用resnet50进行语音数据集分类的源码

下面是一个使用ResNet-50进行语音数据集分类的示例源代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import SpeechCommands

# 设置超参数
num_epochs = 10
batch_size = 32
learning_rate = 0.001

# 数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])

# 加载数据集
train_dataset = SpeechCommands(root='./data', subset='training', download=True, transform=transform)
test_dataset = SpeechCommands(root='./data', subset='testing', download=True, transform=transform)

# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义ResNet-50模型
class ResNet50(nn.Module):
    def __init__(self, num_classes):
        super(ResNet50, self).__init__()
        self.resnet = torch.hub.load('pytorch/vision:v0.6.0', 'resnet50', pretrained=True)
        self.fc = nn.Linear(1000, num_classes)

    def forward(self, x):
        x = self.resnet(x)
        x = self.fc(x)
        return x

# 创建模型实例
model = ResNet50(num_classes=len(train_dataset.classes))

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        # 前向传播
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 打印训练信息
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 在测试集上评估模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy: {} %'.format(100 * correct / total))

请注意，此代码假设您已经安装了相关的库，并且已经下载并准备好了SpeechCommands数据集。您可以根据需要进行修改和调整。希望这可以帮助到您！