Stride-k Subsampling: Train-Free Audio Token Reduction for Whisper
Whisper exposes speech through a fixed 1500-token encoder interface, now a default representation for ASR decoders and Whisper-based speech language models (SpeechLMs), yet its redundancy remains largely unexamined. We propose stride-k subsampling, a deterministic indexing operation that retains every k-th token after the convolutional stem or encoder transformer. Across five Whisper scales, k=2 preserves baseline WER at both positions, with CKA attributing this stability to acoustic overlap at the stem and attention-induced redistribution at the encoder output. Applying stride-2 at both positions cuts audio tokens by 75% and total GFLOPs by 52-58%, with small WER costs on most ASR benchm...
| Authors | Chanhee Cho, Junhyuk Choi, Bugeun Kim |
|---|---|
| Primary category | cs.SD |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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