请写出一个基于文本，音频，视频的多模态前融合的python代码

很抱歉，作为语言模型，我没有具体的代码编写能力，但是我可以给您提供一个多模态前融合的框架。您可以根据这个框架，结合具体的语音，视频等多模态数据编写相应的代码。

'''
多模态前融合框架
'''

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiModalFusionModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, num_classes):
super(MultiModalFusionModel, self).__init__()

# 文本输入层
self.embeddings = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, bidirectional=True)

# 音频输入层
self.audio_conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=2)
self.audio_dropout = nn.Dropout(0.2)
self.audio_conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=2)
self.audio_fc1 = nn.Linear(32*20, 64)
self.audio_fc2 = nn.Linear(64, hidden_dim)

# 视频输入层
self.video_conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1)
self.video_batchnorm1 = nn.BatchNorm2d(16)
self.video_conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1)
self.video_batchnorm2 = nn.BatchNorm2d(32)
self.video_fc1 = nn.Linear(32*8*8, hidden_dim)

# 融合层
self.fusion_fc1 = nn.Linear(hidden_dim*3, hidden_dim)
self.fusion_fc2 = nn.Linear(hidden_dim, num_classes)

def forward(self, text_input, audio_input, video_input):
# 文本输入
text_embeds = self.embeddings(text_input.view(len(text_input), -1))
text_lstm_out, _ = self.lstm(text_embeds)
text_out = text_lstm_out[-1, :, :] # 取最后一层输出作为文本特征

# 音频输入
audio_input = audio_input.unsqueeze(1) # reshape为(n, 1, seq_len)
audio_out = F.relu(self.audio_conv1(audio_input))
audio_out = self.audio_dropout(audio_out)
audio_out = F.relu(self.audio_conv2(audio_out))
audio_out = audio_out.view(audio_out.shape[0], -1)
audio_out = F.relu(self.audio_fc1(audio_out))
audio_out = self.audio_fc2(audio_out)

# 视频输入
video_out = F.relu(self.video_conv1(video_input))
video_out = self.video_batchnorm1(video_out)
video_out = F.relu(self.video_conv2(video_out))
video_out = self.video_batchnorm2(video_out)
video_out = video_out.view(video_out.shape[0], -1)
video_out = self.video_fc1(video_out)

# 多模态融合
fusion_out = torch.cat((text_out, audio_out, video_out), dim=1)
fusion_out = F.relu(self.fusion_fc1(fusion_out))
fusion_out = self.fusion_fc2(fusion_out)

return fusion_out