Transformer 处理测谎数据并进行多模态分析是一个复杂而有趣的任务。下面提供一个简化的 PyTorch 实现框架，用于处理文本、音频等多模态信息以完成测谎检测。 ```python import torch from torch import nn class MultiModalTransformer(nn.Module): def __init__(self, text_dim=768, audio_dim=128, hidden_size=128, num_heads=4, num_layers=2, dropout_rate=0.1): super(MultiModalTransformer, self).__init__() # 文本特征编码器 (假设输入为BERT提取的embedding) self.text_encoder = nn.TransformerEncoder( encoder_layer=nn.TransformerEncoderLayer(d_model=text_dim, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout_rate), num_layers=num_layers ) # 音频特征编码器 (假设输入为MFCC或其他音频特征向量) self.audio_encoder = nn.TransformerEncoder( encoder_layer=nn.TransformerEncoderLayer(d_model=audio_dim, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout_rate), num_layers=num_layers ) # 融合层：将两个模态的信息融合到一起 self.fusion_layer = nn.Linear(text_dim + audio_dim, hidden_size) # 分类头 self.classifier = nn.Sequential( nn.Linear(hidden_size, hidden_size // 2), nn.ReLU(), nn.Dropout(dropout_rate), nn.Linear(hidden_size // 2, 2) # 输出维度表示真/假两类 ) def forward(self, text_inputs, audio_inputs): """ 输入： - text_inputs: shape [batch_size, seq_len_text, text_dim] - audio_inputs: shape [batch_size, seq_len_audio, audio_dim] 返回值： - logits: 测谎分类的概率分布 [batch_size, 2] """ encoded_text = self.text_encoder(text_inputs.permute(1, 0, 2)).mean(dim=0) # 平均池化操作 encoded_audio = self.audio_encoder(audio_inputs.permute(1, 0, 2)).mean(dim=0) combined_feature = torch.cat((encoded_text, encoded_audio), dim=-1) # 模态拼接 fused_representation = self.fusion_layer(combined_feature) # 特征融合 output_logits = self.classifier(fused_representation) # 最终分类 return output_logits if __name__ == "__main__": model = MultiModalTransformer() batch_size = 3 seq_len_text = 50 seq_len_audio = 100 dummy_text_data = torch.rand(batch_size, seq_len_text, 768) dummy_audio_data = torch.rand(batch_size, seq_len_audio, 128) outputs = model(dummy_text_data, dummy_audio_data) print("模型预测结果:", outputs.shape) # 打印输出张量形状 -> 应该是[batch_size, 2] ``` 这是基于 Transformer 架构的一个简化版多模态测谎模型实现代码，实际应用中还需要考虑更多细节例如数据预处理部分以及更精细的超参调整等等。