# DAC-JAX: A JAX Implementation of the Descript Audio Codec

arXiv: https://arxiv.org/abs/2405.11554 · HTML: https://mlsanity.com/paper/2405.11554/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2024.

## Built on by (most cited first, 1 of 1)

- [JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attention](https://mlsanity.com/paper/2512.07168/) (2025)

## Abstract (arXiv)

We present an open-source implementation of the Descript Audio Codec (DAC) using Google's JAX ecosystem of Flax, Optax, Orbax, AUX, and CLU. Our codebase enables the reuse of model weights from the original PyTorch DAC, and we confirm that the two implementations produce equivalent token sequences and decoded audio if given the same input. We provide a training and fine-tuning script which supports device parallelism, although we have only verified it using brief training runs with a small dataset. Even with limited GPU memory, the original DAC can compress or decompress a long audio file by processing it as a sequence of overlapping "chunks." We implement this feature in JAX and benchmark the performance on two types of GPUs. On a consumer-grade GPU, DAC-JAX outperforms the original DAC for compression and decompression at all chunk sizes. However, on a high-performance, cluster-based GPU, DAC-JAX outperforms the original DAC for small chunk sizes but performs worse for large chunks.