帮我写一个pytorch实现音频分类的深度学习模型吧

好的，下面是一个使用 PyTorch 实现音频分类的深度学习模型的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class AudioClassificationModel(nn.Module):
    def __init__(self, num_classes):
        super(AudioClassificationModel, self).__init__()
        
        self.conv1 = nn.Conv1d(1, 32, kernel_size=3, stride=1, padding=1)
        self.batch_norm1 = nn.BatchNorm1d(32)
        self.pool1 = nn.MaxPool1d(2, stride=2)
        
        self.conv2 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
        self.batch_norm2 = nn.BatchNorm1d(64)
        self.pool2 = nn.MaxPool1d(2, stride=2)
        
        self.fc1 = nn.Linear(64 * 63, 128)
        self.batch_norm3 = nn.BatchNorm1d(128)
        
        self.fc2 = nn.Linear(128, num_classes)
        
    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.batch_norm1(x)
        x = self.pool1(x)
        
        x = self.conv2(x)
        x = F.relu(x)
        x = self.batch_norm2(x)
        x = self.pool2(x)
        
        x = x.view(-1, 64 * 63)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.batch_norm3(x)
        
        x = self.fc2(x)
        return x

这个模型使用了卷积层（Conv1d）、批量归一化层（BatchNorm1d）、最大池化层（MaxPool1d）和全连接层（Linear），并使用 ReLU 激活函数。这仅仅是一个简单的示例，您可以根据需要对其进行修改。