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Parent(s):
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Browse files- .gitignore +2 -0
- README.md +103 -12
- acapella.py +0 -119
- default/dataset_dict.json +1 -0
- default/song1/data-00000-of-00001.arrow +3 -0
- default/song1/dataset_info.json +172 -0
- default/song1/state.json +13 -0
- default/song2/data-00000-of-00001.arrow +3 -0
- default/song2/dataset_info.json +172 -0
- default/song2/state.json +13 -0
- default/song3/data-00000-of-00002.arrow +3 -0
- default/song3/data-00001-of-00002.arrow +3 -0
- default/song3/dataset_info.json +172 -0
- default/song3/state.json +16 -0
- default/song4/data-00000-of-00002.arrow +3 -0
- default/song4/data-00001-of-00002.arrow +3 -0
- default/song4/dataset_info.json +172 -0
- default/song4/state.json +16 -0
- default/song5/data-00000-of-00002.arrow +3 -0
- default/song5/data-00001-of-00002.arrow +3 -0
- default/song5/dataset_info.json +172 -0
- default/song5/state.json +16 -0
- default/song6/data-00000-of-00001.arrow +3 -0
- default/song6/dataset_info.json +172 -0
- default/song6/state.json +13 -0
.gitignore
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rename.sh
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rename.sh
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test.*
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*__pycache__*
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README.md
CHANGED
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@@ -13,7 +13,95 @@ tags:
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pretty_name: Acapella Evaluation Dataset
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size_categories:
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- n<1K
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-
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---
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# Dataset Card for Acapella Evaluation
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@@ -65,12 +153,6 @@ Acapella evaluation/scoring
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### Languages
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Chinese, English
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-
## Maintenance
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-
```bash
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git clone [email protected]:datasets/ccmusic-database/acapella
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cd acapella
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```
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-
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## Usage
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```python
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from datasets import load_dataset
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print(item)
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```
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## Dataset Creation
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### Curation Rationale
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Lack of a training dataset for the acapella scoring system
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### Citation Information
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```bibtex
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@article{Li2022SingingVoiceTE,
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title={Singing-Voice Timbre Evaluations Based on Transfer Learning},
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author={Rongfeng Li and Mingtong Zhang},
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journal={Applied Sciences},
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year={2022},
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url={https://api.semanticscholar.org/CorpusID:252766951}
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}
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```
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| 132 |
### Contributions
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pretty_name: Acapella Evaluation Dataset
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size_categories:
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- n<1K
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+
dataset_info:
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+
- config_name: default
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+
features:
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- name: audio
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dtype:
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audio:
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sampling_rate: 48000
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- name: mel
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dtype: image
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+
- name: singer_id
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dtype:
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class_label:
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names:
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'0': singer1
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'1': singer2
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'2': singer3
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'3': singer4
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'4': singer5
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+
'5': singer6
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'6': singer7
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'7': singer8
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'8': singer9
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'9': singer10
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'10': singer11
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+
'11': singer12
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+
'12': singer13
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'13': singer14
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+
'14': singer15
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+
'15': singer16
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+
'16': singer17
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+
'17': singer18
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'18': singer19
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'19': singer20
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'20': singer21
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'21': singer22
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- name: pitch
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dtype: float32
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- name: rhythm
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+
dtype: float32
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+
- name: vocal_range
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+
dtype: float32
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+
- name: timbre
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+
dtype: float32
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+
- name: pronunciation
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dtype: float32
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+
- name: vibrato
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dtype: float32
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+
- name: dynamic
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+
dtype: float32
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+
- name: breath_control
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dtype: float32
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- name: overall_performance
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dtype: float32
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splits:
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- name: song1
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num_bytes: 8700
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num_examples: 22
