Multi-Scale Neural Audio Codec (SNAC) compressess audio into discrete codes at a low bitrate.

👉 This model was primarily trained on speech data, and its recommended use case is speech synthesis. See below for other pretrained models.

🔗 GitHub repository: https://github.com/hubertsiuzdak/snac/

SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC. However, SNAC introduces a simple change where coarse tokens are sampled less frequently, covering a broader time span.

This model compresses 24 kHz audio into discrete codes at a 0.98 kbps bitrate. It uses 3 RVQ levels with token rates of 12, 23, and 47 Hz.

Currently, all models support only single audio channel (mono).

Install it using:

To encode (and decode) audio with SNAC in Python, use the following code:

You can also encode and reconstruct in a single call:

⚠️ Note that codes is a list of token sequences of variable lengths, each corresponding to a different temporal resolution.

>>> [code.shape[1] for code in codes]
[12, 24, 48]

Module definitions are adapted from the Descript Audio Codec.