diff --git a/README.md b/README.md
index 5050042..fdb6464 100644
--- a/README.md
+++ b/README.md
@@ -20,9 +20,9 @@ This repository also includes Number Detector and Language classifier [models](h
 
 <details>
 <summary>Real Time Example</summary>
-  
+
 https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-9be7-004c891dd481.mp4
-  
+
 </details>
 
 <br/>
diff --git a/cpp/silero_vad_onnx_1.cpp b/cpp/silero_vad_onnx_1.cpp
new file mode 100644
index 0000000..c5f5590
--- /dev/null
+++ b/cpp/silero_vad_onnx_1.cpp
@@ -0,0 +1,290 @@
+#include <iostream>
+#include <vector>
+#include <sstream>
+#include <cstring>
+#include <chrono>
+
+#include "onnxruntime_cxx_api.h"
+#include "wav.h"
+
+class VadModel
+{
+    // OnnxRuntime resources
+    Ort::Env env;
+    Ort::SessionOptions session_options;
+    std::shared_ptr<Ort::Session> session = nullptr;
+    Ort::AllocatorWithDefaultOptions allocator;
+    Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeCPU);
+
+public:
+    void init_engine_threads(int inter_threads, int intra_threads)
+    {   
+        // The method should be called in each thread/proc in multi-thread/proc work
+        session_options.SetIntraOpNumThreads(intra_threads);
+        session_options.SetInterOpNumThreads(inter_threads);
+        session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
+    }
+
+    void init_onnx_model(const std::string &model_path)
+    {   
+        // Init threads = 1 for 
+        init_engine_threads(1, 1);
+        // Load model
+        session = std::make_shared<Ort::Session>(env, model_path.c_str(), session_options);
+    }
+
+    void reset_states()
+    {
+        // Call reset before each audio start
+        std::memset(_h.data(), 0.0f, _h.size() * sizeof(float));
+        std::memset(_c.data(), 0.0f, _c.size() * sizeof(float));
+        triggerd = false;
+        temp_end = 0;
+        current_sample = 0;
+    }
+
+    // Call it in predict func. if you prefer raw bytes input.
+    void bytes_to_float_tensor(const char *pcm_bytes) 
+    {
+        std::memcpy(input.data(), pcm_bytes, window_size_samples * sizeof(int16_t));
+        for (int i = 0; i < window_size_samples; i++)
+        {
+            input[i] = static_cast<float>(input[i]) / 32768; // int16_t normalized to float
+        }
+    }
+
+
+    void predict(const std::vector<float> &data) // const char *data
+    {
+        // bytes_to_float_tensor(data); 
+        
+        // Infer
+        // Inputs
+        input.assign(data.begin(), data.end());
+        Ort::Value input_ort = Ort::Value::CreateTensor<float>(
+            memory_info, input.data(), input.size(), input_node_dims, 2);
+        // std::cout << "input size:" << input.size() << std::endl;
+        Ort::Value sr_ort = Ort::Value::CreateTensor<int64_t>(
+            memory_info, sr.data(), sr.size(), sr_node_dims, 1);
+        Ort::Value h_ort = Ort::Value::CreateTensor<float>(
+            memory_info, _h.data(), _h.size(), hc_node_dims, 3);
+        Ort::Value c_ort = Ort::Value::CreateTensor<float>(
+            memory_info, _c.data(), _c.size(), hc_node_dims, 3);
+
+        ort_inputs.clear(); // clear inputs
+        ort_inputs.emplace_back(std::move(input_ort));
+        ort_inputs.emplace_back(std::move(sr_ort));
+        ort_inputs.emplace_back(std::move(h_ort));
+        ort_inputs.emplace_back(std::move(c_ort));
+
+        // Infer
+        ort_outputs = session->Run(
+            Ort::RunOptions{nullptr},
+            input_node_names.data(), ort_inputs.data(), ort_inputs.size(),
+            output_node_names.data(), output_node_names.size());
+
+        // out put Probability & update h,c recursively
+        float output = ort_outputs[0].GetTensorMutableData<float>()[0];
+        float *hn = ort_outputs[1].GetTensorMutableData<float>();
+        std::memcpy(_h.data(), hn, size_hc * sizeof(float));
+        float *cn = ort_outputs[2].GetTensorMutableData<float>();
+        std::memcpy(_c.data(), cn, size_hc * sizeof(float));
+
+        // Push forward sample index
+        current_sample += window_size_samples;
+        
+        // 1) Reset temp_end when > threshold 
+        if ((output >= threshold) && (temp_end != 0))
+        {
+            temp_end = 0;
+        }
+        // 2) Trigger and start sentence
+        if ((output >= threshold) && (triggerd == false))
+        {
