71 lines
2.3 KiB
Markdown
71 lines
2.3 KiB
Markdown
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---
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license: mit
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tags:
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- audio
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---
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# SNAC 🍿
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Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess audio into discrete codes at a low bitrate.
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👉 This model was primarily trained on speech data, and its recommended use case is speech synthesis. See below for other pretrained models.
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🔗 GitHub repository: https://github.com/hubertsiuzdak/snac/
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## Overview
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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,
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covering a broader time span.
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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
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47 Hz.
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## Pretrained models
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Currently, all models support only single audio channel (mono).
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| Model | Bitrate | Sample Rate | Params | Recommended use case |
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|-----------------------------------------------------------------------------|-----------|-------------|--------|--------------------------|
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| hubertsiuzdak/snac_24khz (this model) | 0.98 kbps | 24 kHz | 19.8 M | 🗣️ Speech |
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| [hubertsiuzdak/snac_32khz](https://huggingface.co/hubertsiuzdak/snac_32khz) | 1.9 kbps | 32 kHz | 54.5 M | 🎸 Music / Sound Effects |
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| [hubertsiuzdak/snac_44khz](https://huggingface.co/hubertsiuzdak/snac_44khz) | 2.6 kbps | 44 kHz | 54.5 M | 🎸 Music / Sound Effects |
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## Usage
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Install it using:
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```bash
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pip install snac
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```
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To encode (and decode) audio with SNAC in Python, use the following code:
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```python
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import torch
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from snac import SNAC
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model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()
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audio = torch.randn(1, 1, 24000).cuda() # B, 1, T
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with torch.inference_mode():
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codes = model.encode(audio)
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audio_hat = model.decode(codes)
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```
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You can also encode and reconstruct in a single call:
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```python
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with torch.inference_mode():
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audio_hat, codes = model(audio)
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```
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⚠️ Note that `codes` is a list of token sequences of variable lengths, each corresponding to a different temporal
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resolution.
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```
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>>> [code.shape[1] for code in codes]
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[12, 24, 48]
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```
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## Acknowledgements
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Module definitions are adapted from the [Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec).
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