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LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis

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arxiv 2505.02625 v1 pith:IP2BH3AH submitted 2025-05-05 cs.CL cs.AIcs.SDeess.AS

Qingkai Fang , Yan Zhou , Shoutao Guo , Shaolei Zhang , Yang Feng This is my paper · ORCID
classification cs.CLcs.AIcs.SDeess.AS
keywords speechinteractionllama-omnimodelsreal-timespokenautoregressiveintelligent
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning

    cs.CL 2026-01 conditional novelty 6.0 of 10

    PALU shows that unlearning only needs local intervention—the first few tokens of the sensitive span and the top-k logits—not full-sequence, full-vocabulary suppression.

  2. X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment

    cs.LG 2026-07 conditional novelty 5.0 of 10

    X3-OPD improves audio-grounded reasoning by training the audio student on its own rollouts with token-level teacher feedback, using a three-tier paired text-audio corpus.

  3. ChipChat: Low-Latency Cascaded Conversational Agent in MLX

    eess.AS 2025-08 conditional novelty 5.0 of 10

    ChipChat is a fully on-device cascaded voice agent that reports about 920 ms total latency using streaming ASR, a state-action LLM, streaming TTS, and a vocoder.

  4. Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model

    cs.AI 2025-06 conditional novelty 5.0 of 10

    Stream-Omni uses CTC-based layer-dimension mapping to align speech with text, achieving vision, speech, and text interaction in one 8B model trained on 23,000 hours of speech.

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