深度学习100例 | 第41天:语音识别 - PyTorch实现
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文章目录
🍖 我的环境:
- 语言环境:Python3.8
- 编译器:Jupyter Lab
- 深度学习环境:
- torch==1.10.0+cu113
- torchvision==0.11.1+cu113
- 创作平台:🔗 极链AI云
- 创作教程:🔎 操作手册
深度学习环境配置教程:小白入门深度学习 | 第四篇:配置PyTorch环境
👉 往期精彩内容
- 深度学习100例 | 第1例:猫狗识别 - PyTorch实现
- 深度学习100例 | 第2例:人脸表情识别 - PyTorch实现
- 深度学习100例 | 第3天:交通标志识别 - PyTorch实现
- 深度学习100例 | 第4例:水果识别 - PyTorch实现
- 🔥 选自专栏:《深度学习100例》Pytorch版
- ✨镜像专栏:《深度学习100例》TensorFlow版
我们的代码流程图如下所示:
一、导入数据
我将使用 torchaudio
来下载 SpeechCommands
数据集,它是由不同人录制的 35 个命令的语音数据集
。在这个数据集中,所有的音频文件都大约 1 秒长(大约 16000 个时间帧长)。
实际的加载和格式化步骤发生在访问数据点时,torchaudio
负责将音频文件转换为张量。如果想直接加载音频文件, 可以使用torchaudio.load()
。它返回一个元组,其中包含新创建的张量以及音频文件的采样频率(SpeechCommands 为 16kHz)。
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchaudio
import matplotlib.pyplot as plt
import IPython.display as ipd
from tqdm import tqdm
让我们检查一下 CUDA GPU 是否可用并选择我们的设备。在 GPU 上运行网络将大大减少训练/测试运行时间。
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
cuda
1. 下载数据
from torchaudio.datasets import SPEECHCOMMANDS
import os
class SubsetSC(SPEECHCOMMANDS):
def __init__(self, subset: str = None):
super().__init__("./", download=True)
def load_list(filename):
filepath = os.path.join(self._path, filename)
with open(filepath) as fileobj:
return [os.path.normpath(os.path.join(self._path, line.strip())) for line in fileobj]
if subset == "validation":
self._walker = load_list("validation_list.txt")
elif subset == "testing":
self._walker = load_list("testing_list.txt")
elif subset == "training":
excludes = load_list("validation_list.txt") + load_list("testing_list.txt")
excludes = set(excludes)
self._walker = [w for w in self._walker if w not in excludes]
# 划分训练集与测试集
train_set = SubsetSC("training")
test_set = SubsetSC("testing")
waveform, sample_rate, label, speaker_id, utterance_number = train_set[0]
2. 数据展示
SpeechCommands
数据集中的数据点是由波形(音频信号)、采样率、话语(标签)、说话者 ID、话语数组成的元组。
print("Shape of waveform: ".format(waveform.size()))
print("Sample rate of waveform: ".format(sample_rate))
plt.plot(waveform.t().numpy());
Shape of waveform: torch.Size([1, 16000])
Sample rate of waveform: 16000
labels = sorted(list(set(datapoint[2] for datapoint in train_set)))
print(labels)
['backward', 'bed', 'bird', 'cat', 'dog', 'down', 'eight', 'five', 'follow', 'forward', 'four', 'go', 'happy', 'house', 'learn', 'left', 'marvin', 'nine', 'no', 'off', 'on', 'one', 'right', 'seven', 'sheila', 'six', 'stop', 'three', 'tree', 'two', 'up', 'visual', 'wow', 'yes', 'zero']
35 个音频标签分别是用户说出的命令
waveform_first, *_ = train_set[0]
ipd.Audio(waveform_first.numpy(), rate=sample_rate)
waveform_second, *_ = train_set[1]
ipd.Audio(waveform_second.numpy(), rate=sample_rate)
waveform_last, *_ = train_set[-1]
ipd.Audio(waveform_last.numpy(), rate=sample_rate)
二、数据准备工作
1. 格式化数据
对于波形,我们对音频进行下采样以加快处理速度,而不会损失太多的分类能力。
new_sample_rate = 8000
transform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=new_sample_rate)
transformed = transform(waveform)
ipd.Audio(transformed.numpy(), rate=new_sample_rate)
我们使用标签列表中的索引对每个单词进行编码。
2. 标签的编码与还原
def label_to_index(word):
# Return the position of the word in labels
return torch.tensor(labels.index(word))
def index_to_label(index):
# Return the word corresponding to the index in labels
# This is the inverse of label_to_index
return labels[index]
word_start = "yes"
index = label_to_index(word_start)
word_recovered = index_to_label(index)
print(word_start, "-->", index, "-->", word_recovered)
yes --> tensor(33) --> yes
3. 构建数据加载器
def pad_sequence(batch):
# Make all tensor in a batch the same length by padding with zeros
batch = [item.t() for item in batch] # 将Tensor进行转置
# 用0填充张量至等长度,.pad_sequence()用法可参考:https://blog.csdn.net/qq_38251616/article/details/125222012
batch = torch.nn.utils.rnn.pad_sequence(batch, batch_first=True, padding_value=0.)
