jpdiazpardo/guturalScream_metalVocals

---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: song_name
dtype: string
- name: artist_name
dtype: string
- name: album_name
dtype: string
- name: release_year
dtype: int64
- name: video_id
dtype: string
- name: timestamp_start
dtype: float64
- name: timestamp_end
dtype: float64
- name: sample_rate
dtype: int64
splits:
- name: train
num_bytes: 1259147118.2099998
num_examples: 1740
- name: test
num_bytes: 403875517.75
num_examples: 580
download_size: 1629538009
dataset_size: 1663022635.9599998
license: mit
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- music
size_categories:
- 1K<n<10K
pretty_name: Scream and gutural sound transcriptions from heavy metal songs
---
# Dataset Card for "Gutural Speech Recognition"
This dataset contains annotations of 57 songs.

### How to use
Load the dataset from huggingface in your notebook:
```python
!pip install datasets[audio]

import datasets

dataset = datasets.load_dataset("jpdiazpardo/guturalScream_metalVocals")
```

### Data Fields

* `audio`: the trimmed audio file from the song.
* `text`: the transcribed vocals.
* `song_name`: the song title.
* `artist_name`: the artist name.
* `album_name`: the name of the album where the song was released.
* `release_year`: the release year of the song.
* `video_id`: the YouTube video id.
* `timestamp_start`: the start time of the snippet from the full audio.
* `timestamp_end`: the end time of the snippet from the full audio.
* `sample_rate`: the sampling rate of the audio.

### Youtube playlist: [Gutural Speech Recognition](https://www.youtube.com/playlist?list=PLkCTyMdVt0AHgp-80jqskjUtfHo-Ht4xy)

### Source Data

| video id | artist | song | album | release_year |
|-------------|-------------------------|-----------------------------------------------|------------------------------------------|--------------|
| 5cLFdIzMhn8 | Amon Armath | Crack the Sky | Berserker | 2019 |
| m_m2oYJkx1A | Arch Enemy | Deceiver, Deceiver | Deceivers | 2022 |
| mjF1rmSV1dM | Arch Enemy | The Eagle Flies Alone | Will to Power | 2017 |
| O59JNz7rdIU | Archtects | A Match Made In Heaven | All Our Gods have Abandoned Us | 2016 |
| -jFgNreZPf0 | Asking Alexandria | Into the Fire | Asking Alexandria | 2017 |
| l7Fi8-7HRhc | Asking Alexandria | Not the American Average | Stand Up and Scream | 2009 |
| z71_E_YqWqA | Asking Alexandria | The Final Episode (Let's Change the Channel) | Stand Up and Scream | 2010 |
| Ql2THDlBD9g | Asking Alexandria | Vultures | Asking Alexandria | 2017 |
| W1l6izYwIhM | Attila | Pizza | Pizza | 2018 |
| gVC7f59ibI8 | Attila | Three 6 | Three 6 | 2017 |
| HKWqzjQAv14 | Behemoth | Ecclesia Diabolica Catholica | I Loved you at your Darkest | 2018 |
| UA_j_72psoo | Behemoth | O Father O Satan O Sun! | The Satanist | 2014 |
| g7yxjTcM7Bs | Behemoth | Wolves ov Siberia | I Loved you at your Darkest | 2018 |
| C7cczTyQ4iY | Bring me the Horizon | Go to Hell, For Heaven's Sake | Sempiternal | 2013 |
| AWggPLXeOkU | Bring me the Horizon | Pray for Pleagues | Count your Blessings | 2006 |
| q2I0ulTZWXA | Bullet for my Valentine | Waking the Demon | Scream Aim Fire | 2008 |
| 482tDopNzoc | Cannibal Corpse | Evisceration Plague | Evisceration Plague | 2009 |
| vlgiWBCbCJk | Cannibal Corpse | Hammer Smashed Face corpse Hammer | Tomb of the Mutilated | 1992 |
| Wks1aBh49sQ | Cradle of Filth | Crawling King Chaos | Existence is Futile | 2021 |
| DNRIaeg6EyY | Cradle of Filth | Heartbreak and Seance | Cryptoriana – The Seductiveness of Decay | 2017 |
| 04F4xlWSFh0 | Drowning Pool | Bodies | Sinner | 2001 |
| B4CcX720DW4 | Gojira | Amazonia | Fortitude | 2021 |
| tvmC7qxtQxs | Gojira | Into the Storm | Fortitude | 2021 |
| EkRrend3sIw | Gojira | The Chant | Fortitude | 2021 |
| uJRUq90EC_A | Hypocrisy | Chemical Whore | Worship | 2021 |
| 75xYN7VBiTY | In Flames | Alias | A Sense of Purpose | 2008 |
| FC3djB7-nc0 | Jinjer | Ape | Micro | 2019 |
| 7f353euyRno | Jinjer | Pit of Consciousness | Macro | 2019 |
| 2N0ShfOOEq4 | Killswitch Engage | The Signal Fire | Atonement | 2019 |
| Lm-sI1EB8BA | Killswitch Engage | Unleashed | Atonement | 2019 |
| lNwHjNz6My4 | Lamb of God | Checkmate | Lamb of God | 2020 |
| SnEXcv0YJQA | Lamb of God | Nevermore | Omens | 2022 |
| VHVsG2taJVs | Lamb of God | Omens | Omens | 2022 |
| GkoYsXDvL8s | Lamb of God | Wake up Dead | Omens | 2022 |
| 7Na3sECLYI8 | Motionless in White | 570 | Graveyard Shift | 2017 |
| Pj2miRJ6bZs | Motionless in White | Another Life | Disguise | 2019 |
| cIEc_11Aydc | Motionless in White | Disguise | Disguise | 2019 |
| TwO0zLLybQ0 | Motionless in White | Eternally Yours | Graveyard Shift | 2017 |
| CYG2kaZ5OfQ | Motionless in White | Undead Ahead 2: The Tale of the Midnight Ride | Disguise | 2019 |
| udeaeWGO4Is | Of Mice & Men | Earth & Sky | Earth and Sky | 2019 |
| AkFqg5wAuFk | Pantera | Walk | Vulgar Display of Power | 1992 |
| UpEHp6u0ZxU | Parkway Drive | Absolute Power | Reverence | 2018 |
| 4dBA2YxbFoE | Parkway Drive | Chronos | Reverence | 2018 |
| 4FTVDKo7kWY | Parkway Drive | I Hope you Rot | Reverence | 2018 |
| WL_8ZY89dP4 | Parkway Drive | Prey | Reverence | 2018 |
| lP6QplMvOBg | Parkway Drive | Shadow Boxing | Reverence | 2018 |
| 5uwyvvxNvqQ | Parkway Drive | Wishing Wells | Reverence | 2018 |
| wLoYIBEZEfw | Slipknot | All Out Life | We are not your Kind | 2019 |
| dymAGwL2kQI | Slipknot | The Chapeltown Rag | The End, so Far | 2022 |
| FukeNR1ydOA | Suicide Silence | Disengage | No Time to Bleed | 2009 |
| dWoQyC8_WtM | Suicide Silence | Unanswered | The Cleansing | 2007 |
| ds9s-pzGD0M | Suicide Silence | You only live Once | The Black Crown | 2011 |
| t2d3EDNDCn8 | Wage War | Low | Pressure | 2019 |
| lWo1N8Q0t9o | Wage War | Witness | Deadweight | 2017 |
| rbWFZMFlDIU | Whitechapel | I Will Find you | Kin | 2021 |
| eVI6c0TlM2g | Whitechapel | The Saw is the Law | Our Endless War | 2014 |
| W72Lnz1n-jw | Whitechapel | When a Demon Defiles a Witch | The Valley | 2019 |

