---
license: cc-by-nc-sa-4.0
---
# SongEval 🎵  
**A Large-Scale Benchmark Dataset for Aesthetic Evaluation of Complete Songs**

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[![Arxiv Paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/pdf/2505.10793)
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## 📖 Overview

**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.


<p align="center"> <img src="assets/intro.png" alt="SongEval" width="800"/> </p>

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## 🌟 Features

- 🎧 **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

<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

Each sample includes:

- `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

- 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

We provide an open-source evaluation toolkit trained on SongEval to help researchers evaluate new music generation outputs:

👉 GitHub: [https://github.com/ASLP-lab/SongEval](https://github.com/ASLP-lab/SongEval)

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## 📥 Download

You can load the dataset directly using 🤗 Datasets:

```python
from datasets import load_dataset

dataset = load_dataset("ASLP-lab/SongEval")

```

## 🙏 Acknowledgement

This project is mainly organized by the audio, speech and language processing lab [(ASLP@NPU)](http://www.npu-aslp.org/).

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.

<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}
}

```