写python代码读取wav文件并利用pytorch实现双向lstm训练语音分类网络数据集在data路径下里面有每一个文件夹里有很多wav文件每个文件夹代表一类注意每个wav文件的长度不同需要做必要的预处理。训练完成之后需要利用recallaccuracyprecision评估性能

以下是一个读取wav文件并使用双向LSTM训练语音分类网络的示例代码：

import os
import torch
import torchaudio
import torchaudio.transforms as T
from torch.utils.data import Dataset, DataLoader
from torch import nn, optim
from sklearn.metrics import recall_score, accuracy_score, precision_score

class SpeechDataset(Dataset):
    def __init__(self, root_dir):
        self.root_dir = root_dir
        self.classes = os.listdir(root_dir)
        self.class_to_idx = {c: i for i, c in enumerate(self.classes)}
        self.files = self.get_files()

    def get_files(self):
        files = []
        for class_name in self.classes:
            class_dir = os.path.join(self.root_dir, class_name)
            for file_name in os.listdir(class_dir):
                file_path = os.path.join(class_dir, file_name)
                files.append((file_path, self.class_to_idx[class_name]))
        return files

    def __getitem__(self, index):
        file_path, label = self.files[index]
        waveform, _ = torchaudio.load(file_path)
        # 预处理：将每个音频文件的长度统一为固定长度，这里假设为16000
        waveform = T.PadTrim(16000)(waveform)
        return waveform, label

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

class SpeechModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(SpeechModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, bidirectional=True)
        self.fc = nn.Linear(hidden_size * 2, num_classes)

    def forward(self, x):
        x, _ = self.lstm(x)
        x = x[:, -1, :]
        x = self.fc(x)
        return x

# 设置参数
root_dir = 'data'
input_size = 1
hidden_size = 128
num_classes = len(os.listdir(root_dir))
batch_size = 32
num_epochs = 10

# 创建数据集和数据加载器
dataset = SpeechDataset(root_dir)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 创建模型和优化器
model = SpeechModel(input_size, hidden_size, num_classes)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
for epoch in range(num_epochs):
    model.train()
    for waveform, labels in dataloader:
        waveform = waveform.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(waveform.unsqueeze(2))
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# 评估模型
model.eval()
y_true = []
y_pred = []
with torch.no_grad():
    for waveform, labels in dataloader:
        waveform = waveform.to(device)
        labels = labels.to(device)
        outputs = model(waveform.unsqueeze(2))
        _, predicted = torch.max(outputs.data, 1)
        y_true.extend(labels.cpu().numpy())
        y_pred.extend(predicted.cpu().numpy())

# 计算评估指标
recall = recall_score(y_true, y_pred, average='macro')
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred, average='macro')

print('Recall:', recall)
print('Accuracy:', accuracy)
print('Precision:', precision)

请确保已经安装了所需的依赖库（torch、torchaudio、sklearn），并将wav文件存放在正确的路径下（'data'文件夹中的子文件夹代表不同的类别）。此代码示例中使用了torch和torchaudio库来处理音频数据，使用了双向LSTM作为模型结构，并使用了Adam优化器和交叉熵损失函数进行训练。最后使用sklearn库计算了模型的召回率、准确率和精确率评估指标