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README.md

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+# [WIP] SNAC 🍿
+
+Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess 44.1 kHz audio into discrete codes at a low bitrate.
+
+See GitHub repository: https://github.com/hubertsiuzdak/snac/
+
+## Overview
+
+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 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.
+
+## Usage
+
+Install it using:
+
+```bash
+pip install snac
+```
+
+A pretrained model that compresses audio into discrete codes at a 2.2 kbps bitrate is available
+at [Hugging Face](https://huggingface.co/hubertsiuzdak/snac). It uses 4 RVQ levels with token rates of 12.5, 25, 50, and
+100 Hz.
+
+To encode (and reconstruct) audio with SNAC in Python, use the following code:
+
+```python
+import torch
+from snac import SNAC
+
+model = SNAC.from_pretrained("hubertsiuzdak/snac").eval().cuda()
+audio = torch.randn(1, 1, 44100).cuda()  # B, 1, T
+
+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]
+[13, 26, 52, 104]
+```
+
+## Acknowledgements
+
+Module definitions are adapted from the [Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec).