标题： 编解码器滑液：通过动态帧率的神经语音编解码器时间冗余压缩 显示英文标题

标题： CodecSlime: Temporal Redundancy Compression of Neural Speech Codec via Dynamic Frame Rate

摘要： 神经语音编解码器已被广泛用于音频压缩和各种下游任务。 当前主流的编解码器是固定帧率（FFR）的，它们为每个等长的片段分配相同数量的标记。 然而，语音在时间信息密度上本质上是不均匀的。 因此，许多标记被浪费在稳定状态段落上，比如长元音和静音。 为了解决这种不匹配，我们提出了CodecSlime，这是一种通过在神经语音编解码器上支持动态帧率（DFR）首次实现压缩时间冗余的插件式方法。 我们的方法是无监督的且与架构无关的，结合了两个关键创新，ScheDFR和Melt-and-Cool，分别用于适应推理和训练。 当集成到典型的VQ-GAN编解码器主干中并在40 Hz DFR（$\approx$600 bps）下运行时，CodecSlime的重建WER相对于具有相同模型架构和类似比特率的传统FFR基线最多减少了46%，而其他指标也具有竞争力。 CodecSlime还能够在重建质量和比特率之间实现灵活的权衡：一个模型支持在多个帧率下进行推理，并且在相应的帧率下始终优于FFR模型。 音频样本可在https://acadarmeria.github.io/codecslime/获取。 显示英文摘要

摘要： Neural speech codecs have been widely used in audio compression and various downstream tasks. Current mainstream codecs are fixed-frame-rate (FFR), which allocate the same number of tokens to every equal-duration slice. However, speech is inherently non-uniform in temporal information density. As a result, many tokens are wasted on steady-state segments like long vowels and silences. To address this mismatch, we present CodecSlime, a plugin-style method for compressing temporal redundancy through supporting dynamic frame rate (DFR) on neural speech codecs for the first time. Our method is unsupervised and architecture-agnostic, combining two key innovations, ScheDFR and Melt-and-Cool, for adapting inference and training, respectively. When integrated into a typical VQ-GAN codec backbone and operating at 40 Hz DFR ($\approx$ 600 bps), the reconstruction WER of CodecSlime is reduced by up to 46% relative to conventional FFR baselines with the same model architecture and similar bitrates, while other metrics are also competitive. CodecSlime also enables flexible trade-offs between reconstruction quality and bitrate: a single model supports inference at multiple frame rates and consistently outperforms FFR models at the corresponding frame rates. Audio samples are available at https://acadarmeria.github.io/codecslime/.