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- name: song2
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num_bytes: 8700
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num_examples: 22
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- name: song1
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num_bytes: 8700
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num_examples: 22
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+
- name: song2
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num_bytes: 8700
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num_examples: 22
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- name: song1
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num_bytes: 8700
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num_examples: 22
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- name: song2
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num_bytes: 8700
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num_examples: 22
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download_size: 1385286751
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dataset_size: 52200
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configs:
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- config_name: default
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data_files:
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- split: song1
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path: default/song1/data-*.arrow
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- split: song2
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path: default/song2/data-*.arrow
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- split: song3
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path: default/song3/data-*.arrow
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- split: song4
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path: default/song4/data-*.arrow
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- split: song5
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path: default/song5/data-*.arrow
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- split: song6
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path: default/song6/data-*.arrow
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| 105 |
---
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| 106 |
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# Dataset Card for Acapella Evaluation
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| 153 |
### Languages
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Chinese, English
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| 156 |
## Usage
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| 157 |
```python
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from datasets import load_dataset
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print(item)
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```
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| 165 |
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+
## Maintenance
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| 167 |
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```bash
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GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/acapella
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cd acapella
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```
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| 171 |
+
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| 172 |
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## Mirror
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| 173 |
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<https://www.modelscope.cn/datasets/ccmusic-database/acapella>
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+
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| 175 |
## Dataset Creation
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| 176 |
### Curation Rationale
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| 177 |
Lack of a training dataset for the acapella scoring system
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| 213 |
### Citation Information
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| 214 |
```bibtex
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| 215 |
@article{Li2022SingingVoiceTE,
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| 216 |
+
title = {Singing-Voice Timbre Evaluations Based on Transfer Learning},
|
| 217 |
+
author = {Rongfeng Li and Mingtong Zhang},
|
| 218 |
+
journal = {Applied Sciences},
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| 219 |
+
year = {2022},
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| 220 |
+
url = {https://api.semanticscholar.org/CorpusID:252766951}
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| 221 |
}
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| 222 |
```
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| 223 |
### Contributions
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acapella.py
DELETED
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-
import os
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import datasets
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import pandas as pd
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from datasets.tasks import AudioClassification
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-
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-
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_NAMES = {
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"songs": [f"song{i}" for i in range(1, 7)],
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"singers": [f"singer{i}" for i in range(1, 23)],
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}
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-
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_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"
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-
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_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"
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-
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_URLS = {
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"audio": f"{_DOMAIN}/audio.zip",
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"mel": f"{_DOMAIN}/mel.zip",
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-
}
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-
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-
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-
class acapella(datasets.GeneratorBasedBuilder):
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def _info(self):
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return datasets.DatasetInfo(
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features=datasets.Features(
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-
{
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-
"audio": datasets.Audio(sampling_rate=48000),
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"mel": datasets.Image(),
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"singer_id": datasets.features.ClassLabel(names=_NAMES["singers"]),
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| 30 |
-
"pitch": datasets.Value("float32"),
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"rhythm": datasets.Value("float32"),
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-
"vocal_range": datasets.Value("float32"),
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| 33 |
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"timbre": datasets.Value("float32"),
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| 34 |
-
"pronunciation": datasets.Value("float32"),
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"vibrato": datasets.Value("float32"),
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| 36 |
-
"dynamic": datasets.Value("float32"),
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| 37 |
-
"breath_control": datasets.Value("float32"),
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| 38 |
