【AudioClassificationModelZoo-Pytorch】基于Pytorch的声音事件检测分类系统
源码：https://github.com/Shybert-AI/AudioClassificationModelZoo-Pytorch
模型测试表
| 模型网络结构 | batch_size | FLOPs(G) | Params(M) | 特征提取方式 | 数据集 | 类别数量 | 模型验证集性能 |
|---|---|---|---|---|---|---|---|
| EcapaTdnn | 128 | 0.48 | 6.1 | mel | UrbanSound8K | 10 | accuracy=0.974, precision=0.972 recall=0.967, F1-score=0.967 |
| PANNS（CNN6) | 128 | 0.98 | 4.57 | mel | UrbanSound8K | 10 | accuracy=0.971, precision=0.963 recall=0.954, F1-score=0.955 |
| TDNN | 128 | 0.21 | 2.60 | mel | UrbanSound8K | 10 | accuracy=0.968, precision=0.964 recall=0.959, F1-score=0.958 |
| PANNS（CNN14) | 128 | 1.98 | 79.7 | mel | UrbanSound8K | 10 | accuracy=0.966, precision=0.956 recall=0.957, F1-score=0.952 |
| PANNS（CNN10) | 128 | 1.29 | 4.96 | mel | UrbanSound8K | 10 | accuracy=0.964, precision=0.955 recall=0.955, F1-score=0.95 |
| DTFAT(MaxAST) | 16 | 8.32 | 68.32 | mel | UrbanSound8K | 10 | accuracy=0.963, precision=0.939 recall=0.935, F1-score=0.933 |
| EAT-M-Transformer | 128 | 0.16 | 1.59 | mel | UrbanSound8K | 10 | accuracy=0.935, precision=0.905 recall=0.907, F1-score=0.9 |
| AST | 16 | 5.28 | 85.26 | mel | UrbanSound8K | 10 | accuracy=0.932, precision=0.893 recall=0.887, F1-score=0.884 |
| TDNN_GRU_SE | 256 | 0.26 | 3.02 | mel | UrbanSound8K | 10 | accuracy=0.929, precision=0.916 recall=0.907, F1-score=0.904 |
| mn10_as | 128 | 0.03 | 4.21 | mel | UrbanSound8K | 10 | accuracy=0.912, precision=0.88 recall=0.894, F1-score=0.878 |
| dymn10_as | 128 | 0.01 | 4.76 | mel | UrbanSound8K | 10 | accuracy=0.904, precision=0.886 recall=0.883, F1-score=0.872 |
| ERes2NetV2 | 128 | 0.87 | 5.07 | mel | UrbanSound8K | 10 | accuracy=0.874, precision=0.828 recall=0.832, F1-score=0.818 |
| ResNetSE_GRU | 128 | 1.84 | 10.31 | mel | UrbanSound8K | 10 | accuracy=0.865, precision=0.824 recall=0.827, F1-score=0.813 |
| ResNetSE | 128 | 1.51 | 7.15 | mel | UrbanSound8K | 10 | accuracy=0.859, precision=0.82 recall=0.819, F1-score=0.807 |
| CAMPPlus | 128 | 0.47 | 7.30 | mel | UrbanSound8K | 10 | accuracy=0.842, precision=0.793 recall=0.788, F1-score=0.778 |
| HTS-AT | 16 | 5.70 | 27.59 | mel | UrbanSound8K | 10 | accuracy=0.84, precision=0.802 recall=0.796, F1-score=0.795 |
| EffilecentNet_B2 | 128 | – | 7.73 | mel | UrbanSound8K | 10 | accuracy=0.779, precision=0.718 recall=0.741, F1-score=0.712 |
| ERes2Net | 128 | 1.39 | 6.22 | mel | UrbanSound8K | 10 | accuracy=0.778, precision=0.808 recall=0.787, F1-score=0.779 |
| Res2Net | 128 | 0.04 | 5.09 | mel | UrbanSound8K | 10 | accuracy=0.723, precision=0.669 recall=0.672, F1-score=0.648 |
