---
license: mit
task_categories:
- audio-classification
- table-question-answering
- summarization
language:
- zh
- en
tags:
- music
- art
pretty_name: Acapella Evaluation Dataset
size_categories:
- n<1K
viewer: false
---

# Dataset Card for Acapella Evaluation
This raw dataset comprises six Mandarin pop song segments performed by 22 singers, resulting in a total of 132 audio clips. Each segment includes both a verse and a chorus. Four judges from the China Conservatory of Music assess the singing across nine dimensions: pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamics, breath control, and overall performance, using a 10-point scale. The evaluations are recorded in an Excel spreadsheet in .xls format.

## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/CCMUSIC/acapella_evaluation>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic/acapella>
- **Point of Contact:** <https://www.mdpi.com/2076-3417/12/19/9931>

### Dataset Summary
Due to the original dataset comprising separate files for audio recordings and evaluation sheets, which hindered efficient data retrieval, we have consolidated the raw vocal recordings with their corresponding assessments. The dataset is divided into six segments, each representing a different song, resulting in a total of six divisions. Each segment contains 22 entries, with each entry detailing the vocal recording of an individual singer sampled at 22,050 Hz, the singer's ID, and evaluations across the nine dimensions previously mentioned. Consequently, each entry encompasses 11 columns of data. This dataset is well-suited for tasks such as vocal analysis and regression-based singing voice rating. For instance, as previously stated, the final column of each entry denotes the overall performance score, allowing the audio to be utilized as data and this score to serve as the label for regression analysis.

### Supported Tasks and Leaderboards
Acapella evaluation/scoring

### Languages
Chinese, English

## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/acapella
cd acapella
```

## Usage
```python
from datasets import load_dataset

dataset = load_dataset("ccmusic-database/acapella")
for i in range(1, 7):
    for item in dataset[f"song{i}"]:
        print(item)
```

## Dataset Structure
|                                                       audio(22050Hz)                                                        |           mel(22050Hz)            | singer_id | pitch / rhythm / ... / overall_performance |
| :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------: | :-------: | :----------------------------------------: |
| <audio controls src="https://huggingface.co/datasets/ccmusic-database/acapella/resolve/main/data/song1%20(16).wav"></audio> | <img src="./data/song1 (16).jpg"> |    int    |                float(0-10)                 |
|                                                             ...                                                             |                ...                |    ...    |                    ...                     |

### Data Instances
.wav & .csv

### Data Fields
song, singer id, pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance

### Data Splits
song1-6

## Dataset Creation
### Curation Rationale
Lack of a training dataset for the acapella scoring system

### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou

#### Who are the source language producers?
Students and judges from CCMUSIC

### Annotations
#### Annotation process
6 Mandarin song segments were sung by 22 singers, totaling 132 audio clips. Each segment consists of a verse and a chorus. Four judges evaluate the singing from nine aspects which are pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance on a 10-point scale. The scores are recorded on a sheet.

#### Who are the annotators?
Judges from CCMUSIC

### Personal and Sensitive Information
Singers' and judges' names are hided

## Considerations for Using the Data
### Social Impact of Dataset
Providing a training dataset for the acapella scoring system may improve the development of related Apps

### Discussion of Biases
Only for Mandarin songs

### Other Known Limitations
No starting point has been marked for the vocal

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
[Li, R.; Zhang, M. Singing-Voice Timbre Evaluations Based on Transfer Learning. Appl. Sci. 2022, 12, 9931. https://doi.org/10.3390/app12199931](https://www.mdpi.com/2076-3417/12/19/9931)

### Licensing Information
```
MIT License

Copyright (c) CCMUSIC

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```

### Citation Information
```bibtex
@dataset{zhaorui_liu_2021_5676893,
  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
  title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
  month        = {mar},
  year         = {2024},
  publisher    = {HuggingFace},
  version      = {1.2},
  url          = {https://huggingface.co/ccmusic-database}
}
```
### Contributions
Provide a training dataset for the acapella scoring system