DASS_small_AudioSet_50.1

DASS: Distilled Audio State-space Models

This version is distilled from SSLAM (88M), DASS small (30M) mAP: 50.1 and DASS medium (49M) mAP: 50.2.

It is introduced in the paper DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners and first released in this repository.

How to Get Started with the Model

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

config = AutoConfig.from_pretrained('saurabhati/DASS_small_AudioSet_50.1',trust_remote_code=True)
audio_model = AutoModelForAudioClassification.from_pretrained('saurabhati/DASS_small_AudioSet_50.1',trust_remote_code=True)
feature_extractor = AutoFeatureExtractor.from_pretrained('saurabhati/DASS_small_AudioSet_50.1',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']

Model Details

<!-- 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 sizes: DASS small (30M) mAP: 47.2 and DASS medium (49M) mAP: 47.6. DASSv2 model is distilled from an ensemble of AST and HTS-AT which sigificantly boosts the performance on Audio classification task. New performance: DASS small (30M) mAP: 48.6 and DASS medium (49M) mAP: 48.9.

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.

DASS model in based on the VMamba: Visual State Space Model 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.

Results

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

Citation

@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, code), VMamba(paper, code) thanks for their excellant works. Please make sure to check them out.