Variable Bitrate Residual Vector Quantization for Audio Coding

This repository contains official implementation of the paper Variable Bitrate Residual Vector Quantization for Audio Coding:

• Accepted at NeurIPS 2024 Machine Learning and Compression Workshop
• Accepted at ICASSP 2025

This repository is not complete. Functions, features, or scripts are not fully implemented. I will complete it as soon as possible.

🔊 Audio Samples:

Importance map and audio samples are available at: Link

📌 Code Base

Our work is based on DAC [1], and our experiments were conducted using its framework.
Thus, this code is also built upon the DAC repository.

• DAC GitHub: DAC GitHub Link

⚙️ Environment Setup

To set up the environment, follow these steps:

# Create a conda environment
conda create -n vrvq python=3.9

# Activate the environment
conda activate vrvq

# Install dependencies
conda install pytorch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 pytorch-cuda=11.8 -c pytorch -c nvidia  ## We used this command for PyTorch install
pip install -r requirements.txt

Training example

# ex) bash scripts/script_train.sh $EXP_PATH $GPU

# Single GPU
bash scripts/scripts_train.sh vrvq/vrvq_a2 0

# Multi GPU
bash scripts/scripts_train.sh vrvq/vrvq_a2 0,1