MambAdapter: Lightweight Mamba-Based Adapters for Parameter-Efficient Transfer Learning in Speech and Audio
Abstract
Fine-tuning Transformer-based foundation models has become the dominant strategy for domain adaptation in audio and speech processing. To reduce the computational and memory costs of this process, parameter-efficient transfer learning (PETL) methods have been widely explored. Meanwhile, Mamba, a recent state-space model, has emerged as a promising alternative to Transformers for sequence modeling. In this work, we present MambAdapter, a parameter-efficient transfer learning approach that integrates Mamba into low-rank bottleneck adapters. Our design combines parameter sharing across adapters with the injection of a lightweight Mamba module, enabling more effective modeling of audio features. We demonstrate that MambAdapter matches or outperforms strong PETL baselines on four audio classification tasks and five speech recognition languages, even when operating under reduced parameter budgets.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this work is the introduction of MambAdapter, a novel parameter-efficient transfer learning method that combines Mamba's state-space modeling with low-rank bottleneck adapters, achieving competitive performance on audio and speech tasks while significantly reducing the number of trainable parameters. This paper represents a meaningful advancement in the quest for efficient model adaptation in the rapidly evolving field of audio processing.
Methodology
The paper introduces MambAdapter, which innovatively integrates Mamba, a state-space model, into low-rank bottleneck adapters for parameter-efficient transfer learning in speech and audio tasks. The methodology is well-grounded in existing literature, leveraging the strengths of Mamba's linear-time modeling capabilities while addressing the inefficiencies of traditional Transformer fine-tuning. The use of shared projections and the lightweight Mamba module is a thoughtful design choice that enhances the model's ability to capture long-range dependencies in audio data.
Results & evaluation
The experimental setup is robust, with comprehensive evaluations across multiple audio classification tasks and multilingual speech recognition. The authors provide a clear comparison against established PETL baselines, demonstrating that MambAdapter achieves competitive or superior performance while maintaining a lower parameter budget. The results are statistically validated through averaging over multiple random seeds, which adds credibility to their findings.
Reproducibility
The paper includes a link to the code repository, which is essential for reproducibility. However, the paper could benefit from more detailed hyperparameter settings and training configurations to facilitate easier replication of results by other researchers.
Limitations
While the paper presents promising results, it does not extensively explore the limitations of MambAdapter, such as potential performance degradation in extremely low-resource settings or the impact of varying audio characteristics on model performance. Additionally, the focus on a limited number of datasets may restrict the generalizability of the findings.
Broader impact
The integration of Mamba into PETL frameworks has significant implications for the field of audio and speech processing, particularly in resource-constrained environments. The findings could influence future research directions in efficient model adaptation, potentially leading to advancements in real-time speech recognition and audio classification applications. The main contribution of this work is the introduction of MambAdapter, a novel parameter-efficient transfer learning method that combines Mamba's state-space modeling with low-rank bottleneck adapters, achieving competitive performance on audio and speech tasks while significantly reducing the number of trainable parameters. This paper represents a meaningful advancement in the quest for efficient model adaptation in the rapidly evolving field of audio processing.
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