README.md

---
license: cdla-permissive-2.0
---

## Model Summary
[DAC auto-encoder models](https://github.com/descriptinc/descript-audio-codec) provide compact discrete tokenization of speech and audio signals that facilitate signal generation by cascaded generative AI models (e.g. multi-modal generative AI models) and high-quality reconstruction of the original signals. [The current models](https://www.isca-archive.org/interspeech_2024/shechtman24_interspeech.pdf) improve upon the [original DAC models](https://github.com/descriptinc/descript-audio-codec) by allowing a more compact representation for speech-only signals with high-quality signal reconstruction.

## Usage
follow [DAC](https://github.com/descriptinc/descript-audio-codec) installation instructions
download the model weights from the current repo (e.g., *weights_24khz_1.5kbps_v1.0*)
### Compress audio
```
python3 -m dac encode /path/to/input --output /path/to/output/codes --weights_path /path/to/weights_24khz_1.5kbps_v1.0
```

This command will create `.dac` files with the same name as the input files. It will also preserve the directory structure relative to input root and re-create it in the output directory. Please use `python -m dac encode --help` for more options.

### Reconstruct audio from compressed codes
```
python3 -m dac decode /path/to/output/codes --output /path/to/reconstructed_input --weights_path /path/to/weights_24khz_1.5kbps_v1.0
```

This command will create `.wav` files with the same name as the input files. It will also preserve the directory structure relative to input root and re-create it in the output directory. Please use `python -m dac decode --help` for more options.

### Programmatic Usage
```py
import dac
from audiotools import AudioSignal

# Download a model
model_path = /path/to/weights_24khz_1.5kbps_v1.0
model = dac.DAC.load(model_path)

model.to('cuda')

# Load audio signal file
signal = AudioSignal('input.wav')

# Encode audio signal as one long file
# (may run out of GPU memory on long files)
signal.to(model.device)

x = model.preprocess(signal.audio_data, signal.sample_rate)
z, codes, latents, _, _ = model.encode(x)

# Decode audio signal
y = model.decode(z)

# Alternatively, use the `compress` and `decompress` functions
# to compress long files.

signal = signal.cpu()
x = model.compress(signal)

# Save and load to and from disk
x.save("compressed.dac")
x = dac.DACFile.load("compressed.dac")

# Decompress it back to an AudioSignal
y = model.decompress(x)

# Write to file
y.write('output.wav')
```