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SongEval/README.md

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+# 🎵 SongEval: A Benchmark Dataset for Song Aesthetics Evaluation
+
+[![Hugging Face Dataset](https://img.shields.io/badge/HuggingFace-Dataset-blue)](https://huggingface.co/datasets/ASLP-lab/SongEval)
+[![Arxiv Paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/pdf/2505.10793)
+[![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/)  
+
+
+This repository provides a **trained aesthetic evaluation toolkit** based on [SongEval](https://huggingface.co/datasets/ASLP-lab/SongEval), the first large-scale, open-source dataset for human-perceived song aesthetics. The toolkit enables **automatic scoring of generated song** across five perceptual aesthetic dimensions aligned with professional musician judgments.
+
+---
+
+## 🌟 Key Features
+
+- 🧠 **Pretrained neural models** for perceptual aesthetic evaluation
+- 🎼 Predicts **five aesthetic dimensions**:
+  - Overall Coherence
+  - Memorability
+  - Naturalness of Vocal Breathing and Phrasing
+  - Clarity of Song Structure
+  - Overall Musicality
+<!-- - 🧪 Supports **batch evaluation** for model benchmarking -->
+- 🎧 Accepts **full-length songs** (vocals + accompaniment) as input
+- ⚙️ Simple inference interface
+
+---
+
+## 📦 Installation
+
+Clone the repository and install dependencies:
+
+```bash
+git clone https://github.com/ASLP-lab/SongEval.git
+cd SongEval
+pip install -r requirements.txt
+```
+
+## 🚀 Quick Start
+
+- Evaluate a single audio file:
+
+```bash
+python eval.py -i /path/to/audio.mp3 -o /path/to/output
+```
+
+- Evaluate a list of audio files:
+
+```bash
+python eval.py -i /path/to/audio_list.txt -o /path/to/output
+```
+
+- Evaluate all audio files in a directory:
+
+```bash
+python eval.py -i /path/to/audio_directory -o /path/to/output
+```
+
+- Force evaluation on CPU  (⚠️ CPU evaluation may be significantly slower) :
+
+
+```bash
+python eval.py -i /path/to/audio.wav -o /path/to/output --use_cpu True
+```
+
+
+## 🙏 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>
+
+## 📑 License
+This project is released under the CC BY-NC-SA 4.0 license. 
+
+You are free to use, modify, and build upon it for non-commercial purposes, with attribution.
+
+## 📚 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}
+}
+
+```