【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