---
license: bsd-3-clause
pipeline_tag: audio-classification
library_name: transformers
tags:
  - PyTorch
  - State-space
  - Mamba
---

# DASS: Distilled Audio State-space Models

<!-- Provide a quick summary of what the model is/does. -->


DASS: Distilled Audio State-space Models is an audio classification model finetuned on AudioSet-2M. 
DASS is the first state-space model that outperforms transformer-based audio classifiers such as AST (Audio Spectrogram Transformer), HTS-AT, and Audio-MAE.
DASS achieves state-of-the-art performance on the audio-classification
task on Audioset while significantly reducing the model size. For example, compared to AST which contains approximately 87M
parameters, DASS-small contains one-third, 30M, parameters and outperforms the AST model (AudioSet-2M map: 45.9 vs DASS small mAP: 47.2). 
It is available in two variants: DASS small (30M) mAP: 47.2 and DASS medium (49M) mAP: 47.6.

It is also significantly more duration robust (training on shorter audio and testing on long audio without fine-tuning on longer audio) than the AST model. 
For example, for both AST and DASS models training on 10-second long audios, the performance of AST models drops to less than 5 mAP when 
the input is 50 seconds, which is < 12% of the performance for 10-second input, while DASS’s performance is 45.5 mAP (96%) in the same setting. 
On a single A6000 GPU, DASS can take up to 2.5-hours of audio input and still maintain 62% of its
performance compared to a 10-second input.

It is introduced in the paper [DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners](https://arxiv.org/pdf/2407.04082) and 
first released in [this repository](https://github.com/Saurabhbhati/DASS).

## Model Details

DASS model in based on the [VMamba: Visual State Space Model](https://arxiv.org/pdf/2401.10166) applied to audio. 
It is trained with binary cross entropy loss w.r.t. ground truth labels and kl-divergence loss w.r.t teacher AST model. 

## How to Get Started with the Model

Use the code below to get started with the model.

```python

import torch
import librosa
from transformers import AutoConfig, AutoModelForAudioClassification, AutoFeatureExtractor

config = AutoConfig.from_pretrained('saurabhati/DASS_small_AudioSet_47.2',trust_remote_code=True)
audio_model = AutoModelForAudioClassification.from_pretrained('saurabhati/DASS_small_AudioSet_47.2',trust_remote_code=True)
feature_extractor = AutoFeatureExtractor.from_pretrained('saurabhati/DASS_small_AudioSet_47.2',trust_remote_code=True)

waveform, sr = librosa.load("audio/eval/_/_/--4gqARaEJE_0.000.flac", sr=16000)
inputs = feature_extractor(waveform,sr, return_tensors='pt')

with torch.no_grad():
    logits = torch.sigmoid(audio_model(**inputs).logits)

predicted_class_ids = torch.where(logits[0] > 0.5)[0]
predicted_label = [audio_model.config.id2label[i.item()] for i in predicted_class_ids]
predicted_label
['Animal', 'Domestic animals, pets', 'Dog']

```

### Results

Below are the results for DASS models finetuned and evaluated on AudioSet-2M. 

|                                           | Params | Pretrain |  mAP |
|-------------------------------------------|:------:|:--------:|:----:|
| Transformer based models                                             |
| [AST](https://arxiv.org/pdf/2104.01778)                 |   87M  |   IN SL  | 45.9 |
| [HTS-AT](https://arxiv.org/pdf/2202.00874)              |   31M  |   IN SL  | 47.1 |
| [PaSST](https://arxiv.org/pdf/2110.05069)         |        |   IN SL  | 47.1 |
| [Audio-MAE](https://arxiv.org/pdf/2207.06405) |   86M  |    SSL   | 47.3 |
| Concurrent SSM models                     |        |          |      |
| [AuM](https://arxiv.org/pdf/2406.03344)               |   26M  |   IN SL  | 39.7 |
| [Audio Mamba](https://arxiv.org/pdf/2405.13636)         |   40M  |   IN SL  | 44.0 |
| DASS-Small                                |   30M  |   IN SL  | 47.2 |
| DASS-Medium                               |   49M  |   IN SL  | 47.6 |


## Citation

```bibtex
@article{bhati2024dass,
  title={DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners},
  author={Bhati, Saurabhchand and Gong, Yuan and Karlinsky, Leonid and Kuehne, Hilde and Feris, Rogerio and Glass, James},
  journal={arXiv preprint arXiv:2407.04082},
  year={2024}
}
```

## Acknowledgements 

This project is based on AST([paper](https://arxiv.org/pdf/2104.01778), [code](https://github.com/YuanGongND/ast/tree/master)), 
VMamba([paper](https://arxiv.org/pdf/2401.10166), [code](https://github.com/MzeroMiko/VMamba/tree/main)) thanks for their excellant works.
Please make sure to check them out.