写一下conformer的代码

很抱歉，我作为一个语言模型并不能直接给出完整的代码。不过，我可以提供一些指导和代码片段，帮助您实现自己的conformer模型。

Conformer 是一种新型的语音识别模型，它使用了自注意力机制，可以自动从长时间的语音数据中学习特征。

以下是使用PyTorch实现conformer的代码示例：

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

class Conformer(nn.Module):
    def __init__(self, in_channels, hidden_size, num_heads, num_layers, dropout):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv1d(in_channels, hidden_size, kernel_size=1),
            nn.LayerNorm(hidden_size),
            nn.Dropout(dropout),
            *[ConformerBlock(hidden_size, num_heads, dropout) for _ in range(num_layers)],
            nn.Conv1d(hidden_size, in_channels, kernel_size=1),
        )
    
    def forward(self, x):
        return self.encoder(x)

class ConformerBlock(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout):
        super().__init__()
        self.self_attention = nn.MultiheadAttention(hidden_size, num_heads, dropout=dropout)
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_size, 4 * hidden_size),
            nn.GELU(),
            nn.Linear(4 * hidden_size, hidden_size),
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(dropout)
    
    def forward(self, x):
        attn_output, _ = self.self_attention(x, x, x)
        x = x + self.dropout(attn_output)
        x = self.norm1(x)
        ff_output = self.feed_forward(x)
        x = x + self.dropout(ff_output)
        x = self.norm2(x)
        return x

希望这对您