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---
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](https://arxiv.org/abs/2104.01778) architecture. This model is a distilled version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the [AudioSet](https://research.google.com/audioset/download.html) 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](https://huggingface.co/bookbot/distil-ast-audioset/tree/main) tab, as well as the [Training metrics](https://huggingface.co/bookbot/distil-ast-audioset/tensorboard) 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](https://anantoj.github.io), [David Samuel Setiawan](https://davidsamuell.github.io/), [Wilson Wongso](https://wilsonwongso.dev/). 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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