Introduction

Simplified PyTorch implementation of audio classification, support multi-gpu training and validating, automatic mixed precision training, knowledge distillation etc.

Requirements

torch == 1.8.1
torchvision
torchaudio
torchmetrics == 1.2.0
loguru
tqdm
timm == 0.6.12 (optional)

Supported models

• L3Net ¹
• ResNets ²
• MobileNetV2 ³
• timm ⁴

This repo also supports timm⁴ which provides hundereds of pretrained models. For example, if you want to train mobilenetv3_small from timm, you may change the config file to

config.model = 'timm'
config.timm_model = 'mobilenetv3_small_100'

or use command-line arguments

python main.py --model timm --timm_model mobilenetv3_small_100

Details of the configurations can also be found in this file.

Supported datasets

• ESC50 ⁵

If you want to test datasets from torchaudio, you may refer to this site. Noted that this site is outdated since the version of torchaudio(0.9.1) is bounded to torch(1.8.1). If you want to test datasets from newer version of torchaudio, you need to update this codebase to be compatible with newer torch. You can also download the audio files and build your own dataset following the style of ESC50 dataset if you don't want to update the codebase.

Knowledge Distillation

Currently only support the original knowledge distillation method proposed by Geoffrey Hinton.⁶

How to use

DDP training

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 main.py

DP training

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py

Performances

ESC50

Model	pretrained	fold-1 acc(%)	fold-2 acc(%)	fold-3 acc(%)	fold-4 acc(%)	fold-5 acc(%)	paper acc(%)	Mean Accuracy(%)
L3Net		81.25	80.75	78.50	82.50	81.50	79.3	80.90
ResNet18		73.25	74.75	74.00	75.25	73.25	n.a.	74.10
ResNet18	✅	85.50	85.50	86.50	88.00	84.75	n.a.	86.05
MobileNetv2		76.25	78.00	74.00	77.75	69.75	n.a.	75.15
MobileNetv2	✅	90.00	87.25	87.75	88.75	88.50	n.a.	88.45

References

Footnotes

1. Look, Listen and Learn ↩

2. Deep Residual Learning for Image Recognition ↩

3. MobileNetV2: Inverted Residuals and Linear Bottlenecks ↩

4. PyTorch Image Models ↩ ↩²

5. ESC-50: Dataset for Environmental Sound Classification ↩

6. Distilling the Knowledge in a Neural Network ↩