基于pytorch深度学习框架开发多模态情感分析 语音模态与文本模态特征注意力融合

多模态情感分析 语音模态与文本模态特征注意力融合 基于pytorch深度学习框架开发

文本编码器采用预训练的多语言bert模型，beause数据集中包含中英两个语言的文本，需要基于该模型做微调

语音特征提取器采用预训练的wav2vec2模型

项目所用的数据集为EATD_Corpus数据集，包含三类样本，分别为negative、neutral、positive

1

实现一个多模态情感分析项目，结合语音和文本模态，并使用注意力机制融合特征，可以分为以下几个步骤：

1. 环境搭建
2. 数据预处理
3. 模型构建
4. 训练与评估
5. 部署

1. 环境搭建

确保安装了以下依赖：

• PyTorch
• Transformers (用于BERT和Wav2Vec2)
• Gradio (用于前端展示)

pip install torch torchvision torchaudio transformers gradio

2. 数据预处理

假设你已经有了EATD_Corpus数据集，包含音频文件和对应的文本。

import os
import pandas as pd

# 加载数据集
def load_dataset(data_dir):
    audio_files = []
    texts = []
    labels = []

    for label in ['negative', 'neutral', 'positive']:
        label_dir = os.path.join(data_dir, label)
        for file in os.listdir(label_dir):
            if file.endswith('.wav'):
                audio_files.append(os.path.join(label_dir, file))
                with open(os.path.join(label_dir, file.replace('.wav', '.txt')), 'r') as f:
                    texts.append(f.read().strip())
                labels.append(label)

    return pd.DataFrame({'audio': audio_files, 'text': texts, 'label': labels})

data_dir = 'path/to/your/dataset'
df = load_dataset(data_dir)

3. 模型构建

文本编码器（BERT）

from transformers import BertTokenizer, BertModel

class TextEncoder(nn.Module):
    def __init__(self, bert_model_name='bert-base-multilingual-cased'):
        super(TextEncoder, self).__init__()
        self.tokenizer = BertTokenizer.from_pretrained(bert_model_name)
        self.model = BertModel.from_pretrained(bert_model_name)

    def forward(self, text):
        inputs = self.tokenizer(text, return_tensors='pt', padding=True, truncation=True)
        outputs = self.model(**inputs)
        return outputs.last_hidden_state.mean(dim=1)

语音特征提取器（Wav2Vec2）

from transformers import Wav2Vec2Processor, Wav2Vec2Model

class AudioEncoder(nn.Module):
    def __init__(self, wav2vec2_model_name='facebook/wav2vec2-base-960h'):
        super(AudioEncoder, self).__init__()
        self.processor = Wav2Vec2Processor.from_pretrained(wav2vec2_model_name)
        self.model = Wav2Vec2Model.from_pretrained(wav2vec2_model_name)

    def forward(self, audio_file):
        inputs = self.processor(audio_file, return_tensors='pt')
        outputs = self.model(**inputs)
        return outputs.last_hidden_state.mean(dim=1)

多模态融合模型

import torch.nn as nn

class MultimodalEmotionClassifier(nn.Module):
    def __init__(self, text_encoder, audio_encoder):
        super(MultimodalEmotionClassifier, self).__init__()
        self.text_encoder = text_encoder
        self.audio_encoder = audio_encoder
        self.attention = nn.MultiheadAttention(768, num_heads=8)
        self.fc = nn.Linear(768 * 2, 3)

    def forward(self, text, audio):
        text_features = self.text_encoder(text)
        audio_features = self.audio_encoder(audio)
        
        # Attention mechanism
        text_features = text_features.unsqueeze(1)
        audio_features = audio_features.unsqueeze(1)
        fused_features, _ = self.attention(text_features, audio_features, audio_features)
        fused_features = fused_features.squeeze(1)
        
        output = self.fc(torch.cat((text_features, fused_features), dim=1))
        return output

4. 训练与评估

import torch
from torch.utils.data import Dataset, DataLoader

class EATDDataset(Dataset):
    def __init__(self, df, text_encoder, audio_encoder):
        self.df = df
        self.text_encoder = text_encoder
        self.audio_encoder = audio_encoder

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        text = row['text']
        audio_file = row['audio']
        label = row['label']

        text_feature = self.text_encoder(text)
        audio_feature = self.audio_encoder(audio_file)

        return text_feature, audio_feature, label

# 数据加载
dataset = EATDDataset(df, TextEncoder(), AudioEncoder())
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# 模型实例化
model = MultimodalEmotionClassifier(TextEncoder(), AudioEncoder())

# 损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

# 训练循环
num_epochs = 10
for epoch in range(num_epochs):
    for text_features, audio_features, labels in dataloader:
        optimizer.zero_grad()
        outputs = model(text_features, audio_features)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

5. 部署

使用Gradio进行前端展示。

import gradio as gr

def predict_emotion(text, audio_file):
    text_feature = text_encoder(text)
    audio_feature = audio_encoder(audio_file)
    output = model(text_feature, audio_feature)
    _, predicted = torch.max(output, 1)
    return predicted.item()

iface = gr.Interface(fn=predict_emotion, inputs=["text", "file"], outputs="label")
iface.launch()

总结

基本框架：基于PyTorch的多模态情感分析系统。同学可根据具体需求进一步优化和扩展功能，biru zhege添加更多的模型层、改进注意力机制等。