Duplicate from hubertsiuzdak/snac_32khz

Co-authored-by: Hubert Siuzdak <hubertsiuzdak@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+tags:
+- audio
+---
+
+# SNAC 🍿
+
+Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess audio into discrete codes at a low bitrate.
+
+👉 This model was primarily trained on music data, and its recommended use case is music (and SFX) generation. See below for other pretrained models.
+
+🔗 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 model compresses 32 kHz audio into discrete codes at a 1.9 kbps bitrate. It uses 4 RVQ levels with token rates of 10, 21, 42, and
+83 Hz.
+
+## Pretrained models
+
+Currently, all models support only single audio channel (mono).
+
+| Model                                                                       | Bitrate   | Sample Rate | Params | Recommended use case     | 
+|-----------------------------------------------------------------------------|-----------|-------------|--------|--------------------------|
+| [hubertsiuzdak/snac_24khz](https://huggingface.co/hubertsiuzdak/snac_24khz) | 0.98 kbps | 24 kHz      | 19.8 M | 🗣️ Speech               |  
+| hubertsiuzdak/snac_32khz (this model)                                       | 1.9 kbps  | 32 kHz      | 54.5 M | 🎸 Music / Sound Effects | 
+| [hubertsiuzdak/snac_44khz](https://huggingface.co/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.from_pretrained("hubertsiuzdak/snac_32khz").eval().cuda()
+audio = torch.randn(1, 1, 32000).cuda()  # B, 1, T
+
+with torch.inference_mode():
+    codes = model.encode(audio)
+    audio_hat = 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](https://github.com/descriptinc/descript-audio-codec).

config.json

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+{
+    "sampling_rate": 32000,
+    "encoder_dim": 64,
+    "encoder_rates": [2, 3, 8, 8],
+    "decoder_dim": 1536,
+    "decoder_rates": [8, 8, 3, 2],
+    "attn_window_size": 32,
+    "codebook_size": 4096,
+    "codebook_dim": 8,
+    "vq_strides": [8, 4, 2, 1],
+    "noise": true,
+    "depthwise": true
+}

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