pytorch 中的1dcnn音频预处理代码

我可以回答这个问题。以下是一个使用 PyTorch 实现的 1D CNN 音频预处理代码的示例：

import torch
import torchaudio
import torch.nn as nn

class AudioCNN(nn.Module):
    def __init__(self):
        super(AudioCNN, self).__init__()
        self.conv1 = nn.Conv1d(1, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(128 * 2205, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv3(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = x.view(-1, 128 * 2205)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

这个模型使用了三个卷积层和两个全连接层，可以用于对音频进行分类。