@inproceedings{wu-etal-2025-towards,
    title = "Towards Codec-{LM} Co-design for Neural Codec Language Models",
    author = "Wu, Shih-Lun  and
      Lahoti, Aakash  and
      Desai, Arjun D  and
      Goel, Karan  and
      Donahue, Chris  and
      Gu, Albert",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-srw.6/",
    pages = "55--65",
    ISBN = "979-8-89176-192-6",
    abstract = "Neural codec language models (or codec LMs) are emerging as a powerful framework for audio generation tasks like text-to-speech (TTS). These models leverage advancements in language modeling and residual vector quantization (RVQ)-based audio codecs, which compress audios into discrete codes for LMs to process. Despite the close interdependence of codecs and LMs in these systems, research on codecs and LMs has largely remained siloed. In this work, we propose three techniques for better codec-LM co-design: (i) a frame-wise codec encoder that improves both LM log-likelihood and end-to-end TTS metrics, (ii) LM codebook level dropout, a method to efficiently navigate a portion of the codec-LM design space by training a single LM, and (iii) increased codec frame duration, which we show can accelerate inference while maintaining end-to-end performance. Our experiments demonstrate that combining all three co-design techniques results in doubled inference speed, and improvements in intelligibility, audio quality, and speaker control in TTS relative to a siloed baseline."
}