+            triggerd = true;
+            speech_start = current_sample - speech_pad_samples;
+            printf("{ start: %.3f s }\n", 1.0 * current_sample / sample_rate);
+        }
+        // 3) Speaking 
+        if ((output >= (threshold - 0.15)) && (triggerd == true))
+        {
+            printf("{ speaking: %.3f s }\n", 1.0 * current_sample / sample_rate);
+        }
+        // 4) End 
+        if ((output < (threshold - 0.15)) && (triggerd == true))
+        {
+
+            if (temp_end != 0)
+            {
+                temp_end = current_sample;
+            }
+            // a. silence < min_slience_samples, continue speaking 
+            if ((current_sample - temp_end) < min_silence_samples)
+            {
+                printf("{ speaking: %.3f s }\n", 1.0 * current_sample / sample_rate);
+            }
+            // b. silence >= min_slience_samples, end speaking
+            else
+            {
+                speech_end = temp_end + speech_pad_samples;
+                temp_end = 0;
+                triggerd = false;
+                printf("{ end: %.3f s }\n", 1.0 * current_sample / sample_rate);
+            }
+        }
+        // 5) Silence
+        if ((output < threshold) && (triggerd == false))
+        {
+            printf("{ silence: %.3f s }\n", 1.0 * current_sample / sample_rate);
+        }
+
+    }
+
+    // Print input output shape of the model
+    void GetInputOutputInfo(
+        const std::shared_ptr<Ort::Session> &session,
+        std::vector<const char *> *in_names, std::vector<const char *> *out_names)
+    {
+        Ort::AllocatorWithDefaultOptions allocator;
+        // Input info
+        int num_nodes = session->GetInputCount();
+        in_names->resize(num_nodes);
+        for (int i = 0; i < num_nodes; ++i)
+        {
+            char *name = session->GetInputName(i, allocator);
+            Ort::TypeInfo type_info = session->GetInputTypeInfo(i);
+            auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
+            ONNXTensorElementDataType type = tensor_info.GetElementType();
+            std::vector<int64_t> node_dims = tensor_info.GetShape();
+            std::stringstream shape;
+            for (auto j : node_dims)
+            {
+                shape << j;
+                shape << " ";
+            }
+            std::cout << "\tInput " << i << " : name=" << name << " type=" << type
+                      << " dims=" << shape.str() << std::endl;
+            (*in_names)[i] = name;
+        }
+        // Output info
+        num_nodes = session->GetOutputCount();
+        out_names->resize(num_nodes);
+        for (int i = 0; i < num_nodes; ++i)
+        {
+            char *name = session->GetOutputName(i, allocator);
+            Ort::TypeInfo type_info = session->GetOutputTypeInfo(i);
+            auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
+            ONNXTensorElementDataType type = tensor_info.GetElementType();
+            std::vector<int64_t> node_dims = tensor_info.GetShape();
+            std::stringstream shape;
+            for (auto j : node_dims)
+            {
+                shape << j;
+                shape << " ";
+            }
+            std::cout << "\tOutput " << i << " : name=" << name << " type=" << type
+                      << " dims=" << shape.str() << std::endl;
+            ;
+            (*out_names)[i] = name;
+        }
+    }
+
+private:
+    // model config
+    int64_t window_size_samples;  // Assign when init, support 256 512 768 for 8k; 512 1024 1536 for 16k.
+    int sample_rate;
+    int sr_per_ms;  // Assign when init, support 8 or 16
+    float threshold = 0.5;
+    int min_silence_samples; // sr_per_ms * #ms
+    int speech_pad_samples = 0; // Can be used in offline infer to get as much speech as possible
+
+    // model states
+    bool triggerd = false;
+    unsigned int speech_start = 0; 
+    unsigned int speech_end = 0;
+    unsigned int temp_end = 0;
+    unsigned int current_sample = 0;    
+    // MAX 4294967295 samples / 8sample per ms / 1000 / 60 = 8947 minutes  
+    float output;
+
+    // Onnx model
+    // Inputs
+    std::vector<Ort::Value> ort_inputs;
+    
+    std::vector<const char *> input_node_names = {"input", "sr", "h", "c"};
+    std::vector<float> input;
+    std::vector<int64_t> sr;
+    unsigned int size_hc = 2 * 1 * 64; // It's FIXED.
