VocalNet: Speech LLM with Multi-Token Prediction for Faster and High-Quality Generation

• URL: http://arxiv.org/abs/2504.04060v2
• Date: Tue, 22 Apr 2025 07:59:31 GMT
• Title: VocalNet: Speech LLM with Multi-Token Prediction for Faster and High-Quality Generation
• Authors: Yuhao Wang, Heyang Liu, Ziyang Cheng, Ronghua Wu, Qunshan Gu, Yanfeng Wang, Yu Wang,
• Abstract summary: Speech large language models (LLMs) have emerged as a prominent research focus in speech processing.<n>We introduce VocalNet-1B and VocalNet-8B, a series of high-performance, low-latency speech LLMs enabled by a scalable and model-agnostic training framework.<n>Central to our contribution is the first application of multi-token prediction (MTP) to speech LLMs.
• Score: 26.34810950257782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech large language models (LLMs) have emerged as a prominent research focus in speech processing. We introduce VocalNet-1B and VocalNet-8B, a series of high-performance, low-latency speech LLMs enabled by a scalable and model-agnostic training framework designed for real-time voice interaction. Central to our contribution is the first application of multi-token prediction (MTP) to speech LLMs. This approach represents a paradigm shift from standard next-token prediction (NTP), offering simultaneous improvements in generation speed and quality. Informed by analysis of MTP's effect on speech generation and experimental comparisons, we designed a straightforward and highly effective MTP implementation. Experiments demonstrate that VocalNet performs on par with mainstream Omni LLMs even with limited training data, and significantly surpasses existing open-source speech LLMs. To foster reproducibility and community advancement, all model weights, inference code, training data, and framework implementations have been made publicly available at https://github.com/SJTU-OmniAgent/VocalNet

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