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arXiv · 2306.05617

Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

Chenglong Wang·Jiangyan Yi·Xiaohui Zhang·Jianhua Tao·Le Xu·Ruibo Fu
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Abstract

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times.

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Chenglong Wang, Jiangyan Yi, Xiaohui Zhang, Jianhua Tao, Le Xu, Ruibo Fu. 2023-06-09. Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection. https://arxiv.org/abs/2306.05617

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