+    std::vector<float> _h;
+    std::vector<float> _c;
+
+    int64_t input_node_dims[2] = {}; 
+    const int64_t sr_node_dims[1] = {1};
+    const int64_t hc_node_dims[3] = {2, 1, 64};
+
+    // Outputs
+    std::vector<Ort::Value> ort_outputs;
+    std::vector<const char *> output_node_names = {"output", "hn", "cn"};
+    
+
+public:
+    // Construct init
+    VadModel(const std::string ModelPath, 
+             int sample_rate, int frame_size, 
+             float threshold, int min_silence_duration_ms, int speech_pad_ms) 
+    {
+        init_onnx_model(ModelPath);
+        sr_per_ms = sample_rate / 1000;
+        min_silence_samples = sr_per_ms * min_silence_duration_ms;
+        speech_pad_samples = sr_per_ms * speech_pad_ms;
+        window_size_samples = frame_size * sr_per_ms; // Input 64ms/frame * 8ms = 512 samples/frame
+        input.resize(window_size_samples);
+        input_node_dims[0] = 1;
+        input_node_dims[1] = window_size_samples;
+        // std::cout << "== Input size" << input.size() << std::endl;
+        _h.resize(size_hc);
+        _c.resize(size_hc);
+        sr.resize(1);
+    }
+
+};
+
+int main()
+{
+
+    // Read wav
+    wav::WavReader wav_reader("silero-vad-master/test_audios/test0_for_vad.wav");
+
+    std::vector<int16_t> data(wav_reader.num_samples());
+    std::vector<float> input_wav(wav_reader.num_samples());
+
+    for (int i = 0; i < wav_reader.num_samples(); i++)
+    {
+        data[i] = static_cast<int16_t>(*(wav_reader.data() + i));
+    }
+
+    for (int i = 0; i < wav_reader.num_samples(); i++)
+    {
+        input_wav[i] = static_cast<float>(data[i]) / 32768;
+    }
+
+    std::string path = "silero-vad-master/files/silero_vad.onnx";
+    int test_sr = 8000;
+    int test_frame_ms = 64;
+    int test_window_samples = test_frame_ms * (test_sr/1000);
+    VadModel vad(path, test_sr, test_frame_ms);
+    // std::cout << "== 3" << std::endl;
+    // std::cout << vad.window_size_samples1() << std::endl;
+
+    for (int j = 0; j < wav_reader.num_samples(); j += test_window_samples)
+    {
+        std::vector<float> r{&input_wav[0] + j, &input_wav[0] + j + test_window_samples};
+        auto start = std::chrono::high_resolution_clock::now();
+        // Predict and print throughout process time
+        vad.predict(r);
+        auto end = std::chrono::high_resolution_clock::now();
+        auto elapsed_time = std::chrono::duration_cast<std::chrono::nanoseconds>(end-start);
+        std::cout << "== Elapsed time: " << elapsed_time.count() << "ns" << " ==" <<std::endl;
+
+    }
+}
diff --git a/cpp/wav.h b/cpp/wav.h
new file mode 100644
index 0000000..097f1c2
--- /dev/null
+++ b/cpp/wav.h
@@ -0,0 +1,205 @@
+// Copyright (c) 2016 Personal (Binbin Zhang)
+//
+// 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.
+
+
+#ifndef FRONTEND_WAV_H_
+#define FRONTEND_WAV_H_
+
+#include <assert.h>
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include <string>
+
+// #include "utils/log.h"
+
+namespace wav {
+
+struct WavHeader {
+  char riff[4];  // "riff"
+  unsigned int size;
+  char wav[4];  // "WAVE"
+  char fmt[4];  // "fmt "
+  unsigned int fmt_size;
+  uint16_t format;
+  uint16_t channels;
+  unsigned int sample_rate;
+  unsigned int bytes_per_second;
+  uint16_t block_size;
+  uint16_t bit;
+  char data[4];  // "data"
+  unsigned int data_size;
+};
+
+class WavReader {
+ public:
+  WavReader() : data_(nullptr) {}
+  explicit WavReader(const std::string& filename) { Open(filename); }
+
+  bool Open(const std::string& filename) {
+    FILE* fp = fopen(filename.c_str(), "rb"); //文件读取
+    if (NULL == fp) {
+      std::cout << "Error in read " << filename;
+      return false;
+    }
+
+    WavHeader header;
+    fread(&header, 1, sizeof(header), fp);
+    if (header.fmt_size < 16) {
+      fprintf(stderr,
+              "WaveData: expect PCM format data "
+              "to have fmt chunk of at least size 16.\n");
+      return false;
+    } else if (header.fmt_size > 16) {
+      int offset = 44 - 8 + header.fmt_size - 16;
+      fseek(fp, offset, SEEK_SET);
+      fread(header.data, 8, sizeof(char), fp);
+    }
+    // check "riff" "WAVE" "fmt " "data"
+
+    // Skip any sub-chunks between "fmt" and "data".  Usually there will
+    // be a single "fact" sub chunk, but on Windows there can also be a
+    // "list" sub chunk.
