Reproduce “Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis” on OpenResearch
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About this paper
Xilin Jiang, Yinghao Aaron Li, Adrian Nicolas Florea, Cong Han, Nima Mesgarani
Researchers at Columbia University evaluated Mamba against Transformers across speech separation, recognition, and synthesis tasks. Mamba models matched or exceeded Transformer performance for encoding and non-autoregressive tasks while providing substantial memory and speed efficiency for longer speech sequences, particularly in high-resolution processing.
Key insights
- Bidirectional Mamba encoders demonstrated performance comparable to or superior to Transformer encoders for tasks like speech separation and ASR, efficiently capturing long-range dependencies.
- Unidirectional Mamba decoders, particularly for complex autoregressive generation and multimodal input integration (e.g., ASR, TTS), generally lagged behind Transformer decoders.
- Mamba's linear complexity yielded significant memory and speed efficiency gains for longer speech sequences, with benefits becoming more pronounced as the task's token resolution increased (i.e., for tasks with effectively longer sequences).
Summary by alphaXiv.
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