Codec-SUPERB: Sound Codec Speech Processing Universal Performance Benchmark
Codec-SUPERB is an innovative benchmark designed for assessing the effectiveness of audio codec models in performing various speech-related tasks. By facilitating collaboration within the community, this project aims to propel advancements in the field of speech processing while maintaining high-quality audio information.
Introduction
Codec-SUPERB establishes a cutting-edge standard for the evaluation of sound codecs. It offers a robust and transparent framework for performance measurement across diverse speech processing tasks, with the aim of promoting innovation and setting new quality benchmarks in audio processing.
Key Features
Out-of-the-Box Codec Interface
The project supplies a user-friendly codec interface ready for immediate use, streamlining the process of integrating and testing different codec models. This feature supports rapid experimentation and iteration.
Multi-Perspective Leaderboard
Codec-SUPERB introduces a unique evaluation approach through a multi-perspective leaderboard that fosters innovation in sound codec research. This tool provides comprehensive assessments and ensures competitive transparency among developers.
Standardized Environment
To ensure fair and consistent comparisons, Codec-SUPERB offers a standardized testing environment. This uniformity enhances the reliability and universal interpretability of benchmark results.
Unified Datasets
The project includes a collection of unified datasets tailored to test a broad spectrum of speech processing scenarios. These datasets help evaluate models under diverse conditions, closely resembling real-world applications.
Installation
To get started with Codec-SUPERB, users can clone the repository and install the necessary dependencies using the following commands:
git clone https://github.com/voidful/Codec-SUPERB.git
cd Codec-SUPERB
pip install -r requirements.txt
Usage
Leaderboard
Codec-SUPERB features an online leaderboard that users can access to track model performance and compare results.
Codec Interface in Action
An example usage is provided below, demonstrating how to list available codecs, load a codec, process audio data, and synthesize sound:
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']
Contribution
Codec-SUPERB welcomes contributions from the community, whether it involves adding new codec models, increasing the dataset collection, or enhancing the benchmark framework. Detailed guidelines for contributions can be found in the CONTRIBUTING.md file.
License
Codec-SUPERB operates under the MIT License, with more detailed information available in the LICENSE file.
Reference Sound Codec Repositories
The project references several key sound codec repositories that serve as resources and inspiration for further developments:
In summary, Codec-SUPERB is poised to play a significant role in the advancement of audio codec models, offering a comprehensive toolkit for research and development in speech processing.