+    while (0 != strncmp(header.data, "data", 4)) {
+      // We will just ignore the data in these chunks.
+      fseek(fp, header.data_size, SEEK_CUR);
+      // read next sub chunk
+      fread(header.data, 8, sizeof(char), fp);
+    }
+
+    num_channel_ = header.channels;
+    sample_rate_ = header.sample_rate;
+    bits_per_sample_ = header.bit;
+    int num_data = header.data_size / (bits_per_sample_ / 8);
+    data_ = new float[num_data]; // Create 1-dim array
+    num_samples_ = num_data / num_channel_;
+
+    for (int i = 0; i < num_data; ++i) {
+      switch (bits_per_sample_) {
+        case 8: {
+          char sample;
+          fread(&sample, 1, sizeof(char), fp);
+          data_[i] = static_cast<float>(sample);
+          break;
+        }
+        case 16: {
+          int16_t sample;
+          fread(&sample, 1, sizeof(int16_t), fp);
+          // std::cout << sample;
+          data_[i] = static_cast<float>(sample);
+          // std::cout << data_[i];
+          break;
+        }
+        case 32: {
+          int sample;
+          fread(&sample, 1, sizeof(int), fp);
+          data_[i] = static_cast<float>(sample);
+          break;
+        }
+        default:
+          fprintf(stderr, "unsupported quantization bits");
+          exit(1);
+      }
+    }
+    fclose(fp);
+    return true;
+  }
+
+  int num_channel() const { return num_channel_; }
+  int sample_rate() const { return sample_rate_; }
+  int bits_per_sample() const { return bits_per_sample_; }
+  int num_samples() const { return num_samples_; }
+
+  ~WavReader() {
+    delete[] data_;
+  }
+
+  const float* data() const { return data_; }
+
+ private:
+  int num_channel_;
+  int sample_rate_;
+  int bits_per_sample_;
+  int num_samples_;  // sample points per channel
+  float* data_;
+};
+
+class WavWriter {
+ public:
+  WavWriter(const float* data, int num_samples, int num_channel,
+            int sample_rate, int bits_per_sample)
+      : data_(data),
+        num_samples_(num_samples),
+        num_channel_(num_channel),
+        sample_rate_(sample_rate),
+        bits_per_sample_(bits_per_sample) {}
+
+  void Write(const std::string& filename) {
+    FILE* fp = fopen(filename.c_str(), "w");
+    // init char 'riff' 'WAVE' 'fmt ' 'data'
+    WavHeader header;
+    char wav_header[44] = {0x52, 0x49, 0x46, 0x46, 0x00, 0x00, 0x00, 0x00, 0x57,
+                           0x41, 0x56, 0x45, 0x66, 0x6d, 0x74, 0x20, 0x10, 0x00,
+                           0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                           0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                           0x64, 0x61, 0x74, 0x61, 0x00, 0x00, 0x00, 0x00};
+    memcpy(&header, wav_header, sizeof(header));
+    header.channels = num_channel_;
+    header.bit = bits_per_sample_;
+    header.sample_rate = sample_rate_;
+    header.data_size = num_samples_ * num_channel_ * (bits_per_sample_ / 8);
+    header.size = sizeof(header) - 8 + header.data_size;
+    header.bytes_per_second =
+        sample_rate_ * num_channel_ * (bits_per_sample_ / 8);
+    header.block_size = num_channel_ * (bits_per_sample_ / 8);
+
+    fwrite(&header, 1, sizeof(header), fp);
+
+    for (int i = 0; i < num_samples_; ++i) {
+      for (int j = 0; j < num_channel_; ++j) {
+        switch (bits_per_sample_) {
+          case 8: {
+            char sample = static_cast<char>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+          case 16: {
+            int16_t sample = static_cast<int16_t>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+          case 32: {
+            int sample = static_cast<int>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+        }
+      }
+    }
+    fclose(fp);
+  }
+
+ private:
+  const float* data_;
+  int num_samples_;  // total float points in data_
+  int num_channel_;
+  int sample_rate_;
+  int bits_per_sample_;
+};
+
+}  // namespace wenet
+
+#endif  // FRONTEND_WAV_H_
diff --git a/runtime/cpp/README.md b/runtime/cpp/README.md
new file mode 100644
index 0000000..9cce823
--- /dev/null
+++ b/runtime/cpp/README.md
@@ -0,0 +1,50 @@
+# Stream example in C++
+
+Here's a simple example of the vad model in c++ onnxruntime.
