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README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+---
+# SongEval 🎵  
+**A Large-Scale Benchmark Dataset for Aesthetic Evaluation of Complete Songs**
+
+[![Hugging Face Dataset](https://img.shields.io/badge/HuggingFace-Dataset-blue)](https://huggingface.co/datasets/ASLP-lab/SongEval)  
+[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)  
+
+---
+
+## 📖 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>
+
+---
+
+## 🌟 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>
+
+
+---
+
+## 📂 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)
+
+---
+
+## 🔍 Use Cases
+
+- Benchmarking song generation models from an aesthetic viewpoint
+- Training perceptual quality predictors for song
+- Exploring alignment between objective metrics and human judgments
+
+---
+
+## 🧪 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)
+
+---
+
+## 📥 Download
+
+You can load the dataset directly using 🤗 Datasets:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ASLP-lab/SongEval")
+
+```
+
+## 🙏 Acknowledgement
+
+We sincerely thank the **Shanghai Conservatory of Music** for their expert guidance on music theory, aesthetics, and annotation design.
+<p align="center"> <img src="assets/sy_logo.jpg" alt="Shanghai Conservatory of Music Logo" width="300"/> </p>
+
+---
+
+## 📬 Citation
+Coming soon!
+
+