPass":"off"
+         }
+    }
+}
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/modelzoo_level.txt b/ACL_PyTorch/contrib/audio/rawnet2/modelzoo_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20e36b3f785cf4dbfc970929d2d793911d57c314
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/modelzoo_level.txt
@@ -0,0 +1,3 @@
+FuncStatus:OK
+PrecisionStatus:OK
+PerfStatus:NOK
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/perf_t4.sh b/ACL_PyTorch/contrib/audio/rawnet2/perf_t4.sh
new file mode 100644
index 0000000000000000000000000000000000000000..26aa1c974822b20801cd53cd8dda1842560b8248
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/perf_t4.sh
@@ -0,0 +1,21 @@
+#! /bin/bash
+
+trtexec --onnx=RawNet2_sim_bs1.onnx --fp16 --shapes=wav:1x59049 > RawNet2_bs1.log
+perf_str=`grep "GPU.* mean.*ms$" ReID_bs1.log`
+if [ -n "$perf_str" ]; then
+    perf_num=`echo $perf_str | awk -F' ' '{print $16}'`
+else
+    perf_str=`grep "mean.*ms$" ReID_bs1.log`
+    perf_num=`echo $perf_str | awk -F' ' '{print $4}'`
+fi
+awk 'BEGIN{printf "gpu bs1 fps:%.3f\n", 1000*1/('$perf_num'/1)}'
+
+trtexec --onnx=RawNet2_sim_bs16.onnx --fp16 --shapes=wav:16x59049 > RawNet2_bs16.log
+perf_str=`grep "GPU.* mean.*ms$" ReID_bs16.log`
+if [ -n "$perf_str" ]; then
+    perf_num=`echo $perf_str | awk -F' ' '{print $16}'`
+else
+    perf_str=`grep "mean.*ms$" ReID_bs16.log`
+    perf_num=`echo $perf_str | awk -F' ' '{print $4}'`
+fi
+awk 'BEGIN{printf "gpu bs16 fps:%.3f\n", 1000*1/('$perf_num'/16)}'
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/rawnet2.patch b/ACL_PyTorch/contrib/audio/rawnet2/rawnet2.patch
new file mode 100644
index 0000000000000000000000000000000000000000..2c7a30355c7f27004ebac91ba11f97a4ff882f36
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/rawnet2.patch
@@ -0,0 +1,55 @@
+From 2d6205ea3f2b1b61f4eb3063ca3ccf49138c4465 Mon Sep 17 00:00:00 2001
+From: yinin <yzl1154695556@qq.com>
+Date: Sat, 26 Mar 2022 16:26:20 +0800
+Subject: [PATCH] patch
+
+---
+ .../RawNet2/Pre-trained_model/model_RawNet2_original_code.py  | 2 +-
+ python/RawNet2/dataloader.py                                  | 4 ++--
+ python/RawNet2/parser.py                                      | 2 +-
+ 3 files changed, 4 insertions(+), 4 deletions(-)
+
+diff --git a/python/RawNet2/Pre-trained_model/model_RawNet2_original_code.py b/python/RawNet2/Pre-trained_model/model_RawNet2_original_code.py
+index c5981fc..9e3df1d 100755
+--- a/python/RawNet2/Pre-trained_model/model_RawNet2_original_code.py
++++ b/python/RawNet2/Pre-trained_model/model_RawNet2_original_code.py
+@@ -265,7 +265,7 @@ class RawNet(nn.Module):
+         
+         self.sig = nn.Sigmoid()
+         
+-    def forward(self, x, y = 0, is_test=False, is_TS=False):
++    def forward(self, x, y = 0, is_test=True, is_TS=False):
+         #follow sincNet recipe
+         nb_samp = x.shape[0]
+         len_seq = x.shape[1]
+diff --git a/python/RawNet2/dataloader.py b/python/RawNet2/dataloader.py
+index c1791e2..3dfa802 100644
+--- a/python/RawNet2/dataloader.py
++++ b/python/RawNet2/dataloader.py
+@@ -113,8 +113,8 @@ class TA_Dataset_VoxCeleb2(data.Dataset):
+ 
+ 		if not self.return_label:
+ 			return list_X
+-		y = self.labels[ID.split('/')[0]]
+-		return list_X, y 
++		#y = self.labels[ID.split('/')[0]]
++		return list_X, ID 
+ 
+ 	def _normalize_scale(self, x):
+ 		'''
+diff --git a/python/RawNet2/parser.py b/python/RawNet2/parser.py
+index bea9112..4c15f34 100644
+--- a/python/RawNet2/parser.py
++++ b/python/RawNet2/parser.py
+@@ -14,7 +14,7 @@ def str2bool(v):
+ def get_args():
+     parser = argparse.ArgumentParser()
+     #dir
+-    parser.add_argument('-name', type = str, required = True)
++    parser.add_argument('-name', type = str, default = 'rawnet2')
+     parser.add_argument('-save_dir', type = str, default = 'DNNs/')
+     parser.add_argument('-DB', type = str, default = 'DB/VoxCeleb1/')
+     parser.add_argument('-DB_vox2', type = str, default = 'DB/VoxCeleb2/')
+-- 
+2.33.0.windows.2
+
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/requirements.txt b/ACL_PyTorch/contrib/audio/rawnet2/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4c42a0cc0aeca63750fc209152436c342b452cfa
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/requirements.txt
@@ -0,0 +1,9 @@
+torch == 1.5.0
+torchvision == 0.6.0
+onnx == 1.10.1
+onnx-simplifier==0.3.6