+
+
+
+## Requirements
+
+Code are tested in the environments bellow, feel free to try others.
+
+- WSL2 + Debian-bullseye (docker)  
+- gcc 12.2.0
+- onnxruntime-linux-x64-1.12.1
+
+
+
+## Usage
+
+1. Install gcc 12.2.0, or just pull the docker image with `docker pull gcc:12.2.0-bullseye`
+
+2. Install onnxruntime-linux-x64-1.12.1
+
+   - Dowload lib onnxruntime: 
+
+     `wget https://github.com/microsoft/onnxruntime/releases/download/v1.12.1/onnxruntime-linux-x64-1.12.1.tgz`
+
+   - Unzip. Assume the path is `/root/onnxruntime-linux-x64-1.12.1`
+
+3. Modify wav path & Test configs in main function
+
+   `wav::WavReader wav_reader("${path_to_your_wav_file}");`
+
+   test sample rate, frame per ms, threshold...
+
+4. Build with gcc and run
+
+   ```bash
+   # Build
+   g++ silero-vad-onnx.cpp -I /root/onnxruntime-linux-x64-1.12.1/include/ -L /root/onnxruntime-linux-x64-1.12.1/lib/ -lonnxruntime  -Wl,-rpath,/root/onnxruntime-linux-x64-1.12.1/lib/ -o test
+   
+   # Run
+   ./test
+   ```
+
+   build: 
+
+   `g++ silero-vad-onnx.cpp -I /root/onnxruntime-linux-x64-1.12.1/include/ -L /root/onnxruntime-linux-x64-1.12.1/lib/ -lonnxruntime  -Wl,-rpath,/root/onnxruntime-linux-x64-1.12.1/lib/ -o test`
+
+   `./test`
+
diff --git a/runtime/cpp/silero-vad-onnx.cpp b/runtime/cpp/silero-vad-onnx.cpp
new file mode 100644
index 0000000..59846e1
--- /dev/null
+++ b/runtime/cpp/silero-vad-onnx.cpp
@@ -0,0 +1,253 @@
+#include <iostream>
+#include <vector>
+#include <sstream>
+#include <cstring>
+#include <chrono>
+
+#include "onnxruntime_cxx_api.h"
+#include "wav.h"
+
+class VadIterator
+{
+    // OnnxRuntime resources
+    Ort::Env env;
+    Ort::SessionOptions session_options;
+    std::shared_ptr<Ort::Session> session = nullptr;
+    Ort::AllocatorWithDefaultOptions allocator;
+    Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeCPU);
+
+public:
+    void init_engine_threads(int inter_threads, int intra_threads)
+    {   
+        // The method should be called in each thread/proc in multi-thread/proc work
+        session_options.SetIntraOpNumThreads(intra_threads);
+        session_options.SetInterOpNumThreads(inter_threads);
+        session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
+    }
+
+    void init_onnx_model(const std::string &model_path)
+    {   
+        // Init threads = 1 for 
+        init_engine_threads(1, 1);
+        // Load model
+        session = std::make_shared<Ort::Session>(env, model_path.c_str(), session_options);
+    }
+
+    void reset_states()
+    {
+        // Call reset before each audio start
+        std::memset(_h.data(), 0.0f, _h.size() * sizeof(float));
+        std::memset(_c.data(), 0.0f, _c.size() * sizeof(float));
+        triggerd = false;
+        temp_end = 0;
+        current_sample = 0;
+    }
+
+    // Call it in predict func. if you prefer raw bytes input.
