You can deploy this model and start earning money today!

facebook/
dacvae-watermarked

Name: dacvae-watermarked
Availability: InStock

apache-2.0

Views

No views yet

DACVAE

VAE version of the Descript Audio Codec, which has a continuous latent space. Descript Audio Codec (DAC) is a high fidelity general neural audio codec, introduced in the paper titled High-Fidelity Audio Compression with Improved RVQGAN. Most code is adopted from the open-source repo DAC

Installation

$ pip install git+https://github.com/facebookresearch/dacvae

Usage

python

from dacvae import DACVAE
import torchaudio


model = DACVAE.load("facebook/dacvae-watermarked")
wav, sample_rate = torchaudio.load("<path to audio file>")
# Resample to expected sample rate
resampled = torchaudio.functional.resample(wav, sample_rate, model.sample_rate)
# Convert stero to mono (if applicable)
resampled = resampled.mean(dim=0, keepdim=True)
# Expected shape is batch x 1 x samples
model_input = resampled.unsqueeze(0)
encoded = model.encode(model_input)
# `decoded` shape is `batch x 1 x samples`
decoded = model.decode(encoded)

Watermarking

The DAC-VAE decoder has been integrated with Audioseal to ensure all audios generated contain watermarks that are verifiable independently. We develop a new watermarking model with adapted architecture specifically for DAC-VAE to optimize the high-fidelity outcome. We also plan to release the detector API. Stay tuned !

Contributing

See contributing and code of conduct for more information.

License

This project is licensed under the SAM License - see the LICENSE file for details.