| MobileNetV4 | 128 | 0.03 | 2.51 | mel | UrbanSound8K | 10 | accuracy=0.608, precision=0.553 recall=0.549, F1-score=0.523 |
说明：
使用的测试集为从数据集中每10条音频取一条，共874条。
5.准备数据
生成数据集的list,label_list.txt,train_list.txt,test_list.txt
执行create_data.py即可生成数据列表，里面提供了生成多种数据集列表方式，具体看代码。
python create_data.py
生成的列表是长这样的，前面是音频的路径，后面是该音频对应的标签，从0开始，路径和标签之间用\t隔开。
dataset/UrbanSound8K/audio/fold2/104817-4-0-2.wav 4
dataset/UrbanSound8K/audio/fold9/105029-7-2-5.wav 7
dataset/UrbanSound8K/audio/fold3/107228-5-0-0.wav 5
dataset/UrbanSound8K/audio/fold4/109711-3-2-4.wav 3
5.特征提取（可选，如果进行特征提取，训练耗时提升36倍）,已提取的特征文件和已训练的模型文件下载。模型放到model目录下，特征放到features目录下。
链接: https://pan.baidu.com/s/15ziJovO3t41Nqgqtmovuew 提取码: 8a59
python extract_feature.py
6.训练,可以通过指定–model_type的参数来指定模型，进行模型训练。
如：EcapaTdnn、PANNS（CNN6)、TDNN、PANNS（CNN14)、PANNS（CNN10)、DTFAT(MaxAST)、EAT-M-Transformer、AST、TDNN_GRU_SE、mn10_as、dymn10_as、ERes2NetV2、ResNetSE_GRU、ResNetSE、CAMPPlus、HTS-AT、EffilecentNet_B2、ERes2Net、Res2Net、MobileNetV4
python train.py --model_type EAT-M-Transformer
在线提取特征训练的日志为：
Epoch: 10
Train: 100%|██████████| 62/62 [07:28<00:00, 7.23s/it, BCELoss=0.931, accuracy=0.502, precision=0.563, recall=0.508, F1-score=0.505]
Valid: 100%|██████████| 14/14 [00:53<00:00, 3.82s/it, BCELoss=1.19, accuracy=0.425, precision=0.43, recall=0.393, F1-score=0.362]
Epoch: 11
Train: 100%|██████████| 62/62 [07:23<00:00, 7.16s/it, BCELoss=2.17, accuracy=0.377, precision=0.472, recall=0.386, F1-score=0.375]
Valid: 100%|██████████| 14/14 [00:48<00:00, 3.47s/it, BCELoss=2.7, accuracy=0.362, precision=0.341, recall=0.328, F1-score=0.295]
Epoch: 12
Train: 100%|██████████| 62/62 [07:20<00:00, 7.11s/it, BCELoss=1.8, accuracy=0.297, precision=0.375, recall=0.308, F1-score=0.274]
Valid: 100%|██████████| 14/14 [00:48<00:00, 3.47s/it, BCELoss=1.08, accuracy=0.287, precision=0.317, recall=0.285, F1-score=0.234]
离线提取特征训练的日志为：
Epoch: 1
Train: 100%|██████████| 62/62 [00:12<00:00, 4.77it/s, BCELoss=8.25, accuracy=0.0935, precision=0.0982, recall=0.0878, F1-score=0.0741]
Valid: 100%|██████████| 14/14 [00:00<00:00, 29.53it/s, BCELoss=5.98, accuracy=0.142, precision=0.108, recall=0.129, F1-score=0.0909]
Model saved in the folder : model
Model name is : SAR_Pesudo_ResNetSE_s0_BCELoss
Epoch: 2
Train: 100%|██████████| 62/62 [00:12<00:00, 4.93it/s, BCELoss=7.71, accuracy=0.117, precision=0.144, recall=0.113, F1-score=0.0995]
Valid: 100%|██████████| 14/14 [00:00<00:00, 34.54it/s, BCELoss=8.15, accuracy=0.141, precision=0.0811, recall=0.133, F1-score=0.0785]
7.测试
测试采用流式测试的方式，即每次送入模型2秒的音频数据，将音频数据转为[1,1,64,100]维度的张量数据，然后送入到模型中进行推理，每次都很得到推理的结构，可以根据阈值来判断该事件是否发生。
python model_test.py --model_type EAT-M-Transformer