+    void bytes_to_float_tensor(const char *pcm_bytes) 
+    {
+        std::memcpy(input.data(), pcm_bytes, window_size_samples * sizeof(int16_t));
+        for (int i = 0; i < window_size_samples; i++)
+        {
+            input[i] = static_cast<float>(input[i]) / 32768; // int16_t normalized to float
+        }
+    }
+
+
+    void predict(const std::vector<float> &data)
+    {
+        // bytes_to_float_tensor(data); 
+        
+        // Infer
+        // Create ort tensors
+        input.assign(data.begin(), data.end());
+        Ort::Value input_ort = Ort::Value::CreateTensor<float>(
+            memory_info, input.data(), input.size(), input_node_dims, 2);
+        Ort::Value sr_ort = Ort::Value::CreateTensor<int64_t>(
+            memory_info, sr.data(), sr.size(), sr_node_dims, 1);
+        Ort::Value h_ort = Ort::Value::CreateTensor<float>(
+            memory_info, _h.data(), _h.size(), hc_node_dims, 3);
+        Ort::Value c_ort = Ort::Value::CreateTensor<float>(
+            memory_info, _c.data(), _c.size(), hc_node_dims, 3);
+
+        // Clear and add inputs
+        ort_inputs.clear();
+        ort_inputs.emplace_back(std::move(input_ort));
+        ort_inputs.emplace_back(std::move(sr_ort));
+        ort_inputs.emplace_back(std::move(h_ort));
+        ort_inputs.emplace_back(std::move(c_ort));
+
+        // Infer
+        ort_outputs = session->Run(
+            Ort::RunOptions{nullptr},
+            input_node_names.data(), ort_inputs.data(), ort_inputs.size(),
+            output_node_names.data(), output_node_names.size());
+
+        // Output probability & update h,c recursively
+        float output = ort_outputs[0].GetTensorMutableData<float>()[0];
+        float *hn = ort_outputs[1].GetTensorMutableData<float>();
+        std::memcpy(_h.data(), hn, size_hc * sizeof(float));
+        float *cn = ort_outputs[2].GetTensorMutableData<float>();
+        std::memcpy(_c.data(), cn, size_hc * sizeof(float));
+
+        // Push forward sample index
+        current_sample += window_size_samples;
+        
+        // Reset temp_end when > threshold 
+        if ((output >= threshold) && (temp_end != 0))
+        {
+            temp_end = 0;
+        }
+        // 1) Silence
+        if ((output < threshold) && (triggerd == false))
+        {
+            // printf("{ silence: %.3f s }\n", 1.0 * current_sample / sample_rate);
+        }
+        // 2) Speaking 
+        if ((output >= (threshold - 0.15)) && (triggerd == true))
+        {
+            // printf("{ speaking_2: %.3f s }\n", 1.0 * current_sample / sample_rate);
+        }
+
+        // 3) Start
+        if ((output >= threshold) && (triggerd == false))
+        {
+            triggerd = true;
+            speech_start = current_sample - window_size_samples - speech_pad_samples; // minus window_size_samples to get precise start time point.
+            printf("{ start: %.3f s }\n", 1.0 * speech_start / sample_rate);
+        }
+
+        // 4) End 
+        if ((output < (threshold - 0.15)) && (triggerd == true))
+        {
+
+            if (temp_end != 0)
+            {
+                temp_end = current_sample;
+            }
+            // a. silence < min_slience_samples, continue speaking 
+            if ((current_sample - temp_end) < min_silence_samples)
+            {
+                // printf("{ speaking_4: %.3f s }\n", 1.0 * current_sample / sample_rate);
+                // printf("");
+            }
+            // b. silence >= min_slience_samples, end speaking
+            else
+            {
+                speech_end = current_sample + speech_pad_samples;
+                temp_end = 0;
+                triggerd = false;
+                printf("{ end: %.3f s }\n", 1.0 * speech_end / sample_rate);
+            }
+        }
+
+
+    }
+
+private:
+    // model config
+    int64_t window_size_samples;  // Assign when init, support 256 512 768 for 8k; 512 1024 1536 for 16k.
+    int sample_rate;
+    int sr_per_ms;  // Assign when init, support 8 or 16
+    float threshold;
+    int min_silence_samples; // sr_per_ms * #ms
+    int speech_pad_samples; // usually a 
+
+    // model states
+    bool triggerd = false;
+    unsigned int speech_start = 0; 
+    unsigned int speech_end = 0;
+    unsigned int temp_end = 0;
+    unsigned int current_sample = 0;    
+    // MAX 4294967295 samples / 8sample per ms / 1000 / 60 = 8947 minutes  
+    float output;
+
+    // Onnx model
+    // Inputs
+    std::vector<Ort::Value> ort_inputs;
+    
+    std::vector<const char *> input_node_names = {"input", "sr", "h", "c"};
+    std::vector<float> input;
+    std::vector<int64_t> sr;
+    unsigned int size_hc = 2 * 1 * 64; // It's FIXED.
