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, 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 of the current integrated version of the dataset. The data structure can be viewed in the viewer. The current dataset is already endorsed by published articles, hence there is no need to construct the eval subset.
Dataset Structure
| audio | mel | singer_id | pitch / rhythm / ... / overall_performance (9 colums) |
|---|---|---|---|
| .wav, 48000Hz | .jpg, 48000Hz | int | float(0-10) |
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
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
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]: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
Citation Information
@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