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| license: cc-by-nc-sa-4.0 |
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| # SongEval 🎵 |
| **A Large-Scale Benchmark Dataset for Aesthetic Evaluation of Complete Songs** |
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| <!-- [](https://huggingface.co/datasets/ASLP-lab/SongEval) --> |
| [](https://github.com/ASLP-lab/SongEval) |
| [](https://arxiv.org/pdf/2505.10793) |
| [](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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| ## 📖 Overview |
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| **SongEval** is the first open-source, large-scale benchmark dataset designed for **aesthetic evaluation of complete songs**. It provides over **2,399 songs** (~140 hours) annotated by **16 expert raters** across **five perceptual dimensions**. The dataset enables research in evaluating and improving music generation systems from a human aesthetic perspective. |
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| <p align="center"> <img src="assets/intro.png" alt="SongEval" width="800"/> </p> |
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| ## 🌟 Features |
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| - 🎧 **2,399 complete songs** (with vocals and accompaniment) |
| - ⏱️ **~140 hours** of high-quality audio |
| - 🌍 **English and Chinese** songs |
| - 🎼 **9 mainstream genres** |
| - 📝 **5 aesthetic dimensions**: |
| - Overall Coherence |
| - Memorability |
| - Naturalness of Vocal Breathing and Phrasing |
| - Clarity of Song Structure |
| - Overall Musicality |
| - 📊 Ratings on a **5-point Likert scale** by **musically trained annotators** |
| - 🎙️ Includes outputs from **five generation models** + a subset of real/bad-case samples |
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| <div style="display: flex; justify-content: space-between;"> |
| <img src="assets/score.png" alt="Image 1" style="width: 48%;" /> |
| <img src="assets/distribution.png" alt="Image 2" style="width: 48%;" /> |
| </div> |
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| ## 📂 Dataset Structure |
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| Each sample includes: |
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| - `audio`: WAV audio of the full song |
| - `gender`: male or female |
| - `aesthetic_scores`: dict of five human-annotated scores (1–5) |
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| ## 🔍 Use Cases |
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| - Benchmarking song generation models from an aesthetic viewpoint |
| - Training perceptual quality predictors for song |
| - Exploring alignment between objective metrics and human judgments |
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| ## 🧪 Evaluation Toolkit |
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| We provide an open-source evaluation toolkit trained on SongEval to help researchers evaluate new music generation outputs: |
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| 👉 GitHub: [https://github.com/ASLP-lab/SongEval](https://github.com/ASLP-lab/SongEval) |
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| ## 📥 Download |
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| You can load the dataset directly using 🤗 Datasets: |
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| ```python |
| from datasets import load_dataset |
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| dataset = load_dataset("ASLP-lab/SongEval") |
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| ``` |
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| ## 🙏 Acknowledgement |
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| This project is mainly organized by the audio, speech and language processing lab [(ASLP@NPU)](http://www.npu-aslp.org/). |
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| We sincerely thank the **Shanghai Conservatory of Music** for their expert guidance on music theory, aesthetics, and annotation design. |
| Meanwhile, we thank AISHELL to help with the orgnization of the song annotations. |
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| <p align="center"> <img src="assets/logo.png" alt="Shanghai Conservatory of Music Logo"/> </p> |
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| ## 📬 Citation |
| If you use this toolkit or the SongEval dataset, please cite the following: |
| ``` |
| @article{yao2025songeval, |
| title = {SongEval: A Benchmark Dataset for Song Aesthetics Evaluation}, |
| author = {Yao, Jixun and Ma, Guobin and Xue, Huixin and Chen, Huakang and Hao, Chunbo and Jiang, Yuepeng and Liu, Haohe and Yuan, Ruibin and Xu, Jin and Xue, Wei and others}, |
| journal = {arXiv preprint arXiv:2505.10793}, |
| year={2025} |
| } |
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| ``` |
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