Authors: June Young Yi, Dongwook Lee, Jiheum Yeom, Sungroh Yoon
Organizations: Department of Computer Science and Engineering, Seoul National University · 2Interdisciplinary Program in Artificial Intelligence, Seoul National University · Department of Electrical and Computer Engineering, Seoul National University · 4AIIS, ASRI, INMC, and ISRC, Seoul National University
Neural Audio Codecs are widely adopted in speech generation and editing. However, existing neural audio codecs are not idempotent: across the paper's twelve baseline systems, every configuration tested rewrites, on average, at least 15% of its tokens in a single decode-re-encode pass. This poses a problem for utilizing Neural Audio Codecs as token interfaces in pipelines where re-encoding decoded outputs can occur. We present LILAC, a fully convolutional 24 kHz speech codec at 9.375 Hz and 0.75 kbit/s that is codec idempotent by construction; re-encoding the decoded audio of any valid token stream returns the identical stream. LILAC achieves idempotency while maintaining competitive quality, reaching UTMOS 4.14 and 4.24 on LibriSpeech and LibriTTS-R test sets, comparable to SOTA sub-1 kbit/s Neural Audio Codecs.