Soundwave: Less is More for Speech-Text Alignment in LLMs

Existing end-to-end speech large language models (LLMs) usually rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth.We focus on two fundamental problems between speech and text: the representation space gap and sequence length inconsistency.We propose Soundwave, which utilizes an efficient training strategy and a novel architecture to address these issues.Results show that Soundwave outperforms other advanced speech LLMs in speech translation and AIR-Bench speech tasks with only a fraction of the training data.Further analysis shows that Soundwave still retains its intelligence during conversation.