怎么画声调主体分布图

声调主体分布图是指在一段语音中，各个音节所对应的声调轮廓图。下面是画声调主体分布图的步骤：

1. 首先需要有一段语音的波形和对应的文本标注，可以使用语音分析软件进行录制和标注。

2. 利用语音分析软件对波形进行预处理，将语音信号切分成单个音节，并提取出每个音节的基频和能量等信息。

3. 根据标注信息，将每个音节的基频和能量信息与其对应的文本标注进行匹配，确定每个音节所对应的声调类型。

4. 将各个音节的基频和能量等信息绘制在声调轮廓图中，可以使用MATLAB等绘图软件进行绘制。横轴代表时间，纵轴代表基频或能量等信息，不同颜色代表不同声调类型。

5. 根据声调轮廓图，可以进一步分析语音中的声调特征，如声调变化趋势、高低声调分布等，并进行声调矫正和优化。

相关问题

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。

textcnn声调识别模型pytorch代码

以下是使用 PyTorch 实现的 TextCNN 声调识别模型的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TextCNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, num_classes, num_filters):
        super(TextCNN, self).__init__()
        
        # Embedding layer
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        
        # Convolutional layers
        self.conv1 = nn.Conv2d(1, num_filters, (2, embedding_dim))
        self.conv2 = nn.Conv2d(1, num_filters, (3, embedding_dim))
        self.conv3 = nn.Conv2d(1, num_filters, (4, embedding_dim))
        
        # Pooling layer
        self.pool = nn.MaxPool2d((3, 1))
        
        # Fully connected layer
        self.fc = nn.Linear(num_filters * 3, num_classes)
        
    def forward(self, x):
        x = self.embedding(x)
        x = x.unsqueeze(1) # add channel dimension for conv2d input
        
        # Convolutional layers with ReLU activation and max pooling
        conv1_out = F.relu(self.conv1(x)).squeeze(3)
        conv1_out = self.pool(conv1_out).squeeze(2)
        
        conv2_out = F.relu(self.conv2(x)).squeeze(3)
        conv2_out = self.pool(conv2_out).squeeze(2)
        
        conv3_out = F.relu(self.conv3(x)).squeeze(3)
        conv3_out = self.pool(conv3_out).squeeze(2)
        
        # Concatenate the output of the convolutional layers
        x = torch.cat((conv1_out, conv2_out, conv3_out), 1)
        
        # Fully connected layer with softmax activation
        x = self.fc(x)
        x = F.softmax(x, dim=1)
        return x

在上面的代码中，`TextCNN` 类继承了 `nn.Module` 类，其中实现了一个带有嵌入层、卷积层、池化层和全连接层的 TextCNN 声调识别模型。在 `__init__` 方法中，我们定义了各个层的结构和参数，而在 `forward` 方法中，我们对输入进行了嵌入处理、卷积、池化和全连接处理，最终输出一个概率分布向量。

在使用该模型时，我们需要指定以下参数：

- `vocab_size`：词汇表大小。
- `embedding_dim`：嵌入维度。
- `num_classes`：类别数。
- `num_filters`：卷积核数量。

例如，以下是使用该模型进行声调分类的示例代码：

# Define model hyperparameters
vocab_size = len(word2id)
embedding_dim = 128
num_classes = 4
num_filters = 64

# Create TextCNN model
model = TextCNN(vocab_size, embedding_dim, num_classes, num_filters)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Train the model
for epoch in range(num_epochs):
    for i, batch in enumerate(train_loader):
        inputs, labels = batch
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        if (i+1) % 1000 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# Test the model
with torch.no_grad():
    correct = 0
    total = 0
    for batch in test_loader:
        inputs, labels = batch
        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))

在上面的代码中，我们首先定义了模型的超参数，并创建了一个 `TextCNN` 对象。然后，我们定义了损失函数和优化器，使用训练集训练模型，并在测试集上评估模型的性能。