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# SongFormBench 🏆 |
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[[English](README.md) ｜ 中文] |
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**一个高质量的音乐结构分析基准** |
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<div align="center"> |
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[](https://arxiv.org/abs/2510.02797) |
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[](https://github.com/ASLP-lab/SongFormer) |
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[](https://huggingface.co/spaces/ASLP-lab/SongFormer) |
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[](https://huggingface.co/ASLP-lab/SongFormer) |
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[](https://huggingface.co/datasets/ASLP-lab/SongFormDB) |
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[](https://huggingface.co/datasets/ASLP-lab/SongFormBench) |
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[](https://discord.gg/p5uBryC4Zs) |
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[](http://www.npu-aslp.org/) |
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</div> |
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<div align="center"> |
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<h3> |
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Chunbo Hao<sup>1*</sup>, Ruibin Yuan<sup>2,5*</sup>, Jixun Yao<sup>1</sup>, Qixin Deng<sup>3,5</sup>,<br>Xinyi Bai<sup>4,5</sup>, Wei Xue<sup>2</sup>, Lei Xie<sup>1†</sup> |
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</h3> |
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<p> |
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<sup>*</sup>Equal contribution <sup>†</sup>Corresponding author |
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</p> |
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<p> |
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<sup>1</sup>Audio, Speech and Language Processing Group (ASLP@NPU),<br>Northwestern Polytechnical University<br> |
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<sup>2</sup>Hong Kong University of Science and Technology<br> |
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<sup>3</sup>Northwestern University<br> |
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<sup>4</sup>Cornell University<br> |
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<sup>5</sup>Multimodal Art Projection (M-A-P) |
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</p> |
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</div> |
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--- |
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## 🌟 什么是 SongFormBench？ |
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SongFormBench 是一个**经过精心整理、由专家标注的基准数据集**，旨在彻底改变音乐结构分析（MSA）的评估方式。我们的数据集为比较 MSA 模型提供了统一标准。 |
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### 📊 数据集构成 |
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- **🎸 SongFormBench-HarmonixSet (BHX)**: 来自 HarmonixSet 的 200 首歌曲 |
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- **🎤 SongFormBench-CN (BC)**: 100 首中文流行歌曲 |
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**总计：300 首高质量标注歌曲** |
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--- |
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## ✨ 主要亮点 |
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### 🎯 **统一评估标准** |
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- 建立了 **标准化基准**，实现 MSA 模型间的公平比较 |
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- 消除了评估协议中的不一致性 |
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### 🏷️ **简单标签系统** |
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- 采用广泛使用的7类分类系统（如 [arxiv.org/abs/2205.14700](https://arxiv.org/abs/2205.14700) 中所述） |
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- 保留 **pre-chorus** 段落以增强粒度 |
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- 可轻松转换为 7 类（pre-chorus → verse）以保证兼容性 |
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### 👨🔬 **专家验证质量** |
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- 多源验证 |
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- 专家标注员手动校正 |
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### 🌏 **多语言覆盖** |
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- **首个中文 MSA 数据集**（100 首歌曲） |
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- 弥补了中文音乐结构分析的空白 |
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- 支持跨语言 MSA 研究 |
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--- |
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## 🚀 快速开始 |
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### 快速加载 |
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```python |
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from datasets import load_dataset |
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# 加载完整基准数据集 |
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dataset = load_dataset("ASLP-lab/SongFormBench") |
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``` |
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--- |
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## 📚 资源与链接 |
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- 📖 论文：*即将发布* |
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- 💻 代码：[GitHub 仓库](https://github.com/ASLP-lab/SongFormer) |
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- 🧑💻 模型：[SongFormer](https://huggingface.co/ASLP-lab/SongFormer) |
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- 📂 数据集：[SongFormDB](https://huggingface.co/datasets/ASLP-lab/SongFormDB) |
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--- |
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## 🤝 引用 |
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```bibtex |
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@misc{hao2025songformer, |
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title = {SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision}, |
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author = {Chunbo Hao and Ruibin Yuan and Jixun Yao and Qixin Deng and Xinyi Bai and Wei Xue and Lei Xie}, |
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year = {2025}, |
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eprint = {2510.02797}, |
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archivePrefix = {arXiv}, |
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primaryClass = {eess.AS}, |
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url = {https://arxiv.org/abs/2510.02797} |
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} |
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``` |
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--- |
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## 🎼 梅尔频谱图细节 |
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<details> |
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<summary>Click to expand/collapse</summary> |
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环境配置可参考 BigVGan 的官方实现。如果音频源失效，可使用以下方法重建音频。 |
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### 🎸 SongFormBench-HarmonixSet |
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使用官方 HarmonixSet 梅尔频谱图。复现方法如下： |
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```bash |
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# 克隆 BigVGAN 仓库 |
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git clone https://github.com/NVIDIA/BigVGAN.git |
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# 进入 utils 目录 |
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cd utils/HarmonixSet |
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# 更新 inference_e2e.sh 中的 BIGVGAN_REPO_DIR |
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# 运行推理脚本 |
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bash inference_e2e.sh |
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``` |
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### 🎤 SongFormBench-CN |
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使用 [**bigvgan_v2_44khz_128band_256x**](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x) 重建。 |
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您应首先下载 bigvgan_v2_44khz_128band_256x，然后将其项目目录添加到 PYTHONPATH 中，之后即可使用以下代码： |
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```python |
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# 查看实现 |
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utils/CN/infer.py |
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``` |
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</details> |
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## 📧 联系方式 |
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如有问题、反馈或合作机会，请访问我们的 [GitHub 仓库](https://github.com/ASLP-lab/SongFormer) 或提交问题。 |
