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https://github.com/descriptinc/descript-audio-codec

State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.
https://github.com/descriptinc/descript-audio-codec

audio audio-compression codec compression-algorithm deep-learning gans generative-adversarial-network pytorch residual-vector-quantization

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

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README 

        
# 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**.



![](https://static.arxiv.org/static/browse/0.3.4/images/icons/favicon-16x16.png) [arXiv Paper: High-Fidelity Audio Compression with Improved RVQGAN

](http://arxiv.org/abs/2306.06546) 


📈 [Demo Site](https://descript.notion.site/Descript-Audio-Codec-11389fce0ce2419891d6591a68f814d5)


âš™ [Model Weights](https://github.com/descriptinc/descript-audio-codec/releases/download/0.0.1/weights.pth)



👉 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.) 





Comparison of compressions approaches. Our model achieves a higher compression factor compared to all baseline methods. Our model has a ~90x compression factor compared to 32x compression factor of EnCodec and 64x of SoundStream. Note that we operate at a target bitrate of 8 kbps, whereas EnCodec operates at 24 kbps and SoundStream at 6 kbps. We also operate at 44.1 kHz, whereas EnCodec operates at 48 kHz and SoundStream operates at 24 kHz.



## 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

```bash

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.](#docker-image)



### 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

```py

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 

    ```



    Usage on GPU:

    ```

    docker run --gpus=all dac 

    ```



    `` 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