jpdiazpardo/guturalScream_metalVocals

Dataset Card for "Gutural Speech Recognition"

This dataset contains annotations of 57 songs.

How to use

Load the dataset from huggingface in your notebook:

!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

Source Data

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 (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}
}