---
license: mit
tags:
- DAC
- Descript Audio Codec
- PyTorch
---

# Descript Audio Codec (DAC)
DAC is the state-of-the-art audio tokenizer with improvement upon the previous tokenizers like SoundStream and EnCodec.

This model card provides an easy-to-use API for a *pretrained DAC* [1] for 16khz audio whose backbone and pretrained weights are from [its original reposotiry](https://github.com/descriptinc/descript-audio-codec). With this API, you can encode and decode by a single line of code either using CPU or GPU. Furhtermore, it supports chunk-based processing for memory-efficient processing, especially important for GPU processing. 









### Model variations
There are three types of model depending on an input audio sampling rate. 

| Model | Input audio sampling rate [khz] |
| ------------------ | ----------------- |
| [`hance-ai/descript-audio-codec-44khz`](https://huggingface.co/hance-ai/descript-audio-codec-44khz) | 44.1khz |
| [`hance-ai/descript-audio-codec-24khz`](https://huggingface.co/hance-ai/descript-audio-codec-24khz) | 24khz |
| [`hance-ai/descript-audio-codec-16khz`](https://huggingface.co/hance-ai/descript-audio-codec-16khz) | 16khz |




# Dependency
See `requirements.txt`.





# Usage

### Load
```python
from transformers import AutoModel

# device setting
device = 'cpu'  # or 'cuda:0'

# load
model = AutoModel.from_pretrained('hance-ai/descript-audio-codec-16khz', trust_remote_code=True)
model.to(device)
```

### Encode
```python
audio_filename = 'path/example_audio.wav'
zq, s = model.encode(audio_filename)
```
`zq` is discrete embeddings with dimension of (1, num_RVQ_codebooks, token_length) and `s` is a token sequence with dimension of (1, num_RVQ_codebooks, token_length). 


### Decode
```python
# decoding from `zq`
waveform = model.decode(zq=zq)  # (1, 1, audio_length); the output has a mono channel.

# decoding from `s`
waveform = model.decode(s=s)  # (1, 1, audio_length); the output has a mono channel.
```

### Save a waveform as an audio file
```python
model.waveform_to_audiofile(waveform, 'out.wav')
```

### Save and load tokens
```python
model.save_tensor(s, 'tokens.pt')
loaded_s = model.load_tensor('tokens.pt')
```










# References
[1] Kumar, Rithesh, et al. "High-fidelity audio compression with improved rvqgan." Advances in Neural Information Processing Systems 36 (2024).



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- chunk processing 
- add device parameter in the test notebook
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