  The sequence length along the time axis is often the dominant factor of the
computation in speech processing. Works have been proposed to reduce the
sequence length for lowering the computational cost in self-supervised speech
models. 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 operating 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.
