saurabhati
/

DASS_small_AudioSet_50.1

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 ---
+license: bsd-3-clause
+pipeline_tag: audio-classification
 library_name: transformers
+tags:
+  - PyTorch
+  - State-space
+  - Mamba
 ---
+# 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](https://arxiv.org/pdf/2407.04082) and
+first released in [this repository](https://github.com/Saurabhbhati/DASS).
 ## How 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_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](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.
 ### 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 |
+| [BEATS_iter3](https://arxiv.org/pdf/2212.09058)         |   90M  |  AS SSL  | 48.6 |
+| [EAT](https://arxiv.org/pdf/2401.03497v1)               |   88M  |  AS SSL  | 48.6 |
+| [SSLAM](https://openreview.net/pdf?id=odU59TxdiB)       |   88M  |  AS SSL  | 50.2
+| 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 |
+| DASS-Small (teach: AST + HTS-AT)                        |   30M  |   IN SL  | 48.6 |
+| DASS-Medium (teach: AST + HTS-AT)                       |   49M  |   IN SL  | 48.9 |
+| DASS-Small (teach: SSLAM)                               |   30M  |   IN SL  | 50.1 |
+| DASS-Medium (teach: SSLAM)                              |   49M  |   IN SL  | 50.2 |
+## 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.