Once-for-All Sequence Compression for Self-Supervised Speech Models
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The sequence length along the time axis is often the dominant factor of the computational cost of self-supervised speech models. Works have been proposed to reduce the sequence length for lowering the computational cost. However, different downstream tasks have different tolerance of sequence compressing, so a model that produces a fixed compressing rate may not fit all tasks. In this work, we introduce a once-for-all (OFA) sequence compression framework for self-supervised speech models that supports a continuous range of compressing rates. The framework is evaluated on various tasks, showing marginal degradation compared to the fixed compressing rate variants with a smooth performance-efficiency trade-off. We further explore adaptive compressing rate learning, demonstrating the ability to select task-specific preferred frame periods without needing a grid search.
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