| --- |
| 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. |
