写一个语音降噪和语音识别联合训练的pytorch代码

好的，以下是一个简单的语音降噪和语音识别联合训练的PyTorch代码示例：

import torch
import torch.nn as nn
import torchaudio

class DenoiseASR(nn.Module):
    def __init__(self):
        super(DenoiseASR, self).__init__()
        self.denoise_net = nn.Sequential(
            nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=256, out_channels=128, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=64, out_channels=32, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=32, out_channels=1, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.Tanh()
        )
        self.asr_net = nn.Sequential(
            nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=512, out_channels=1024, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv1d(in_channels=1024, out_channels=2048, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=2048, out_channels=1024, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=1024, out_channels=512, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=512, out_channels=256, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=256, out_channels=128, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=64, out_channels=32, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose1d(in_channels=32, out_channels=1, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.Tanh()
        )
        self.fc = nn.Linear(2048, 10)

    def forward(self, x):
        # Denoise network
        x_denoise = self.denoise_net(x)
        # ASR network
        x_asr = self.asr_net(x)
        x_asr = x_asr.view(x_asr.size(0), -1)
        x_asr = self.fc(x_asr)
        return x_denoise, x_asr

这个模型包括两个部分，一个是语音降噪的网络，一个是语音识别的网络。两个网络都是卷积神经网络，其中语音降噪网络和语音识别网络的结构是相同的，只是最后输出层的大小不同。模型的输入是一个声音信号，输出是降噪后的声音信号和语音识别结果。模型的训练可以使用声音信号和语音识别标签的数据进行联合训练，以提高模型的性能。