MambAdapter: Lightweight Mamba-Based Adapters for Transfer Learning (arxiv.org)

Researchers have introduced MambAdapter, a novel approach designed to enhance parameter-efficient transfer learning in the realms of audio and speech processing. By integrating the recently developed Mamba state-space model into low-rank bottleneck adapters, MambAdapter significantly reduces computational and memory demands while maintaining robust performance. This innovative design facilitates parameter sharing across adapters and incorporates the lightweight Mamba module, making it adept at modeling complex audio features. The significance of MambAdapter lies in its ability to match or exceed the performance of existing state-of-the-art methods in parameter-efficient transfer learning (PETL) across various tasks, including four audio classification challenges and recognition in five different speech languages. This accomplishment underscores the potential of MambAdapter to optimize transformer-based models for practical applications, thereby broadening the capabilities and accessibility of state-of-the-art AI tools in the audio processing landscape—all while operating under constrained parameter budgets.