return batch.permute(0, 2, 1)
def collate_fn(batch):
# A data tuple has the form:
# waveform, sample_rate, label, speaker_id, utterance_number
tensors, targets = [], []
# Gather in lists, and encode labels as indices
for waveform, _, label, *_ in batch:
tensors += [waveform]
targets += [label_to_index(label)]
# Group the list of tensors into a batched tensor
tensors = pad_sequence(tensors)
targets = torch.stack(targets)
return tensors, targets
batch_size = 256
if device == "cuda":
num_workers = 1
pin_memory = True
else:
num_workers = 0
pin_memory = False
# 关于torch.utils.data.DataLoader()用法不清楚的同学,可以参考文章:
# https://blog.csdn.net/qq_38251616/article/details/125221503
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=num_workers,
pin_memory=pin_memory,
)
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=batch_size,
shuffle=False,
drop_last=False,
collate_fn=collate_fn,
num_workers=num_workers,
pin_memory=pin_memory,
)
for X, y in test_loader:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
Shape of X [N, C, H, W]: torch.Size([256, 1, 16000])
Shape of y: torch.Size([256]) torch.int64
三、构建模型
在本教程中,我们将使用卷积神经网络来处理原始音频数据。通常对音频数据应用更高级的转换,但 CNN 可用于准确处理原始数据。具体架构仿照本文描述的M5网络架构。模型处理原始音频数据的一个重要方面是其第一层过滤器的感受野。我们模型的第一个滤波器长度为 80,因此在处理以 8kHz 采样的音频时,感受野约为 10ms(在 4kHz 时约为 20ms)。这个大小类似于经常使用从 20 毫秒到 40 毫秒的感受野的语音处理应用程序。
class M5(nn.Module):
def __init__(self, n_input=1, n_output=35, stride=16, n_channel=32):
super().__init__()
self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=stride)
self.bn1 = nn.BatchNorm1d(n_channel)
self.pool1 = nn.MaxPool1d(4)
self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3)
self.bn2 = nn.BatchNorm1d(n_channel)
self.pool2 = nn.MaxPool1d(4)
self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3)
self.bn3 = nn.BatchNorm1d(2 * n_channel)
self.pool3 = nn.MaxPool1d(4)
self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3)
self.bn4 = nn.BatchNorm1d(2 * n_channel)
self.pool4 = nn.MaxPool1d(4)
self.fc1 = nn.Linear(2 * n_channel, n_output)
def forward(self, x):
x = self.conv1(x)
x = F.relu(self.bn1(x))
x = self.pool1(x)
x = self.conv2(x)
x = F.relu(self.bn2(x))
x = self.pool2(x)
x = self.conv3(x)
x = F.relu(self.bn3(x))
x = self.pool3(x)
x = self.conv4(x)
x = F.relu(self.bn4(x))
x = self.pool4(x)
x = F.avg_pool1d(x, x.shape[-1])
x = x.permute(0, 2, 1)
x = self.fc1(x)
return F.log_softmax(x, dim=2)
model = M5(n_input=transformed.shape[0], n_output=len(labels))
model.to(device)
print(model)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
n = count_parameters(model)
print("Number of parameters: %s" % n)
M5(
(conv1): Conv1d(1, 32, kernel_size=(80,), stride=(16,))
(bn1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(pool1): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv1d(32, 32, kernel_size=(3,), stride=(1,))
(bn2): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(pool2): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)
(conv3): Conv1d(32, 64, kernel_size=(3,), stride=(1,))
(bn3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(pool3): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)
(conv4): Conv1d(64, 64, kernel_size=(3,), stride=(1,))
(bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(pool4): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)
(fc1): Linear(in_features=64, out_features=35, bias=True)
)
Number of parameters: 26915
我们将使用本文中使用的相同优化技术,即权重衰减设置为 0.0001 的 Adam 优化器。起初,我们将以 0.01 的学习率进行训练,但scheduler在 20 个 epoch 之后的训练期间,我们将使用 a 将其降低到 0.001。
