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---
license: cc-by-4.0
language:
- zh
- ja
tags:
- singing
- MOS
size_categories:
- 1B<n<10B
---
# 🎵 SingMOS-Pro
> **[Important Notice]**
> We have officially released the **[SingMOS-Pro dataset](https://huggingface.co/datasets/TangRain/SingMOS-Pro)** — the official benchmark for singing voice quality assessment.
---
## 📚 Related Resources
- 🧾 **Paper:** [*SingMOS-Pro: A Comprehensive Benchmark for Singing Quality Assessment*](https://arxiv.org/abs/2510.01812)
→ Describes dataset design, annotation methodology, and experiments.
- 🎶 **VoiceMOS 2024 Singing Track:** [SingMOS_v1](https://huggingface.co/datasets/TangRain/SingMOS_v1)
→ For reproducing or comparing with the official VoiceMOS 2024 track.
- 🤖 **Pretrained Model:** [Singing MOS Predictor](https://github.com/South-Twilight/SingMOS/tree/main)
→ Ready-to-use MOS prediction models trained on SingMOS and SingMOS-Pro.
---
## 🧩 Overview
**SingMOS-Pro** contains **7,981** Chinese and Japanese vocal clips, totaling **11.15 hours** of singing recordings.
Most samples are recorded at **16 kHz**, with a few at **24 kHz** or **44.1 kHz**.
This dataset enables large-scale research on **singing quality assessment** for tasks such as:
- Singing voice synthesis (SVS)
- Voice conversion (SVC)
- MOS prediction and correlation modeling
To use the dataset effectively, please refer to the following files:
| File | Description |
|------|--------------|
| `split.json` | Defines train/test partitions |
| `score.json` | Provides system- and utterance-level MOS annotations |
| `sys_info.json` | Describes system metadata (type, model, dataset, etc.) |
| `metadata.csv` | Flat-format summary of all utterances and attributes |
---
## 📂 Dataset Structure
```
SingMOS-Pro
├── wavs/ # Singing audio clips
│ ├── sys0001-utt0001.wav
│ ├── ...
├── info/ # Metadata and annotations
│ ├── split.json
│ ├── score.json
│ ├── sys_info.json
└── metadata.csv
````
---
## 🧾 File Descriptions
<details>
<summary><b>1️⃣ split.json — Dataset Partition File</b></summary>
Defines the train/test splits for each dataset.
**Example:**
```json
{
"dataset_name": {
"train": ["utt0001", "utt0002", "utt0003"],
"test": ["utt0101", "utt0102"]
}
}
````
**Field Descriptions:**
| Field | Description |
| -------------- | ------------------------------------------------------- |
| `dataset_name` | Name of the sub-dataset (e.g., `acesinger`, `opencpop`) |
| `train` | List of utterance IDs used for training |
| `test` | List of utterance IDs used for testing |
🔹 **Usage:** Load this file to ensure consistent dataset splits across experiments.
</details>
---
<details>
<summary><b>2️⃣ score.json — MOS Annotation File</b></summary>
Contains both **system-level** and **utterance-level** MOS (Mean Opinion Score) annotations.
**Example:**
```json
{
"system": {
"sys0001": {
"score": 3.85,
"ci": 0.07
}
},
"utterance": {
"utt0001": {
"sys_id": "sys0001",
"wav": "wavs/sys0001-utt0001.wav",
"score": {
"mos": 3.9,
"scores": [3.5, 4.0, 4.2],
"judges": ["J01", "J02", "J03"]
}
}
}
}
```
**Field Descriptions:**
| Field | Description |
| -------------- | -------------------------------------------- |
| `system` | Stores system-level MOS results |
| `sys_id` | Unique system identifier (e.g., `sys0001`) |
| `score` | Average MOS of the system |
| `ci` | Confidence interval for the system-level MOS |
| `utterance` | Stores utterance-level annotations |
| `utt_id` | Unique utterance identifier |
| `wav` | Relative path to the audio file |
| `score.mos` | Mean MOS for the utterance |
| `score.scores` | List of individual ratings from judges |
| `score.judges` | List of judge identifiers |
🔹 **Usage:**
* Evaluate system performance by comparing `system` and `utterance` levels.
* Compute correlations, inter-rater consistency, or build MOS prediction models.
