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distil-ast-audioset

Audio Classification
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metadata
language: en
license: apache-2.0
tags:
  - audio-classification
  - generated_from_trainer
metrics:
  - accuracy
  - f1

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