Six Layers Less: Encoder Pruning for Whisper with Label-Free Recovery
Pruning large pre-trained transformer-based ASR models such as OpenAI's Whisper has seen great adoption, as pruning the decoder led to significant end-to-end transcription speedups. For instance, the {\tt whisper-large-v3-turbo} variant reduced the decoder from 32 to 4 layers, while Distill-Whisper similarly reduced the decoder to only 2 layers. Although some attention has been put towards reducing the size of the encoder, no approach has seen wide adoption. This could be due to the need for custom inference implementations to take advantage of the compressed model. We present an approach that ranks encoder layers by the leave-one-layer-out change in Word Error Rate (WER). The six layers ...
| Authors | Rasmus Aagaard, Nicki Skafte Detlefsen |
|---|---|
| Primary category | cs.CL |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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