---
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
- table-question-answering
- summarization
language:
- zh
- en
tags:
- music
- art
pretty_name: Acapella Evaluation Dataset
size_categories:
- n<1K
dataset_info:
  - config_name: default
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 48000
      - name: mel
        dtype: image
      - name: singer_id
        dtype:
          class_label:
            names:
              '0': singer1
              '1': singer2
              '2': singer3
              '3': singer4
              '4': singer5
              '5': singer6
              '6': singer7
              '7': singer8
              '8': singer9
              '9': singer10
              '10': singer11
              '11': singer12
              '12': singer13
              '13': singer14
              '14': singer15
              '15': singer16
              '16': singer17
              '17': singer18
              '18': singer19
              '19': singer20
              '20': singer21
              '21': singer22
      - name: pitch
        dtype: float32
      - name: rhythm
        dtype: float32
      - name: vocal_range
        dtype: float32
      - name: timbre
        dtype: float32
      - name: pronunciation
        dtype: float32
      - name: vibrato
        dtype: float32
      - name: dynamic
        dtype: float32
      - name: breath_control
        dtype: float32
      - name: overall_performance
        dtype: float32
    splits:
      - name: song1
        num_bytes: 8700
        num_examples: 22
      - name: song2
        num_bytes: 8700
        num_examples: 22
      - name: song3
        num_bytes: 8700
        num_examples: 22
      - name: song4
        num_bytes: 8700
        num_examples: 22
      - name: song5
        num_bytes: 8700
        num_examples: 22
      - name: song6
        num_bytes: 8700
        num_examples: 22
    download_size: 1385286751
    dataset_size: 52200
configs:
  - config_name: default
    data_files:
      - split: song1
        path: default/song1/data-*.arrow
      - split: song2
        path: default/song2/data-*.arrow
      - split: song3
        path: default/song3/data-*.arrow
      - split: song4
        path: default/song4/data-*.arrow
      - split: song5
        path: default/song5/data-*.arrow
      - split: song6
        path: default/song6/data-*.arrow
---

# Dataset Card for Acapella Evaluation
The original dataset, sourced from the [Acapella Evaluation Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou2), 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.

Due to the original dataset comprising separate files for audio recordings and evaluation sheets, which hindered efficient data retrieval, we combined the original vocal recordings with their corresponding evaluation sheets to construct the [default subset](#usage) of the current integrated version of the dataset. The data structure can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/acapella/dataPeview). The current dataset is already endorsed by published articles, hence there is no need to construct the eval subset.

## Dataset Structure
<style>
  .datastructure td {
    vertical-align: middle !important;
    text-align: center;
  }
  .datastructure th {
    text-align: center;
  }
</style>
<table class="datastructure">
    <tr>
        <th>audio</th>
        <th>mel</th>
        <th>singer_id</th>
        <th>pitch / rhythm / ... / overall_performance (9 colums)</th>
    </tr>
    <tr>
        <td>.wav, 48000Hz</td>
        <td>.jpg, 48000Hz</td>
        <td>int</td>
        <td>float(0-10)</td>
    </tr>
</table>

### Data Instances
.zip(.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 Description
### 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

## 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)
```

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

## Mirror
<https://www.modelscope.cn/datasets/ccmusic-database/acapella>

## 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)

### Citation Information
```bibtex
@article{Li2022SingingVoiceTE,
  title   = {Singing-Voice Timbre Evaluations Based on Transfer Learning},
  author  = {Rongfeng Li and Mingtong Zhang},
  journal = {Applied Sciences},
  year    = {2022},
  url     = {https://api.semanticscholar.org/CorpusID:252766951}
}
```
### Contributions
Provide a training dataset for the acapella scoring system