+numpy
+scikit-learn
+scipy
+tqdm
+soundfile
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/test/eval_acc_perf.sh b/ACL_PyTorch/contrib/audio/rawnet2/test/eval_acc_perf.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d2ae11deaef2d94f23cdda457f419128b8a0478
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/test/eval_acc_perf.sh
@@ -0,0 +1,92 @@
+datasets_path="/root/datasets/VoxCeleb1/"
+
+for para in $*
+do
+    if [[ $para == --datasets_path* ]]; then
+        datasets_path=`echo ${para#*=}`
+    fi
+done
+
+# 数据预处理输出bin文件夹
+if [ -e "bin_out_bs1" ]; then
+    rm -r bin_out_bs1
+fi
+if [ -e "bin_out_bs16" ]; then
+    rm -r bin_out_bs16
+fi
+mkdir bin_out_bs1
+mkdir bin_out_bs16
+
+python3.7 RawNet2_preprocess.py --input=${datasets_path}  --batch_size=1 --output="bin_out_bs1/"
+
+if [ $? != 0 ]; then
+    echo "fail preprocess"
+fi
+
+python3.7 RawNet2_preprocess.py --input=${datasets_path}  --batch_size=16 --output="bin_out_bs16/"
+
+if [ $? != 0 ]; then
+    echo "fail preprocess"
+else
+    echo "success"
+fi
+
+#om推理
+if [ -e "om_bs1" ]; then
+    rm -r om_bs1
+fi
+if [ -e "om_bs16" ]; then
+    rm -r om_bs16
+fi
+if [ -e "result" ]; then
+    rm -r result
+fi
+
+mkdir om_bs1
+mkdir om_bs16
+mkdir result
+
+source env.sh
+
+./msame --model RawNet2_sim_bs1.om --input bin_out_bs1 --output om_bs1 --outfmt TXT --device 0
+if [ $? != 0 ]; then
+    echo "fail msame!"
+fi
+./msame --model RawNet2_sim_bs16.om --input bin_out_bs16 --output om_bs16 --outfmt TXT --device 0
+
+if [ $? != 0 ]; then
+    echo "fail msame!"
+fi
+
+#om精度判断
+
+python3.7 RawNet2_postprocess.py --input="om_bs1/" --batch_size=1
+
+if [ $? != 0 ]; then
+    echo "fail!"
+else
+    echo "success"
+fi
+
+python3.7 RawNet2_postprocess.py --input="om_bs16/" --batch_size=16
+
+if [ $? != 0 ]; then
+    echo "fail!"
+else
+    echo "success"
+fi
+
+arch=`uname -m`
+./benchmark.${arch} -round=10 -om_path=RawNet2_sim_bs1.om -device_id=0 -batch_size=1
+./benchmark.${arch} -round=10 -om_path=RawNet2_sim_bs16.om -device_id=0 -batch_size=16
+python3.7 test/parse.py result/PureInfer_perf_of_RawNet2_sim_bs1_in_device_0.txt
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+
+python3.7 test/parse.py result/PureInfer_perf_of_RawNet2_sim_bs16_in_device_0.txt
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/test/parse.py b/ACL_PyTorch/contrib/audio/rawnet2/test/parse.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cdf1420bd3d3e7d14f5add67c57cfe2ad399407
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/test/parse.py
@@ -0,0 +1,32 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+
+import sys
+import json
+import re
+
+if __name__ == '__main__':
+    if sys.argv[1].endswith('.json'):
+        result_json = sys.argv[1]
+        with open(result_json, 'r') as f:
+            content = f.read()
+        tops = [i.get('value') for i in json.loads(content).get('value') if 'Top' in i.get('key')]
+        print('om {} top1:{} top5:{}'.format(result_json.split('_')[1].split('.')[0], tops[0], tops[4]))
+    elif sys.argv[1].endswith('.txt'):
+        result_txt = sys.argv[1]
+        with open(result_txt, 'r') as f:
+            content = f.read()
+        txt_data_list = [i.strip() for i in re.findall(r':(.*?),', content.replace('\n', ',') + ',')]
+        fps = float(txt_data_list[7].replace('samples/s', '')) * 4
+        print('310 bs{} fps:{}'.format(result_txt.split('_')[3], fps))
diff --git a/ACL_PyTorch/contrib/audio/rawnet2/test/pth2om.sh b/ACL_PyTorch/contrib/audio/rawnet2/test/pth2om.sh
new file mode 100644
index 0000000000000000000000000000000000000000..86513dd740fa14c0f6aea369796bfffadb77b1ec
--- /dev/null
+++ b/ACL_PyTorch/contrib/audio/rawnet2/test/pth2om.sh
@@ -0,0 +1,14 @@
+
+
+python RawNet2_pth2onnx.py
+
+
+python3.7 -m onnxsim --input-shape="1,59049" RawNet2.onnx RawNet2_sim_bs1.onnx
+
+python3.7 -m onnxsim --input-shape="16,59049" RawNet2.onnx RawNet2_sim_bs16.onnx
+
+source env.sh
+
+atc --framework=5 --model=RawNet2_sim_bs1.onnx --output=RawNet2_sim_bs1 --input_format=ND --input_shape="wav:1,59049" --log=error --soc_version=Ascend310   --fusion_switch_file=fusion_switch.cfg
+
+atc --framework=5 --model=RawNet2_sim_bs16.onnx --output=RawNet2_sim_bs16 --input_format=ND --input_shape="wav:16,59049" --log=error --soc_version=Ascend310   --fusion_switch_file=fusion_switch.cfg
\ No newline at end of file