Distil Audio Spectrogram Transformer AudioSet

Distil Audio Spectrogram Transformer AudioSet is an audio classification model based on the Audio Spectrogram Transformer architecture. This model is a distilled version of MIT/ast-finetuned-audioset-10-10-0.4593 on the AudioSet dataset.

This model was trained using HuggingFace's PyTorch framework. All training was done on a Google Cloud Engine VM with a Tesla A100 GPU. All necessary scripts used for training could be found in the Files and versions tab, as well as the Training metrics logged via Tensorboard.

Model

Model	#params	Arch.	Training/Validation data
`distil-ast-audioset`	44M	Audio Spectrogram Transformer	AudioSet

Evaluation Results

The model achieves the following results on evaluation:

Model	F1	Roc Auc	Accuracy	mAP
Distil-AST AudioSet	0.4876	0.7140	0.0714	0.4743
AST AudioSet	0.4989	0.6905	0.1247	0.5603

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 0
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy	Map
1.5521	1.0	153	0.7759	0.3929	0.6789	0.0209	0.3394
0.7088	2.0	306	0.5183	0.4480	0.7162	0.0349	0.4047
0.484	3.0	459	0.4342	0.4673	0.7241	0.0447	0.4348
0.369	4.0	612	0.3847	0.4777	0.7332	0.0504	0.4463
0.2943	5.0	765	0.3587	0.4838	0.7284	0.0572	0.4556
0.2446	6.0	918	0.3415	0.4875	0.7296	0.0608	0.4628
0.2099	7.0	1071	0.3273	0.4896	0.7246	0.0648	0.4682
0.186	8.0	1224	0.3140	0.4888	0.7171	0.0689	0.4711
0.1693	9.0	1377	0.3101	0.4887	0.7157	0.0703	0.4741
0.1582	10.0	1530	0.3063	0.4876	0.7140	0.0714	0.4743

Disclaimer

Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.

Authors

Distil Audio Spectrogram Transformer AudioSet was trained and evaluated by Ananto Joyoadikusumo, David Samuel Setiawan, Wilson Wongso. All computation and development are done on Google Cloud.

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.10.0
Tokenizers 0.13.2

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