Benchmarking Language Modeling for Lossless Compression of Full-Fidelity Audio
Autoregressive "language" models (LMs) trained on raw waveforms can be repurposed for lossless audio compression, but prior work is limited to 8-bit audio, leaving open whether such approaches work for practical settings (16/24-bit) and can compete with existing codecs. We benchmark LM-based compression on full-fidelity audio across diverse domains (music, speech, bioacoustics), sampling rates (16kHz-48kHz), and bit depths (8, 16, 24-bit). Standard sample-level tokenization becomes intractable at higher bit depths due to vocabulary size (65K for 16-bit; 16.7M for 24-bit). We propose Trilobyte, a byte-level tokenization schema for full resolution audio, improving vocabulary scaling from $O...
| Authors | Phillip Long, Zachary Novack, Chris Donahue |
|---|---|
| Primary category | cs.SD |
| Published | 9 March 2026 |
| Last updated at source | 5 June 2026 |
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