+    std::vector<float> _h;
+    std::vector<float> _c;
+
+    int64_t input_node_dims[2] = {}; 
+    const int64_t sr_node_dims[1] = {1};
+    const int64_t hc_node_dims[3] = {2, 1, 64};
+
+    // Outputs
+    std::vector<Ort::Value> ort_outputs;
+    std::vector<const char *> output_node_names = {"output", "hn", "cn"};
+    
+
+public:
+    // Construction
+    VadIterator(const std::string ModelPath, 
+             int Sample_rate, int frame_size, 
+             float Threshold, int min_silence_duration_ms, int speech_pad_ms) 
+    {
+        init_onnx_model(ModelPath);
+        sample_rate = Sample_rate;
+        sr_per_ms = sample_rate / 1000;
+        threshold = Threshold;
+        min_silence_samples = sr_per_ms * min_silence_duration_ms;
+        speech_pad_samples = sr_per_ms * speech_pad_ms;
+        window_size_samples = frame_size * sr_per_ms;
+        
+        input.resize(window_size_samples);
+        input_node_dims[0] = 1;
+        input_node_dims[1] = window_size_samples;
+        // std::cout << "== Input size" << input.size() << std::endl;
+        _h.resize(size_hc);
+        _c.resize(size_hc);
+        sr.resize(1);
+    }
+
+};
+
+int main()
+{
+
+    // Read wav
+    wav::WavReader wav_reader("./test_for_vad.wav");
+    std::vector<int16_t> data(wav_reader.num_samples());
+    std::vector<float> input_wav(wav_reader.num_samples());
+
+    for (int i = 0; i < wav_reader.num_samples(); i++)
+    {
+        data[i] = static_cast<int16_t>(*(wav_reader.data() + i));
+    }
+
+    for (int i = 0; i < wav_reader.num_samples(); i++)
+    {
+        input_wav[i] = static_cast<float>(data[i]) / 32768;
+    }
+
+    // ===== Test configs =====
+    std::string path = "../files/silero_vad.onnx";
+    int test_sr = 8000;
+    int test_frame_ms = 64;
+    float test_threshold = 0.5f;
+    int test_min_silence_duration_ms = 0;
+    int test_speech_pad_ms = 0;
+    int test_window_samples = test_frame_ms * (test_sr/1000);
+
+    VadIterator vad(
+        path, test_sr, test_frame_ms, test_threshold,
+        test_min_silence_duration_ms, test_speech_pad_ms);
+
+    for (int j = 0; j < wav_reader.num_samples(); j += test_window_samples)
+    {
+        // std::cout << "== 4" << std::endl;
+        std::vector<float> r{&input_wav[0] + j, &input_wav[0] + j + test_window_samples};
+        auto start = std::chrono::high_resolution_clock::now();
+        // Predict and print throughout process time
+        vad.predict(r);
+        auto end = std::chrono::high_resolution_clock::now();
+        auto elapsed_time = std::chrono::duration_cast<std::chrono::nanoseconds>(end-start);
+        // std::cout << "== Elapsed time: " << 1.0*elapsed_time.count()/1000000 << "ms" << " ==" <<std::endl;
+
+    }
+}
diff --git a/runtime/cpp/wav.h b/runtime/cpp/wav.h
new file mode 100644
index 0000000..097f1c2
--- /dev/null
+++ b/runtime/cpp/wav.h
@@ -0,0 +1,205 @@
+// Copyright (c) 2016 Personal (Binbin Zhang)
+//
+// 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.
+
+
+#ifndef FRONTEND_WAV_H_
+#define FRONTEND_WAV_H_
+
+#include <assert.h>
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include <string>
+
+// #include "utils/log.h"
+
+namespace wav {
+
+struct WavHeader {
+  char riff[4];  // "riff"
+  unsigned int size;
+  char wav[4];  // "WAVE"
+  char fmt[4];  // "fmt "
+  unsigned int fmt_size;
+  uint16_t format;
+  uint16_t channels;
+  unsigned int sample_rate;
+  unsigned int bytes_per_second;
+  uint16_t block_size;
+  uint16_t bit;
+  char data[4];  // "data"
+  unsigned int data_size;
+};
+
+class WavReader {
+ public:
+  WavReader() : data_(nullptr) {}
+  explicit WavReader(const std::string& filename) { Open(filename); }
+
+  bool Open(const std::string& filename) {
+    FILE* fp = fopen(filename.c_str(), "rb"); //文件读取
+    if (NULL == fp) {
+      std::cout << "Error in read " << filename;
+      return false;
+    }
+
+    WavHeader header;
+    fread(&header, 1, sizeof(header), fp);
+    if (header.fmt_size < 16) {
+      fprintf(stderr,
+              "WaveData: expect PCM format data "
+              "to have fmt chunk of at least size 16.\n");
+      return false;
+    } else if (header.fmt_size > 16) {
+      int offset = 44 - 8 + header.fmt_size - 16;
+      fseek(fp, offset, SEEK_SET);
+      fread(header.data, 8, sizeof(char), fp);
+    }
+    // check "riff" "WAVE" "fmt " "data"
+
+    // Skip any sub-chunks between "fmt" and "data".  Usually there will
+    // be a single "fact" sub chunk, but on Windows there can also be a
+    // "list" sub chunk.
