|
|
--- |
|
|
license: cc-by-nc-sa-4.0 |
|
|
task_categories: |
|
|
- text-to-audio |
|
|
- text-to-speech |
|
|
language: |
|
|
- zh |
|
|
- en |
|
|
- fr |
|
|
- ja |
|
|
- ko |
|
|
- es |
|
|
- de |
|
|
- ru |
|
|
- it |
|
|
tags: |
|
|
- singing |
|
|
- audio |
|
|
- croissant |
|
|
pretty_name: a |
|
|
size_categories: |
|
|
- 1B<n<10B |
|
|
configs: |
|
|
- config_name: meta |
|
|
data_files: processed/All/metadata.json |
|
|
--- |
|
|
|
|
|
# GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks |
|
|
|
|
|
#### Yu Zhang*, Changhao Pan*, Wenxiang Guo*, Ruiqi Li, Zhiyuan Zhu, Jialei Wang, Wenhao Xu, Jingyu Lu, Zhiqing Hong, Chuxin Wang, LiChao Zhang, Jinzheng He, Ziyue Jiang, Yuxin Chen, Chen Yang, Jiecheng Zhou, Xinyu Cheng, Zhou Zhao | Zhejiang University |
|
|
|
|
|
Dataset of [GTSinger (NeurIPS 2024 Spotlight)](https://arxiv.org/abs/2409.13832): A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks. |
|
|
|
|
|
[](https://arxiv.org/abs/2409.13832) |
|
|
[](https://github.com/AaronZ345/GTSinger) |
|
|
[](https://mp.weixin.qq.com/s/B1Iqr-24l57f0MslzYEslA) |
|
|
[](https://mp.weixin.qq.com/s/6RLdUzJM5PItklKUTTNz2w) |
|
|
[](https://zhuanlan.zhihu.com/p/993933492) |
|
|
[](https://drive.google.com/drive/folders/1xcdvCxNAEEfJElt7sEP-xT8dMKxn1_Lz?usp=drive_link) |
|
|
|
|
|
We introduce GTSinger, a large Global, multi-Technique, free-to-use, high-quality singing corpus with realistic music scores, designed for all singing tasks, along with its benchmarks. |
|
|
|
|
|
We provide the **full corpus for free** in this repository. |
|
|
|
|
|
And `metadata.json` and `phone_set.json` are also offered for each language in `processed`. **Note: you should change the wav_fn for each segment to your own absolute path! And you can use metadata of multiple languages by concat their data! We will provide the metadata for other languages soon!** |
|
|
|
|
|
Besides, we also provide our dataset on [Google Drive](https://drive.google.com/drive/folders/1xcdvCxNAEEfJElt7sEP-xT8dMKxn1_Lz?usp=drive_link). |
|
|
|
|
|
Moreover, you can visit our [Demo Page](https://aaronz345.github.io/GTSingerDemo) for the audio samples of our dataset as well as the results of our benchmarks. |
|
|
|
|
|
## Updates |
|
|
|
|
|
- 2025.02: We released all processed data of GTSinger and refined 7/9 languages! |
|
|
- 2024.10: We refine the paired speech data of each language! |
|
|
- 2024.10: We released the processed data of Chinese, English, Spanish, German, Russian! |
|
|
- 2024.09: We released the full dataset of GTSinger! |
|
|
- 2024.09: GTSinger is accepted by NeurIPS 2024 (Spotlight)! |
|
|
|
|
|
## Key Features |
|
|
|
|
|
- **80.59 hours of singing voices** in GTSinger are recorded in professional studios by skilled singers, ensuring **high quality and clarity**, forming the largest recorded singing dataset. |
|
|
- Contributed by **20 singers** across **nine widely spoken languages** (Chinese, English, Japanese, Korean, Russian, Spanish, French, German, and Italian) and all four vocal ranges, GTSinger enables zero-shot SVS and style transfer models to learn diverse timbres and styles. |
|
|
- GTSinger provides **controlled comparison** and **phoneme-level annotations** of **six singing techniques** (mixed voice, falsetto, breathy, pharyngeal, vibrato, and glissando) for songs, thereby facilitating singing technique modeling, recognition, and control. |
|
|
- Unlike fine-grained music scores, GTSinger features **realistic music scores** with regular note duration, assisting singing models in learning and adapting to real-world musical composition. |
|
|
- The dataset includes **manual phoneme-to-audio alignments, global style labels** (singing method, emotion, range, and pace), and **16.16 hours of paired speech**, ensuring comprehensive annotations and broad task suitability. |