-
"overall_performance": datasets.Value("float32"),
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-
}
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),
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supervised_keys=("audio", "singer_id"),
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homepage=_HOMEPAGE,
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license="CC-BY-NC-ND",
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version="1.2.0",
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| 45 |
-
task_templates=[
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AudioClassification(
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task="audio-classification",
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audio_column="audio",
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| 49 |
-
label_column="singer_id",
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-
)
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-
],
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)
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-
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-
def _split_generators(self, dl_manager):
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-
songs = {}
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| 56 |
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for index in _NAMES["songs"]:
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| 57 |
-
csv_files = dl_manager.download(f"{_DOMAIN}/{index}.csv")
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| 58 |
-
song_eval = pd.read_csv(csv_files, index_col="singer_id")
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| 59 |
-
scores = []
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| 60 |
-
for i in range(len(_NAMES["singers"])):
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| 61 |
-
scores.append(
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| 62 |
-
{
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| 63 |
-
"pitch": song_eval.iloc[i]["pitch"],
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| 64 |
-
"rhythm": song_eval.iloc[i]["rhythm"],
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| 65 |
-
"vocal_range": song_eval.iloc[i]["vocal_range"],
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| 66 |
-
"timbre": song_eval.iloc[i]["timbre"],
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"pronunciation": song_eval.iloc[i]["pronunciation"],
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| 68 |
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"vibrato": song_eval.iloc[i]["vibrato"],
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| 69 |
-
"dynamic": song_eval.iloc[i]["dynamic"],
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| 70 |
-
"breath_control": song_eval.iloc[i]["breath_control"],
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| 71 |
-
"overall_performance": song_eval.iloc[i]["overall_performance"],
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| 72 |
-
}
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-
)
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-
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| 75 |
-
songs[index] = scores
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-
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-
audio_files = dl_manager.download_and_extract(_URLS["audio"])
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| 78 |
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for fpath in dl_manager.iter_files([audio_files]):
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| 79 |
-
fname: str = os.path.basename(fpath)
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| 80 |
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if fname.endswith(".wav"):
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| 81 |
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song_id = os.path.basename(os.path.dirname(fpath))
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| 82 |
-
singer_id = int(fname.split("(")[1].split(")")[0]) - 1
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| 83 |
-
songs[song_id][singer_id]["audio"] = fpath
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| 84 |
-
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| 85 |
-
mel_files = dl_manager.download_and_extract(_URLS["mel"])
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| 86 |
-
for fpath in dl_manager.iter_files([mel_files]):
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| 87 |
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fname = os.path.basename(fpath)
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| 88 |
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if fname.endswith(".jpg"):
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| 89 |
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song_id = os.path.basename(os.path.dirname(fpath))
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| 90 |
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singer_id = int(fname.split("(")[1].split(")")[0]) - 1
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| 91 |
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songs[song_id][singer_id]["mel"] = fpath
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-
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| 93 |
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split_generator = []
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| 94 |
-
for key in songs.keys():
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| 95 |
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split_generator.append(
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| 96 |
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datasets.SplitGenerator(
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name=key,
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gen_kwargs={"files": songs[key]},
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-
)
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| 100 |
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)
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| 101 |
-
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| 102 |
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return split_generator
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-
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| 104 |
-
def _generate_examples(self, files):
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| 105 |
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for i, item in enumerate(files):
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| 106 |
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yield i, {
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"audio": item["audio"],
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| 108 |
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"mel": item["mel"],
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"singer_id": i,
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"pitch": item["pitch"],
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"rhythm": item["rhythm"],
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| 112 |
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"vocal_range": item["vocal_range"],
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"timbre": item["timbre"],
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| 114 |
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"pronunciation": item["pronunciation"],
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| 115 |
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"vibrato": item["vibrato"],
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"dynamic": item["dynamic"],
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"breath_control": item["breath_control"],
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"overall_performance": item["overall_performance"],
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}
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default/dataset_dict.json
ADDED
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| 1 |
