帮我用pytorch写一个神经网络，用于获取音频文件的特征

好的，我可以回答这个问题。以下是一个简单的用 PyTorch 实现的神经网络，用于获取音频文件的特征：

import torch
import torch.nn as nn

class AudioFeatureExtractor(nn.Module):
    def __init__(self):
        super(AudioFeatureExtractor, self).__init__()
        self.conv1 = nn.Conv1d(1, 32, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2, padding=)
        self.conv2 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2, padding=)
        self.conv3 = nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool3 = nn.MaxPool1d(kernel_size=2, stride=2, padding=)
        self.fc1 = nn.Linear(128 * 11, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 128)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = nn.functional.relu(x)
        x = self.pool3(x)
        x = x.view(-1, 128 * 11)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        x = nn.functional.relu(x)
        x = self.fc3(x)
        return x

这个神经网络包含三个卷积层和三个全连接层，用于从音频文件中提取特征。你可以将音频文件转换为 PyTorch 张量，并将其输入到这个神经网络中，以获取特征。