|
|
|
|
|
## Dataset |
|
|
|
|
|
### Where to download |
|
|
|
|
|
Through this repo you can access our **full dataset** (audio along with TextGrid, json, musicxml) and **processed data** (metadata.json, phone_set.json, spker_set.json) on Hugging Face **for free**! Hope our data is helpful for your research. |
|
|
|
|
|
Besides, we also provide our dataset on [](https://drive.google.com/drive/folders/1xcdvCxNAEEfJElt7sEP-xT8dMKxn1_Lz?usp=drive_link). |
|
|
|
|
|
**Please note that, if you are using GTSinger, it means that you have accepted the terms of [license](./dataset_license.md).** |
|
|
|
|
|
### Data Architecture |
|
|
|
|
|
Our dataset is organized hierarchically. |
|
|
|
|
|
It presents nine top-level folders, each corresponding to a distinct language. |
|
|
|
|
|
Within each language folder, there are five sub-folders, each representing a specific singing technique. |
|
|
|
|
|
These technique folders contain numerous song entries, with each song further divided into several controlled comparison groups: a control group (natural singing without the specific technique), and a technique group (densely employing the specific technique). |
|
|
|
|
|
Our singing voices and speech are recorded at a 48kHz sampling rate with 24-bit resolution in WAV format. |
|
|
|
|
|
Alignments and annotations are provided in TextGrid files, including word boundaries, phoneme boundaries, phoneme-level annotations for six techniques, and global style labels (singing method, emotion, pace, and range). |
|
|
|
|
|
We also provide realistic music scores in musicxml format. |
|
|
|
|
|
Notably, we provide an additional JSON file for each singing voice, facilitating data parsing and processing for singing models. |
|
|
|
|
|
Here is the data structure of our dataset: |
|
|
|
|
|
``` |
|
|
. |
|
|
├── Chinese |
|
|
│ ├── ZH-Alto-1 |
|
|
│ └── ZH-Tenor-1 |
|
|
├── English |
|
|
│ ├── EN-Alto-1 |
|
|
│ │ ├── Breathy |
|
|
│ │ ├── Glissando |
|
|
│ │ │ └── my love |
|
|
│ │ │ ├── Control_Group |
|
|
│ │ │ ├── Glissando_Group |
|
|
│ │ │ └── Paired_Speech_Group |
|
|
│ │ ├── Mixed_Voice_and_Falsetto |
|
|
│ │ ├── Pharyngeal |
|
|
│ │ └── Vibrato |
|
|
│ ├── EN-Alto-2 |
|
|
│ │ ├── Breathy |
|
|
│ │ ├── Glissando |
|
|
│ │ ├── Mixed_Voice_and_Falsetto |
|
|
│ │ ├── Pharyngeal |
|
|
│ │ └── Vibrato |
|
|
│ └── EN-Tenor-1 |
|
|
│ ├── Breathy |
|
|
│ ├── Glissando |
|
|
│ ├── Mixed_Voice_and_Falsetto |
|
|
│ ├── Pharyngeal |
|
|
│ └── Vibrato |
|
|
├── French |
|
|
│ ├── FR-Soprano-1 |
|
|
│ └── FR-Tenor-1 |
|
|
├── German |
|
|
│ ├── DE-Soprano-1 |
|
|
│ └── DE-Tenor-1 |
|
|
├── Italian |
|
|
│ ├── IT-Bass-1 |
|
|
│ ├── IT-Bass-2 |
|
|
│ └── IT-Soprano-1 |
|
|
├── Japanese |
|
|
│ ├── JA-Soprano-1 |
|
|
│ └── JA-Tenor-1 |
|
|
├── Korean |
|
|
│ ├── KO-Soprano-1 |
|
|
│ ├── KO-Soprano-2 |
|
|
│ └── KO-Tenor-1 |
|
|
├── Russian |
|
|
│ └── RU-Alto-1 |
|
|
└── Spanish |
|
|
├── ES-Bass-1 |
|
|
└── ES-Soprano-1 |
|
|
``` |
|
|
|
|
|
## Citations ## |
|
|
|
|
|
If you find this code useful in your research, please cite our work: |
|
|
```bib |
|
|
@article{zhang2024gtsinger, |
|
|
title={Gtsinger: A global multi-technique singing corpus with realistic music scores for all singing tasks}, |
|
|
author={Zhang, Yu and Pan, Changhao and Guo, Wenxiang and Li, Ruiqi and Zhu, Zhiyuan and Wang, Jialei and Xu, Wenhao and Lu, Jingyu and Hong, Zhiqing and Wang, Chuxin and others}, |
|
|
journal={arXiv preprint arXiv:2409.13832}, |
|
|
year={2024} |
|
|
} |
|
|
``` |
|
|
|
|
|
## Disclaimer ## |
|
|
|
|
|
Any organization or individual is prohibited from using any technology mentioned in this paper to generate someone's singing without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws. |
