  Self-supervised speech representation learning (SSL) has shown to be
effective in various downstream tasks, but SSL models are usually large and
slow. Model compression techniques such as pruning aim to reduce the model size
and computation without degradation in accuracy. Prior studies focus on the
pruning of Transformers; however, speech models not only utilize a stack of
Transformer blocks, but also combine a frontend network based on multiple
convolutional layers for low-level feature representation learning. This
frontend has a small size but a heavy computational cost. In this work, we
propose three task-specific structured pruning methods to deal with such
heterogeneous networks. Experiments on LibriSpeech and SLURP show that the
proposed method is more accurate than the original wav2vec2-base with 10% to
30% less computation, and is able to reduce the computation by 40% to 50%
without any degradation.