#### Initial Data Collection and Normalization
The data was collected from the YouTube playlist above and trimmed using the timestamps provided in the dataset.

The audio files were passed through the [Spleeter](https://joss.theoj.org/papers/10.21105/joss.02154) (Hennequin et al., 2020) source separation algorithm to separate the vocals from the other components.

### Licensing Information

MIT License

Copyright (c) 2023 Juan Pablo Díaz

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

```
@article{
Hennequin2020,
doi = {10.21105/joss.02154},
url = {https://doi.org/10.21105/joss.02154},
year = {2020}, publisher = {The Open Journal},
volume = {5}, number = {50}, pages = {2154},
author = {Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam},
title = {Spleeter: a fast and efficient music source separation tool with pre-trained models},
journal = {Journal of Open Source Software}
}
```

原始信息汇总

数据集概述

数据集名称

"Gutural Speech Recognition"

数据集特征

• audio: 音频文件
• text: 转录的歌词
• song_name: 歌曲名称
• artist_name: 艺术家名称
• album_name: 专辑名称
• release_year: 发行年份
• video_id: YouTube视频ID
• timestamp_start: 音频片段开始时间
• timestamp_end: 音频片段结束时间
• sample_rate: 音频采样率

数据集分割

• train: 1740个样本，总大小1259147118.2099998字节
• test: 580个样本，总大小403875517.75字节

数据集大小

• 下载大小: 1629538009字节
• 数据集总大小: 1663022635.9599998字节

许可证

MIT License

任务类别

• automatic-speech-recognition

语言

• en

标签

• music

大小类别

• 1K<n<10K

数据集描述

包含57首歌曲的尖叫和喉音转录，主要来自重金属音乐。