</details>
---
<details>
<summary><b>3️⃣ sys_info.json — System Metadata File</b></summary>
Describes each singing system’s **category**, **dataset source**, **model**, and **sampling rate**.
**Example:**
```json
{
"sys0001": {
"type": "svs",
"dataset": "Opencpop",
"model": "DiffSinger",
"sample_rate": 16000,
"tag": {
"domain_id": "batch1",
"other_info": "default"
}
}
}
```
**Field Descriptions:**
| Field | Description |
| ---------------- | ---------------------------------------------------------------------------------------- |
| `sys_id` | Unique system identifier |
| `type` | System type: `svs` (singing synthesis), `svc` (voice conversion), or `gt` (ground truth) |
| `dataset` | Original dataset source |
| `model` | Model or architecture name used for generation |
| `sample_rate` | Audio sampling rate (Hz) |
| `tag.domain_id` | Batch ID or annotation domain |
| `tag.other_info` | Extra information (e.g., codec codebook, speaker transfer, etc.) |
> 💡 `"other_info": "default"` means no additional metadata is available.
🔹 **Usage:**
* Filter systems by type or dataset.
* Analyze system-level trends and quality differences.
</details>
---
<details>
<summary><b>4️⃣ metadata.csv — Sample-Level Summary Table</b></summary>
Provides a **flat-format summary** of all utterances, integrating data from the JSON files.
Ideal for quick indexing, filtering, and statistical analysis (e.g., via `pandas`).
**Example:**
```json
{
"dataset": "acesinger",
"domain_id": 1,
"id": "sys0001-utt0001",
"judge_id": [1, 2, 3, 4, 5],
"judge_lyrics_score": [],
"judge_melody_score": [],
"judge_score": [4.0, 4.0, 4.0, 4.0, 4.0],
"language": "Chinese",
"lyrics": "",
"model_name": "ace",
"other_info": "default",
"raw_wav_id": "22#2100003752",
"sample_rate": 16000,
"split": "test",
"system": "acesinger@ace@default",
"system_id": "sys0001",
"type": "svs",
"wav": "wav/sys0001-utt0001.wav"
}
```
**Field Descriptions:**
| Field | Description |
| ------------------------------------------- | --------------------------------------------------------- |
| `dataset` | Original dataset name |
| `domain_id` | Annotation batch or domain index |
| `id` | Unique utterance identifier (`sysID-uttID`) |
| `judge_id` | List of judge IDs who rated this utterance |
| `judge_lyrics_score` / `judge_melody_score` | Optional sub-dimension ratings (may be empty) |
| `judge_score` | List of overall MOS ratings from judges |
| `language` | Singing language (`Chinese` or `Japanese`) |
| `lyrics` | Transcribed lyrics text (if available) |
| `model_name` | Model or architecture name used to generate audio |
| `other_info` | Additional configuration info (e.g., codec, speaker info) |
| `raw_wav_id` | Original recording or dataset identifier |
| `sample_rate` | Sampling rate in Hz |
| `split` | Dataset partition (`train` / `test`) |
| `system` | Full system identifier (`dataset@model@info`) |
| `system_id` | System-level ID (matches `sys_info.json`) |
| `type` | System type: `svs`, `svc`, or `gt` |
| `wav` | Relative path to waveform file |
🔹 **Usage:**
* Load with `pandas.read_csv` for analysis.
* Merge by `system_id` or filter by language/type.
* Perform judge-level or system-level statistical analysis.
</details>
---
## 🗓️ Update History
| Date | Update |
| -------------- | ------------------------ |
| **2025-10-09** | Released **SingMOS-Pro** |
| **2024-11-06** | Released **SingMOS** |
| **2024-06-26** | Released **SingMOS_v1** |
---
## 📖 Citation
If you use this dataset, please cite the following paper:
```bibtex
@misc{tang2025singmosprocomprehensivebenchmarksinging,
title={SingMOS-Pro: A Comprehensive Benchmark for Singing Quality Assessment},
author={Yuxun Tang and Lan Liu and Wenhao Feng and Yiwen Zhao and Jionghao Han and Yifeng Yu and Jiatong Shi and Qin Jin},
year={2025},
eprint={2510.01812},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2510.01812}
}
```
|