optimizer = optim.Adam(model.parameters(), lr=0.01, weight_decay=0.0001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.1) # reduce the learning after 20 epochs by a factor of 10
四、训练模型
现在让我们定义一个训练函数,它将我们的训练数据输入模型并执行反向传递和优化步骤。对于训练,我们将使用的损失是负对数似然。然后将在每个 epoch 之后对网络进行测试,以查看在训练期间准确性如何变化。
# 为加速代码运行,训练过程中不计算准确率。
def train(model, epoch, log_interval):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data = data.to(device)
target = target.to(device)
# apply transform and model on whole batch directly on device
data = transform(data)
output = model(data)
# 计算 loss
loss = F.nll_loss(output.squeeze(), target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 打印训练进度
if batch_idx % log_interval == 0:
print(f"Train Epoch: epoch [batch_idx * len(data)/len(train_loader.dataset) (100. * batch_idx / len(train_loader):.0f%)]\\tLoss: loss.item():.6f")
# 记录 loss
losses.append(loss.item())
# 计算预测正确的数目
def number_of_correct(pred, target):
return pred.squeeze().eq(target).sum().item()
# 找到最有可能的标签
def get_likely_index(tensor):
return tensor.argmax(dim=-1)
def test(model, epoch):
model.eval()
correct = 0
for data, target in test_loader:
data = data.to(device)
target = target.to(device)
# apply transform and model on whole batch directly on device
data = transform(data)
output = model(data)
pred = get_likely_index(output)
correct += number_of_correct(pred, target)
print(f"\\nTest Epoch: epoch\\tAccuracy: correct/len(test_loader.dataset) (100. * correct / len(test_loader.dataset):.0f%)\\n")
最后,我们可以训练和测试网络。我们将网络训练 10 个 epoch,然后降低学习率并再训练 10 个 epoch。网络将在每个 epoch 之后进行测试,以查看在训练期间准确度如何变化。
log_interval = 100 # 每100个batch打印一次训练结果
n_epoch = 2
losses = []
# The transform needs to live on the same device as the model and the data.
transform = transform.to(device)
for epoch in range(1, n_epoch + 1):
train(model, epoch, log_interval)
test(model, epoch)
scheduler.step()
Train Epoch: 1 [0/84843 (0%)] Loss: 3.655148
Train Epoch: 1 [25600/84843 (30%)] Loss: 2.012523
Train Epoch: 1 [51200/84843 (60%)] Loss: 1.584120
Train Epoch: 1 [76800/84843 (90%)] Loss: 1.249869
Test Epoch: 1 Accuracy: 6962/11005 (63%)
Train Epoch: 2 [0/84843 (0%)] Loss: 0.964569
Train Epoch: 2 [25600/84843 (30%)] Loss: 1.161757
Train Epoch: 2 [51200/84843 (60%)] Loss: 1.007113
Train Epoch: 2 [76800/84843 (90%)] Loss: 0.843660
Test Epoch: 2 Accuracy: 7219/11005 (66%)
1. 训练过程中的loss
plt.plot(losses)
plt.xlabel("Step", fontsize=12)
plt.ylabel("Loss", fontsize=12)
plt.title("Training Loss")
plt.show()
五、测试模型
def predict(tensor):
tensor = tensor.to(device)
tensor = transform(tensor)
tensor = model(tensor.unsqueeze(0))
tensor = get_likely_index(tensor)
tensor = index_to_label(tensor.squeeze())
return tensor
waveform, sample_rate, utterance, *_ = train_set[-1]
ipd.Audio(waveform.numpy(), rate=sample_rate)
print(f"真实值: utterance. 预测值: predict(waveform).")
真实值: zero. 预测值: zero.
for i, (waveform, sample_rate, utterance, *_) in enumerate(test_set):
output = predict(waveform)
if output != utterance:
ipd.Audio(waveform.numpy(), rate=sample_rate)
print(f"Data point #i. 真实值: utterance. 预测值: output.")
break
else:
print("All examples in this dataset were correctly classified!")
print("In this case, let's just look at the last data point")
ipd.Audio(waveform.numpy(), rate=sample_rate)
print(f"Data point #i. 真实值: utterance. 预测值: output.")
Data point #1. 真实值: right. 预测值: no.
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