新的 vLLM 管道统一了音频生成和理解

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-02 12:55

研究人员开发了一种利用 vLLM 统一音频理解和生成任务的新型推理管道。该系统解决了高吞吐量多模态生成面临的挑战，特别是对于采用 AR+NAR 或多令牌预测等复杂解码策略的语音语言模型。该管道集成了片上声学解码器，用于端到端波形合成，并通过共同调度条件和无条件请求来优化无分类器引导，从而将吞吐量维持在非 CFG 吞吐量的约 80%。 AI

影响这项研究可能带来更高效、更强大的音频生成模型，并可能影响语音合成、内容创作和人机交互等领域的应用。

排序理由该项目是一篇学术论文，详细介绍了一种新的 AI 模型推理技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Haoran Wang, Jinchuan Tian, Siddhant Arora, Shinji Watanabe · 2026-07-03 04:00

An Efficient vLLM-Based Inference Pipeline for Unified Audio Understanding and Generation

arXiv:2607.02119v1 Announce Type: cross Abstract: While Large Multimodal Models excel in comprehension, high-throughput inference engines lack native support for multimodal generation. This is severe in Speech Language Models, where generating multi-layered audio tokens via decou…
arXiv cs.AI TIER_1 English(EN) · Shinji Watanabe · 2026-07-02 12:55

An Efficient vLLM-Based Inference Pipeline for Unified Audio Understanding and Generation

While Large Multimodal Models excel in comprehension, high-throughput inference engines lack native support for multimodal generation. This is severe in Speech Language Models, where generating multi-layered audio tokens via decoupled AR+NAR or synchronous Multi-Token Prediction …

报道来源 [2]

An Efficient vLLM-Based Inference Pipeline for Unified Audio Understanding and Generation

An Efficient vLLM-Based Inference Pipeline for Unified Audio Understanding and Generation

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