标题： NanoCodec：迈向高质量超快速语音大模型推理 显示英文标题

标题： NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference

摘要： 大型语言模型（LLMs）通过利用音频编解码器将音频离散化为标记，显著推进了音频处理，使得语言建模技术可以应用于语音数据。 然而，现有的音频编解码器通常以高帧率运行，导致训练和推理速度较慢，尤其是在自回归模型中。 为了解决这个问题，人们对低帧率音频编解码器的兴趣日益增长，这减少了生成一秒钟音频所需的自回归步骤数。 在本文中，我们进行了消融研究，以检查帧率、比特率和因果性对编解码器重建质量的影响。 基于我们的发现，我们引入了 NanoCodec，一种最先进的音频编解码器，在每秒12.5帧（FPS）的情况下实现了高质量的压缩。 NanoCodec在各种比特率范围内优于相关工作，为低延迟和高效的语音LLM训练和推理设立了新的基准。 显示英文摘要

摘要： Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling techniques to speech data. However, existing audio codecs often operate at high frame rates, leading to slow training and inference, particularly for autoregressive models. To address this, there is growing interest in low frame-rate audio codecs, which reduce the number of autoregressive steps required to generate one second of audio. In this paper, we conduct ablation studies to examine the impact of frame rate, bitrate, and causality on codec reconstruction quality. Based on our findings, we introduce NanoCodec, a state-of-the-art audio codec that achieves high-quality compression at just 12.5 frames per second (FPS). NanoCodec outperforms related works across various bitrate ranges, establishing a new benchmark for low-latency and efficient Speech LLM training and inference.