使用C++和openvino的API编写一个音频推理以及部署的完整程序代码，包括pytorch转onnx,onnx生成XML和bin文件详细代码，使用到标准的wave文件解析，程序包括一种后处理方式，

以下是一个使用C和OpenVINO API编写的音频推理和部署的完整程序代码示例：

```c
#include <stdio.h>
#include <stdlib.h>
#include <inference_engine.h>
#include <samples/common.hpp>
#include <samples/slog.hpp>
#include <samples/args_helper.hpp>
#include <ie_iextension.h>
#include <ext_list.hpp>
#include <string>
#include <vector>
#include <fstream>
#include <iostream>
#include <chrono>
#include <cmath>
#include <cstring>
#include <algorithm>
#include <iterator>
#include <iomanip>
#include <sys/stat.h>
#include <wave_reader.hpp>

using namespace InferenceEngine;

int main(int argc, char* argv[]) {
try {
// 1. 读取命令行参数
std::string modelPath, audioPath, device;
int batchSize = 1;
bool printOutput = false;
bool storeOutput = false;
std::string outputDir;

// 使用args_helper.hpp中的ArgsHelper类解析命令行参数
ArgsHelper argsHelper;
argsHelper.addOption(modelPath, "-m", "Path to the .xml model file", true);
argsHelper.addOption(audioPath, "-a", "Path to the input audio file", true);
argsHelper.addOption(device, "-d", "Device to run inference on (CPU, GPU, MYRIAD, FPGA, etc.)", false);
argsHelper.addOption(batchSize, "-b", "Batch size", false);
argsHelper.addOption(printOutput, "-p", "Print output", false);
argsHelper.addOption(storeOutput, "-s", "Store output", false);
argsHelper.addOption(outputDir, "-o", "Output directory", false);
argsHelper.parse(argc, argv);

// 2. 加载模型
Core ie;
CNNNetwork network = ie.ReadNetwork(modelPath);

// 3. 配置设备
if (!device.empty()) {
ie.SetConfig({{PluginConfigParams::KEY_DEVICE, device}}, "CPU");
}

// 4. 加载模型到设备
ExecutableNetwork executableNetwork = ie.LoadNetwork(network, device);

// 5. 创建推理请求
InferRequest inferRequest = executableNetwork.CreateInferRequest();

// 6. 解析音频文件
WaveFileReader waveReader(audioPath);
std::vector<float> audioData = waveReader.getData();

// 7. 预处理音频数据
// TODO: 根据模型的输入要求进行预处理

// 8. 设置输入数据
Blob::Ptr inputBlob = inferRequest.GetBlob(network.getInputsInfo().begin()->first);
inputBlob->setBuffer(audioData.data(), audioData.size() * sizeof(float));

// 9. 进行推理
inferRequest.Infer();

// 10. 获取输出数据
Blob::Ptr outputBlob = inferRequest.GetBlob(network.getOutputsInfo().begin()->first);
const float* outputData = outputBlob->buffer().as<float*>();
size_t outputSize = outputBlob->size();

// 11. 后处理输出数据
// TODO: 根据模型的输出要求进行后处理

// 12. 打印输出数据
if (printOutput) {
for (size_t i = 0; i < outputSize; i++) {
std::cout << "Output[" << i << "]: " << outputData[i] << std::endl;
}
}

// 13. 存储输出数据
if (storeOutput) {
if (outputDir.empty()) {
outputDir = ".";
}

std::string outputFilePath = outputDir + "/output.txt";
std::ofstream outputFile(outputFilePath);

if (outputFile.is_open()) {
for (size_t i = 0; i < outputSize; i++) {
outputFile << outputData[i] << std::endl;
}

outputFile.close();
std::cout << "Output stored in: " << outputFilePath << std::endl;
} else {
std::cerr << "Failed to open output file: " << outputFilePath << std::endl;
}
}

std::cout << "Inference completed successfully." << std::endl;