LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive
  Streaming Speech Synthesis
Authors: name: Qingkai Fang, name: Yan Zhou, name: Shoutao Guo, name: Shaolei Zhang, name: Yang Feng

Real-time, intelligent, and natural speech interaction is an essential part
of the next-generation human-computer interaction. Recent advancements have
showcased the potential of building intelligent spoken chatbots based on large
language models (LLMs). In this paper, we introduce LLaMA-Omni 2, a series of
speech language models (SpeechLMs) ranging from 0.5B to 14B parameters, capable
of achieving high-quality real-time speech interaction. LLaMA-Omni 2 is built
upon the Qwen2.5 series models, integrating a speech encoder and an
autoregressive streaming speech decoder. Despite being trained on only 200K
multi-turn speech dialogue samples, LLaMA-Omni 2 demonstrates strong
performance on several spoken question answering and speech instruction
following benchmarks, surpassing previous state-of-the-art SpeechLMs like
GLM-4-Voice, which was trained on millions of hours of speech data.