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{"splits": ["song1", "song2", "song3", "song4", "song5", "song6"]}
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default/song1/data-00000-of-00001.arrow
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|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
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|
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|
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|
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|
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| 1 |
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| 2 |
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| 3 |
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| 30 |
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|
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| 32 |
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| 38 |
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| 40 |
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|
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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| 55 |
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| 56 |
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| 57 |
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|
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| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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| 119 |
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|
| 120 |
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| 121 |
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| 122 |
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|
| 123 |
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|
| 124 |
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| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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| 131 |
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| 132 |
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| 133 |
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|
| 135 |
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|
| 136 |
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| 137 |
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| 141 |
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|
| 142 |
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| 143 |
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| 144 |
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| 145 |
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|
| 147 |
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|
| 148 |
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| 149 |
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|
| 150 |
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| 151 |
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| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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"output": "singer_id"
|
| 159 |
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|
| 160 |
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|
| 161 |
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{
|
| 162 |
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"task": "audio-classification",
|
| 163 |
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|
| 164 |
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|
| 165 |
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| 166 |
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| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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default/song1/state.json
ADDED
|
@@ -0,0 +1,13 @@
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| 1 |
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{
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| 2 |
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| 3 |
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{
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| 4 |
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"filename": "data-00000-of-00001.arrow"
|
| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 10 |
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| 11 |
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|
| 12 |
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|
| 13 |
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default/song2/data-00000-of-00001.arrow
ADDED
|
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ADDED
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| 1 |
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{
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| 2 |
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| 3 |
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|
| 4 |
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| 5 |
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| 7 |
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| 8 |
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| 18 |
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| 26 |
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| 29 |
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| 1 |
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| 3 |
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| 4 |
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| 14 |
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| 18 |
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| 19 |
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| 26 |
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|
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| 156 |
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| 158 |
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| 159 |
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"breath_control": {
|
| 107 |
+
"dtype": "float32",
|
| 108 |
+
"_type": "Value"
|
| 109 |
+
},
|
| 110 |
+
"overall_performance": {
|
| 111 |
+
"dtype": "float32",
|
| 112 |
+
"_type": "Value"
|
| 113 |
+
}
|
| 114 |
+
},
|
| 115 |
+
"homepage": "https://www.modelscope.cn/datasets/ccmusic-database/acapella",
|
| 116 |
+
"license": "CC-BY-NC-ND",
|
| 117 |
+
"size_in_bytes": 1385338951,
|
| 118 |
+
"splits": {
|
| 119 |
+
"song1": {
|
| 120 |
+
"name": "song1",
|
| 121 |
+
"num_bytes": 8700,
|
| 122 |
+
"num_examples": 22,
|
| 123 |
+
"dataset_name": "acapella"
|
| 124 |
+
},
|
| 125 |
+
"song2": {
|
| 126 |
+
"name": "song2",
|
| 127 |
+
"num_bytes": 8700,
|
| 128 |
+
"num_examples": 22,
|
| 129 |
+
"dataset_name": "acapella"
|
| 130 |
+
},
|
| 131 |
+
"song3": {
|
| 132 |
+
"name": "song3",
|
| 133 |
+
"num_bytes": 8700,
|
| 134 |
+
"num_examples": 22,
|
| 135 |
+
"dataset_name": "acapella"
|
| 136 |
+
},
|
| 137 |
+
"song4": {
|
| 138 |
+
"name": "song4",
|
| 139 |
+
"num_bytes": 8700,
|
| 140 |
+
"num_examples": 22,
|
| 141 |
+
"dataset_name": "acapella"
|
| 142 |
+
},
|
| 143 |
+
"song5": {
|
| 144 |
+
"name": "song5",
|
| 145 |
+
"num_bytes": 8700,
|
| 146 |
+
"num_examples": 22,
|
| 147 |
+
"dataset_name": "acapella"
|
| 148 |
+
},
|
| 149 |
+
"song6": {
|
| 150 |
+
"name": "song6",
|
| 151 |
+
"num_bytes": 8700,
|
| 152 |
+
"num_examples": 22,
|
| 153 |
+
"dataset_name": "acapella"
|
| 154 |
+
}
|
| 155 |
+
},
|
| 156 |
+
"supervised_keys": {
|
| 157 |
+
"input": "audio",
|
| 158 |
+
"output": "singer_id"
|
| 159 |
+
},
|
| 160 |
+
"task_templates": [
|
| 161 |
+
{
|
| 162 |
+
"task": "audio-classification",
|
| 163 |
+
"label_column": "singer_id"
|
| 164 |
+
}
|
| 165 |
+
],
|
| 166 |
+
"version": {
|
| 167 |
+
"version_str": "0.0.0",
|
| 168 |
+
"major": 0,
|
| 169 |
+
"minor": 0,
|
| 170 |
+
"patch": 0
|
| 171 |
+
}
|
| 172 |
+
}
|
default/song6/state.json
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_data_files": [
|
| 3 |
+
{
|
| 4 |
+
"filename": "data-00000-of-00001.arrow"
|
| 5 |
+
}
|
| 6 |
+
],
|
| 7 |
+
"_fingerprint": "ee0fb212bc3908ae",
|
| 8 |
+
"_format_columns": null,
|
| 9 |
+
"_format_kwargs": {},
|
| 10 |
+
"_format_type": null,
|
| 11 |
+
"_output_all_columns": false,
|
| 12 |
+
"_split": "song6"
|
| 13 |
+
}
|