  Neural architecture search (NAS) has been successfully applied to tasks like
image classification and language modeling for finding efficient
high-performance network architectures. In ASR field especially end-to-end ASR,
the related research is still in its infancy. In this work, we focus on
applying NAS on the most popular manually designed model: Conformer, and then
propose an efficient ASR model searching method that benefits from the natural
advantage of differentiable architecture search (Darts) in reducing
computational overheads. We fuse Darts mutator and Conformer blocks to form a
complete search space, within which a modified architecture called
Darts-Conformer cell is found automatically. The entire searching process on
AISHELL-1 dataset costs only 0.7 GPU days. Replacing the Conformer encoder by
stacking searched cell, we get an end-to-end ASR model (named as
Darts-Conformner) that outperforms the Conformer baseline by 4.7\% on the
open-source AISHELL-1 dataset. Besides, we verify the transferability of the
architecture searched on a small dataset to a larger 2k-hour dataset. To the
best of our knowledge, this is the first successful attempt to apply
gradient-based architecture search in the attention-based encoder-decoder ASR
model.