+    while (0 != strncmp(header.data, "data", 4)) {
+      // We will just ignore the data in these chunks.
+      fseek(fp, header.data_size, SEEK_CUR);
+      // read next sub chunk
+      fread(header.data, 8, sizeof(char), fp);
+    }
+
+    num_channel_ = header.channels;
+    sample_rate_ = header.sample_rate;
+    bits_per_sample_ = header.bit;
+    int num_data = header.data_size / (bits_per_sample_ / 8);
+    data_ = new float[num_data]; // Create 1-dim array
+    num_samples_ = num_data / num_channel_;
+
+    for (int i = 0; i < num_data; ++i) {
+      switch (bits_per_sample_) {
+        case 8: {
+          char sample;
+          fread(&sample, 1, sizeof(char), fp);
+          data_[i] = static_cast<float>(sample);
+          break;
+        }
+        case 16: {
+          int16_t sample;
+          fread(&sample, 1, sizeof(int16_t), fp);
+          // std::cout << sample;
+          data_[i] = static_cast<float>(sample);
+          // std::cout << data_[i];
+          break;
+        }
+        case 32: {
+          int sample;
+          fread(&sample, 1, sizeof(int), fp);
+          data_[i] = static_cast<float>(sample);
+          break;
+        }
+        default:
+          fprintf(stderr, "unsupported quantization bits");
+          exit(1);
+      }
+    }
+    fclose(fp);
+    return true;
+  }
+
+  int num_channel() const { return num_channel_; }
+  int sample_rate() const { return sample_rate_; }
+  int bits_per_sample() const { return bits_per_sample_; }
+  int num_samples() const { return num_samples_; }
+
+  ~WavReader() {
+    delete[] data_;
+  }
+
+  const float* data() const { return data_; }
+
+ private:
+  int num_channel_;
+  int sample_rate_;
+  int bits_per_sample_;
+  int num_samples_;  // sample points per channel
+  float* data_;
+};
+
+class WavWriter {
+ public:
+  WavWriter(const float* data, int num_samples, int num_channel,
+            int sample_rate, int bits_per_sample)
+      : data_(data),
+        num_samples_(num_samples),
+        num_channel_(num_channel),
+        sample_rate_(sample_rate),
+        bits_per_sample_(bits_per_sample) {}
+
+  void Write(const std::string& filename) {
+    FILE* fp = fopen(filename.c_str(), "w");
+    // init char 'riff' 'WAVE' 'fmt ' 'data'
+    WavHeader header;
+    char wav_header[44] = {0x52, 0x49, 0x46, 0x46, 0x00, 0x00, 0x00, 0x00, 0x57,
+                           0x41, 0x56, 0x45, 0x66, 0x6d, 0x74, 0x20, 0x10, 0x00,
+                           0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                           0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                           0x64, 0x61, 0x74, 0x61, 0x00, 0x00, 0x00, 0x00};
+    memcpy(&header, wav_header, sizeof(header));
+    header.channels = num_channel_;
+    header.bit = bits_per_sample_;
+    header.sample_rate = sample_rate_;
+    header.data_size = num_samples_ * num_channel_ * (bits_per_sample_ / 8);
+    header.size = sizeof(header) - 8 + header.data_size;
+    header.bytes_per_second =
+        sample_rate_ * num_channel_ * (bits_per_sample_ / 8);
+    header.block_size = num_channel_ * (bits_per_sample_ / 8);
+
+    fwrite(&header, 1, sizeof(header), fp);
+
+    for (int i = 0; i < num_samples_; ++i) {
+      for (int j = 0; j < num_channel_; ++j) {
+        switch (bits_per_sample_) {
+          case 8: {
+            char sample = static_cast<char>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+          case 16: {
+            int16_t sample = static_cast<int16_t>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+          case 32: {
+            int sample = static_cast<int>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+        }
+      }
+    }
+    fclose(fp);
+  }
+
+ private:
+  const float* data_;
+  int num_samples_;  // total float points in data_
+  int num_channel_;
+  int sample_rate_;
+  int bits_per_sample_;
+};
+
+}  // namespace wenet
+
+#endif  // FRONTEND_WAV_H_