SNAC 🍿

Multi-Scale Neural Audio Codec (SNAC) compresses audio into discrete codes at a low bitrate. For more information, read the paper: https://arxiv.org/abs/2410.14411

| 🎸 Music samples | 🗣️ Speech samples | |----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------| |

🎧 More audio samples available at https://hubertsiuzdak.github.io/snac/

Overview

SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC (see the image on the left). However, SNAC introduces a simple change where coarse tokens are sampled less frequently, covering a broader time span (see the image on the right).

This can not only save on bitrate, but more importantly this might be very useful for language modeling approaches to audio generation. E.g. with coarse tokens of ~10 Hz and a context window of 2048 you can effectively model a consistent structure of an audio track for ~3 minutes.

Pretrained models

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

| Model | Bitrate | Sample Rate | Params | Recommended use case | |-----------------------------------------------------------------------------|-----------|-------------|--------|--------------------------| | hubertsiuzdak/snac_24khz | 0.98 kbps | 24 kHz | 19.8 M | 🗣️ Speech | | hubertsiuzdak/snac_32khz | 1.9 kbps | 32 kHz | 54.5 M | 🎸 Music / Sound Effects | | hubertsiuzdak/snac_44khz | 2.6 kbps | 44 kHz | 54.5 M | 🎸 Music / Sound Effects |

Usage

Install it using:

bash pip install snac

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

```python import torch from snac import SNAC

model = SNAC.frompretrained("hubertsiuzdak/snac32khz").eval().cuda() audio = torch.randn(1, 1, 32000).cuda() # placeholder for actual audio with shape (B, 1, T)

with torch.inferencemode(): codes = model.encode(audio) audiohat = model.decode(codes) ```

You can also encode and reconstruct in a single call:

python with torch.inference_mode(): audio_hat, codes = model(audio)

⚠️ 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, 96] ```

Acknowledgements

Module definitions are adapted from the Descript Audio Codec.

Citation

If this code contributes to your research, please cite our work:

@inproceedings{siuzdak2024snac, title={SNAC: Multi-Scale Neural Audio Codec}, author={Siuzdak, Hubert and Gr{\"o}tschla, Florian and Lanzend{\"o}rfer, Luca A}, booktitle={Audio Imagination: NeurIPS 2024 Workshop AI-Driven Speech, Music, and Sound Generation}, year={2024} }