Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition
Authors: name: Zhisheng Zhong, name: Chengyao Wang, name: Yuqi Liu, name: Senqiao Yang, name: Longxiang Tang, name: Yuechen Zhang, name: Jingyao Li, name: Tianyuan Qu, name: Yanwei Li, name: Yukang Chen, name: Shaozuo Yu, name: Sitong Wu, name: Eric Lo, name: Shu Liu, name: Jiaya Jia

As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond
single-domain capabilities is essential to meet the demands for more versatile
and efficient AI. However, previous omni-models have insufficiently explored
speech, neglecting its integration with multi-modality. We introduce Lyra, an
efficient MLLM that enhances multimodal abilities, including advanced
long-speech comprehension, sound understanding, cross-modality efficiency, and
seamless speech interaction. To achieve efficiency and speech-centric
capabilities, Lyra employs three strategies: (1) leveraging existing
open-source large models and a proposed multi-modality LoRA to reduce training
costs and data requirements; (2) using a latent multi-modality regularizer and
extractor to strengthen the relationship between speech and other modalities,
thereby enhancing model performance; and (3) constructing a high-quality,
extensive dataset that includes 1.5M multi-modal (language, vision, audio) data
samples and 12K long speech samples, enabling Lyra to handle complex long
speech inputs and achieve more robust omni-cognition. Compared to other
omni-methods, Lyra achieves state-of-the-art performance on various
vision-language, vision-speech, and speech-language benchmarks, while also
using fewer computational resources and less training data.