Codec-SUPERB: Sound Codec Speech Processing Universal Performance Benchmark

Overview

Codec-SUPERB is a comprehensive benchmark designed to evaluate audio codec models across a variety of speech tasks. Our goal is to facilitate community collaboration and accelerate advancements in the field of speech processing by preserving and enhancing speech information quality.

Introduction
Key Features
Installation
Usage
Contribution
License

Introduction

Codec-SUPERB sets a new benchmark in evaluating sound codec models, providing a rigorous and transparent framework for assessing performance across a range of speech processing tasks. Our goal is to foster innovation and set new standards in audio quality and processing efficiency.

Key Features

Out-of-the-Box Codec Interface

Codec-SUPERB offers an intuitive, out-of-the-box codec interface that allows for easy integration and testing of various codec models, facilitating quick iterations and experiments.

Multi-Perspective Leaderboard

Codec-SUPERB's unique blend of multi-perspective evaluation and an online leaderboard drives innovation in sound codec research by providing a comprehensive assessment and fostering competitive transparency among developers.

Standardized Environment

We ensure a standardized testing environment to guarantee fair and consistent comparison across all models. This uniformity brings reliability to benchmark results, making them universally interpretable.

Unified Datasets

We provide a collection of unified datasets, curated to test a wide range of speech processing scenarios. This ensures that models are evaluated under diverse conditions, reflecting real-world applications.

Installation

git clone https://github.com/voidful/Codec-SUPERB.git
cd Codec-SUPERB
pip install -r requirements.txt

Usage

Leaderboard

Out of the Box Codec Interface

from SoundCodec import codec
import torchaudio

# get all available codec
print(codec.list_codec())
# load codec by name, use encodec as example
encodec_24k_6bps = codec.load_codec('encodec_24k_6bps')

# load audio
waveform, sample_rate = torchaudio.load('sample audio')
resampled_waveform = waveform.numpy()[-1]
data_item = {'audio': {'array': resampled_waveform,
                       'sampling_rate': sample_rate}}

# extract unit
sound_unit = encodec_24k_6bps.extract_unit(data_item).unit

# sound synthesis
decoded_waveform = encodec_24k_6bps.synth(sound_unit, local_save=False)['audio']['array']

Citation

If you use this code or result in your paper, please cite our work as:

@article{wu2024codec,
  title={Codec-superb: An in-depth analysis of sound codec models},
  author={Wu, Haibin and Chung, Ho-Lam and Lin, Yi-Cheng and Wu, Yuan-Kuei and Chen, Xuanjun and Pai, Yu-Chi and Wang, Hsiu-Hsuan and Chang, Kai-Wei and Liu, Alexander H and Lee, Hung-yi},
  journal={arXiv preprint arXiv:2402.13071},
  year={2024}
}

@article{wu2024towards,
  title={Towards audio language modeling-an overview},
  author={Wu, Haibin and Chen, Xuanjun and Lin, Yi-Cheng and Chang, Kai-wei and Chung, Ho-Lam and Liu, Alexander H and Lee, Hung-yi},
  journal={arXiv preprint arXiv:2402.13236},
  year={2024}
}

@article{wu2024codec,
  title={Codec-SUPERB@ SLT 2024: A lightweight benchmark for neural audio codec models},
  author={Wu, Haibin and Chen, Xuanjun and Lin, Yi-Cheng and Chang, Kaiwei and Du, Jiawei and Lu, Ke-Han and Liu, Alexander H and Chung, Ho-Lam and Wu, Yuan-Kuei and Yang, Dongchao and others},
  journal={arXiv preprint arXiv:2409.14085},
  year={2024}
}

Contribution

Contributions are highly encouraged, whether it's through adding new codec models, expanding the dataset collection, or enhancing the benchmarking framework. Please see CONTRIBUTING.md for more details.

License

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

voidful / Codec-SUPERB

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