transformer声调识别完整代码

以下是使用transformer进行声调识别的完整代码示例：

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

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.d_model = d_model
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x * np.sqrt(self.d_model)
        seq_len = x.size(1)
        x = x + self.pe[:seq_len, :]
        return x

class TonalDataset(torch.utils.data.Dataset):
    def __init__(self, data, label):
        super(TonalDataset, self).__init__()
        self.data = data
        self.label = label

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

    def __getitem__(self, index):
        data = self.data[index]
        label = self.label[index]
        return data, label

class TonalModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, num_classes):
        super(TonalModel, self).__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.num_classes = num_classes
        self.pos_encoder = PositionalEncoding(input_dim)
        encoder_layers = nn.TransformerEncoderLayer(d_model=input_dim, nhead=4, dim_feedforward=hidden_dim, dropout=0.1)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers=num_layers)
        self.fc = nn.Linear(input_dim, num_classes)

    def forward(self, x):
        x = self.pos_encoder(x)
        x = self.transformer_encoder(x)
        x = x.mean(dim=1)
        x = self.fc(x)
        return x

def collate_fn(batch):
    data = [item[0] for item in batch]
    label = [item[1] for item in batch]
    data = nn.utils.rnn.pad_sequence(data, batch_first=True, padding_value=0)
    label = torch.tensor(label)
    return data, label

# 加载数据
train_data = np.load('train_data.npy')
train_label = np.load('train_label.npy')
val_data = np.load('val_data.npy')
val_label = np.load('val_label.npy')

train_dataset = TonalDataset(train_data, train_label)
val_dataset = TonalDataset(val_data, val_label)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=32, collate_fn=collate_fn, shuffle=True)
val_loader = torch.utils.data.DataLoader(dataset=val_dataset, batch_size=32, collate_fn=collate_fn, shuffle=False)

# 定义模型
model = TonalModel(40, 256, 3, 3)

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

# 训练模型
for epoch in range(100):
    model.train()
    for data, label in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()

    model.eval()
    val_loss = 0
    val_acc = 0
    with torch.no_grad():
        for data, label in val_loader:
            output = model(data)
            loss = criterion(output, label)
            val_loss += loss.item() * data.size(0)
            _, pred = torch.max(output, 1)
            val_acc += (pred == label).sum().item()

    val_loss /= len(val_dataset)
    val_acc /= len(val_dataset)

    print('Epoch [{}/{}], val_loss: {:.4f}, val_acc: {:.4f}'.format(epoch+1, 100, val_loss, val_acc))

在这个示例中，我们使用transformer模型对40维MFCC特征进行声调分类。我们先定义了一个PositionalEncoding类，用于添加位置编码，然后定义了一个TonalDataset类，用于加载数据。接着我们定义了一个TonalModel类，其中包含一个transformer编码器和一个线性分类器。在训练过程中，我们使用交叉熵损失函数和Adam优化器对模型进行优化。