audiobox-aesthetics 0.0.1

Unified automatic quality assessment for speech, music, and sound.

audiobox-aesthetics

Unified automatic quality assessment for speech, music, and sound.

Read our paper here.

Installation

This repository requires Python 3.9 and Pytorch 2.2 or greater. To install, you can clone this repo and run:

pip install -e .

Pre-trained Models

Model	Link
All axes	checkpoint.pt

Usage

How to run prediction:

1. Create a jsonl files with the following format

   {"path":"/path/to/a.wav"}
   {"path":"/path/to/b.wav"}
   ...
   {"path":"/path/to/z.wav"}
   

   or if you only want to predict aesthetic score from certain timestamp

   {"path":"/path/to/a.wav", "start_time":0, "end_time": 5}
   {"path":"/path/to/b.wav", "start_time":3, "end_time": 10}
   

   and save it as input.jsonl

2. Run following command

   audio-aes input.jsonl --ckpt "/path/to/checkpoint.pt" > output.jsonl
   

   If path for ckpt didn't exist or you haven't download the checkpoint, the script will try to download it automatically.

3. Output file will contains same number of rows as input.jsonl. Each rows contains 4 axes prediction with JSON-formatted dictionary. Check following table for more info:

   Axes name	Full name
   CE	Content Enjoyment
   CU	Content Usefulness
   PC	Production Complexity
   PQ	Production Quality

   Output line example:

   {"CE": 5.146, "CU": 5.779, "PC": 2.148, "PQ": 7.220}
   

4. (Extra) If you want to extract only one axis (i.e. CE), post-process the output file with following command using jq utility:

   jq '.CE' output.jsonl > output-aes_ce.txt

Evaluation dataset

We released our evaluation dataset consisted of 4 axes of aesthetic annotation scores.

Here, we show an example on how to read and re-map each annotation to the actual audio file.

{
    "data_path": "/your_path/LibriTTS/train-clean-100/1363/139304/1363_139304_000011_000000.wav", 
    "Production_Quality": [8.0, 8.0, 8.0, 8.0, 8.0, 9.0, 8.0, 5.0, 8.0, 8.0], 
    "Production_Complexity": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 
    "Content_Enjoyment": [8.0, 6.0, 8.0, 5.0, 8.0, 8.0, 8.0, 6.0, 8.0, 6.0], 
    "Content_Usefulness": [8.0, 6.0, 8.0, 7.0, 8.0, 9.0, 8.0, 6.0, 10.0, 7.0]
}

1. Recognize the dataset name from data_path. In the example, it is LibriTTS.
2. Replace "/your_path/" into your downloaded LibriTTS directory.
3. Each axes contains 10 scores annotated by 10 different human annotators.

data_path	URL
LibriTTS	https://openslr.org/60/
cv-corpus-13.0-2023-03-09	https://commonvoice.mozilla.org/en/datasets
EARS	https://sp-uhh.github.io/ears_dataset/
MUSDB18	https://sigsep.github.io/datasets/musdb.html
musiccaps	https://www.kaggle.com/datasets/googleai/musiccaps
(audioset) unbalanced_train_segments	https://research.google.com/audioset/dataset/index.html
PAM	https://zenodo.org/records/10737388

License

The majority of audiobox-aesthetics is licensed under CC-BY 4.0, as found in the LICENSE file. However, portions of the project are available under separate license terms: https://github.com/microsoft/unilm is licensed under MIT license.

Citation

If you found this repository useful, please use the following BibTeX entry. (will include arXiv link soon)

@article{tjandra2025aes,
    title={Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound},
    author={Tjandra, Andros and Wu, Yi-Chiao and Guo, Baishan and Hoffman, John and Ellis, Brian and Vyas, Apoorv and Shi, Bowen and Chen, Sanyuan and Le, Matt and Zacharov, Nick and Wood, Carleigh and Lee, Ann and Hsu, Wei-ning},
    publisher={Meta AI},
    year={2025},
    url={https://ai.meta.com/research/publications/meta-audiobox-aesthetics-unified-automatic-quality-assessment-for-speech-music-and-sound/}
}

Acknowledgements

Part of model code are copied from https://github.com/microsoft/unilm/tree/master/wavlm.