fyremael / descript-audio-codec

State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.

Descript Audio Codec (.dac): High-Fidelity Audio Compression with Improved RVQGAN

This repository contains training and inference scripts for the Descript Audio Codec (.dac), a high fidelity general neural audio codec, introduced in the paper titled High-Fidelity Audio Compression with Improved RVQGAN.

arXiv Paper: High-Fidelity Audio Compression with Improved RVQGAN
📈 Demo Site
⚙ Model Weights

👉 With Descript Audio Codec, you can compress 44.1 KHz audio into discrete codes at a low 8 kbps bitrate.
🤌 That's approximately 90x compression while maintaining exceptional fidelity and minimizing artifacts.
💪 Our universal model works on all domains (speech, environment, music, etc.), making it widely applicable to generative modeling of all audio.
👌 It can be used as a drop-in replacement for EnCodec for all audio language modeling applications (such as AudioLMs, MusicLMs, MusicGen, etc.)

Usage

Installation

pip install descript-audio-codec

OR

pip install git+https://github.com/descriptinc/descript-audio-codec

Weights

Weights are released as part of this repo under MIT license. We release weights for models that can natively support 16 kHz, 24kHz, and 44.1kHz sampling rates. Weights are automatically downloaded when you first run encode or decode command. You can cache them using one of the following commands

python3 -m dac download # downloads the default 44kHz variant
python3 -m dac download --model_type 44khz # downloads the 44kHz variant
python3 -m dac download --model_type 24khz # downloads the 24kHz variant
python3 -m dac download --model_type 16khz # downloads the 16kHz variant

We provide a Dockerfile that installs all required dependencies for encoding and decoding. The build process caches the default model weights inside the image. This allows the image to be used without an internet connection. Please refer to instructions below.

Compress audio

python3 -m dac encode /path/to/input --output /path/to/output/codes

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

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

import dac
from audiotools import AudioSignal

# Download a model
model_path = dac.utils.download(model_type="44khz")
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')

Docker image

We provide a dockerfile to build a docker image with all the necessary dependencies.

1. Building the image.

   docker build -t dac .
   

2. Using the image.

   Usage on CPU:

   docker run dac <command>
   

   Usage on GPU:

   docker run --gpus=all dac <command>
   

   <command> can be one of the compression and reconstruction commands listed above. For example, if you want to run compression,

   docker run --gpus=all dac python3 -m dac encode ...
   

Training

The baseline model configuration can be trained using the following commands.

Pre-requisites

Please install the correct dependencies

pip install -e ".[dev]"

Environment setup

We have provided a Dockerfile and docker compose setup that makes running experiments easy.

To build the docker image do:

docker compose build

Then, to launch a container, do:

docker compose run -p 8888:8888 -p 6006:6006 dev

The port arguments (-p) are optional, but useful if you want to launch a Jupyter and Tensorboard instances within the container. The default password for Jupyter is password, and the current directory is mounted to /u/home/src, which also becomes the working directory.

Then, run your training command.

Single GPU training

export CUDA_VISIBLE_DEVICES=0
python scripts/train.py --args.load conf/ablations/baseline.yml --save_path runs/baseline/

Multi GPU training

export CUDA_VISIBLE_DEVICES=0,1
torchrun --nproc_per_node gpu scripts/train.py --args.load conf/ablations/baseline.yml --save_path runs/baseline/

Testing

We provide two test scripts to test CLI + training functionality. Please make sure that the trainig pre-requisites are satisfied before launching these tests. To launch these tests please run

python -m pytest tests

Results