Papers › Efficient Training of Audio Transformers with Patchout
Efficient Training of Audio Transformers with Patchout
Khaled Koutini, Jan Schlüter, Hamid Eghbal-zadeh, Gerhard Widmer
The great success of transformer-based models in natural language processing (NLP) has led to various attempts at adapting these architectures to other domains such as vision and audio. Recent work has shown that transformers can outperform Convolutional Neural Networks (CNNs) on vision and audio tasks. However, one of the main shortcomings of transformer models, compared to the well-established CNNs, is the computational complexity. In transformers, the compute and memory complexity is known to grow quadratically with the input length. Therefore, there has been extensive work on optimizing transformers, but often at the cost of degrading predictive performance. In this work, we propose a novel method to optimize and regularize transformers on audio spectrograms. Our proposed models achieve a new state-of-the-art performance on Audioset and can be trained on a single consumer-grade GPU. Furthermore, we propose a transformer model that outperforms CNNs in terms of both performance and training speed. Source code: https://github.com/kkoutini/PaSST
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Classification | AudioSet | PaSST (Ensemble) | Test mAP | 0.496 | #13 of 51 | Archive leaderboard | report |
| Audio Classification | AudioSet | PaSST-S (Single) | Test mAP | 0.471 | #32 of 51 | Archive leaderboard | report |
| Audio Classification | FSD50K | PaSST-S | mAP | 65.55 | #3 of 10 | Archive leaderboard | report |
| Audio Classification | FSD50K | PaSST-N-S | mAP | 64.2 | #5 of 10 | Archive leaderboard | report |
| Audio Tagging | AudioSet | PaSST | mean average precision | 0.496 | #3 of 11 | Archive leaderboard | report |
| Instrument Recognition | OpenMIC-2018 | PaSST | mean average precision | 0.843 | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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