FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
Authors: name: Luca Della Libera, name: Francesco Paissan, name: Cem Subakan, name: Mirco Ravanelli

Large language models have revolutionized natural language processing through
self-supervised pretraining on massive datasets. Inspired by this success,
researchers have explored adapting these methods to speech by discretizing
continuous audio into tokens using neural audio codecs. However, existing
approaches face limitations, including high bitrates, the loss of either
semantic or acoustic information, and the reliance on multi-codebook designs
when trying to capture both, which increases architectural complexity for
downstream tasks. To address these challenges, we introduce FocalCodec, an
efficient low-bitrate codec based on focal modulation that utilizes a single
binary codebook to compress speech between 0.16 and 0.65 kbps. FocalCodec
delivers competitive performance in speech resynthesis and voice conversion at
lower bitrates than the current state-of-the-art, while effectively handling
multilingual speech and noisy environments. Evaluation on downstream tasks
shows that FocalCodec successfully preserves sufficient semantic and acoustic
information, while also being well-suited for generative modeling. Demo
samples, code and checkpoints are available at
https://lucadellalib.github.io/focalcodec-web/.