在python中用pyTorch搭建CNN神经网络实现数字（0~9）语音识别
1.收集训练数据
speech_commands_v0.01.tar.gz
/data/speech_commands_v0.01.tar.gz
自己用迅雷下载什么都行（推荐迅雷）
2.准备环境
①pycharm软件
②cuda和cudnn（我的是11.3）
③python（我的是3.9）
④支持cuda的pytorch
对于环境的准备，老样子，自己csdn查教程一大把。
推荐不要用conda，直接全pip，一步到胃。
3.直接上代码
用pycharm创建CNN_project
(1)提取数据并保存
①先把所需数据集（里面包括0-9语音集）保存到dataset文件夹
②对数据集提取语谱图（spectrogram）并保存数据集和标签集为data.npy,label.npy
import osimport librosaimport numpy as npimport matplotlib.pyplot as pltimport cv2def get_spectrogram(path):data, fs = librosa.load(path, sr=None, mono=True)spect = librosa.stft(data, n_fft=1024, hop_length=320, win_length=1024)# print(spect.shape)# 画语谱图# plt.matshow(spect)# plt.ylabel('Frequency')# plt.xlabel('Time(s)')# plt.title('Spectrogram')# plt.show()return spectdef extract_features():data_path = "./dataset"labels = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']print("标签名：", labels)total_data = []total_label = []for label in labels:label_path = data_path + "\\" + labelwav_names = os.listdir(label_path)for wav_name in wav_names:if wav_name.endswith(".wav"):wav_path = label_path + "\\" + wav_nameprint(wav_path)spect = get_spectrogram(wav_path)spect = np.abs(spect)spect = cv2.resize(spect, (28, 28))total_data.append(spect)total_label.append(labels.index(label))total_data = np.array(total_data)total_label = np.array(total_label)print(total_data.shape)print(total_label.shape)np.save("data.npy", total_data)np.save("label.npy", total_label)if __name__ == '__main__':extract_features()
(2)创建模型
#Cnn.pyimport torchfrom torch import nnclass CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, # 输入为单层图像out_channels=16, # 卷积成16层kernel_size=5, # 卷积壳5x5stride=1, # 步长，每次移动1步padding=2, # 边缘层，给图像边缘增加像素值为0的框),nn.ReLU(), # 激活函数nn.MaxPool2d(kernel_size=2), # 池化层，将图像长宽减少一半)self.conv2 = nn.Sequential(nn.Conv2d(16, 32, 5, 1, 2),nn.ReLU(),nn.MaxPool2d(2),)self.out = nn.Linear(32 * 7 * 7, 10)def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = x.view(x.size(0), -1)output = self.out(x)return outputdef save_model(net, path):torch.save(net, path)def load_model(path):net = torch.load(path)return net
(3)训练模型并保存
import cv2from Cnn import CNN, save_model, load_modelimport torchimport torch.nn.functional as Ffrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_scorefrom sklearn.model_selection import train_test_splitimport torch.utils.data as Dataimport numpy as npfrom Process_data import get_spectrogramdef train_model(net, data, label, lr, batch_size, epoch):print(net)# 能用显卡跑的就用显卡跑# net = net.cuda()data = torch.Tensor(data)data = data.unsqueeze(1)label = torch.Tensor(label).long()# data =data.cuda()# label=label.cuda()# 训练集和测试集7：3train_data, test_data, train_label, test_label = train_test_split(data, label, test_size=0.3, random_state=0)# 学习率LR = lr# 每次投入训练数据大小BATCH_SIZE = batch_size# 训练模型次数EPOCH = epochoptimizer = torch.optim.Adam(net.parameters(), lr=LR)train_dataset = Data.TensorDataset(train_data, train_label)train_loader = Data.DataLoader(dataset=train_dataset,batch_size=BATCH_SIZE,shuffle=True,)test_dataset = Data.TensorDataset(test_data, test_label)test_loader = Data.DataLoader(dataset=test_dataset,batch_size=BATCH_SIZE,shuffle=True,)scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, LR, epochs=EPOCH, steps_per_epoch=len(train_loader))for epoch in range(EPOCH):for step, (batch_data, batch_label) in enumerate(train_loader):print('Epoch:', epoch + 1, '/', EPOCH, 'Step:', step)prediction = net(batch_data)loss = F.cross_entropy(prediction, batch_label)optimizer.zero_grad()loss.backward()optimizer.step()scheduler.step()if step % 50 == 0:accuracy = []for stp, (test_x, test_y) in enumerate(test_loader):test_output = net(test_x)_, pred_y = torch.max(test_output, 1)accuracy.append(torch.sum(pred_y == test_y).item() / len(test_y))print('Epoch', epoch + 1, '| train loss:%.4f' % loss, '| test accuracy:%.4f' % np.mean(accuracy))return netdef test_model(net, data, label):data = torch.Tensor(data)data = data.unsqueeze(1)label = torch.Tensor(label).long()# 训练集和测试集7：3train_data, test_data, train_label, test_label = train_test_split(data, label, test_size=0.3, random_state=0)test_dataset = Data.TensorDataset(test_data, test_label)test_loader = Data.DataLoader(dataset=test_dataset,batch_size=32,shuffle=True,)y_true = []y_pred = []for stp, (test_x, test_y) in enumerate(test_loader):test_output = net(test_x)_, pred_y = torch.max(test_output, 1)y_true.extend(test_y)y_pred.extend(pred_y)print("Accuracy:", accuracy_score(y_true, y_pred))print("Precision_score:", precision_score(y_true, y_pred, average='macro'))print("Recall_score:", recall_score(y_true, y_pred, average='macro'))print("F1_score", f1_score(y_true, y_pred, average='macro'))def predict(model, file):spect = get_spectrogram(file)spect = np.abs(spect)spect = cv2.resize(spect, (28, 28))data = torch.Tensor(spect)data = data.unsqueeze(0)data = data.unsqueeze(0)output = model(data)confidence, pred_y = torch.max(output, 1)print("识别结果为：",pred_y.numpy())if __name__ == '__main__':data = np.load("data.npy")label = np.load("label.npy")cnn = CNN()cnn = train_model(cnn, data, label, lr=0.03, batch_size=500, epoch=20)save_model(cnn, "cnn.pkl")test_model(cnn, data, label)file = "./dataset/zero/096456f9_nohash_0.wav"cnn = load_model("cnn.pkl")predict(cnn, file)
4.最终实现效果
识别率大概在0.9左右
