task_categories:
- automatic-speech-recognition
language:
- en
tags:
- music
- lyrics
- evaluation
- benchmark
- transcription
pretty_name: MUSDB-ALT
license: cc-by-nc-sa-4.0
dataset_info:
features:
- name: name
dtype: string
- name: text
dtype: string
- name: text_tagged
dtype: string
- name: lines
list:
- name: start
dtype: float64
- name: end
dtype: float64
- name: text
dtype: string
- name: text_tagged
dtype: string
splits:
- name: test
num_bytes: 141507
num_examples: 39
download_size: 80501
dataset_size: 141507
configs:
- config_name: default
data_files:
- split: test
path: data/test.jsonl
Dataset Card for MUSDB-ALT
This dataset contains long-form lyric transcripts following the Jam-ALT guidelines
for the test set of the dataset MUSDB18, with line-level timings.
There are two versions of each transcript at the song and line level - text contains the normal transcript, and text_tagged contains the transcript with non-lexical
vocables enclosed in tags <nl> and </nl>.
Dataset Details
The dataset was constructed manually, based on the MUSDB18 lyrics extension as a starting point. The lyrics extension contains transcripts of the 45 English language songs out of the 50 in the MUSDB18 test set. We annotated 39 of those 45 songs, excluding 6 for the following reasons:
- Signe Jakobsen - What Have You Done To Me : Three overlapping vocal lines that could not be separated into lead and backing vocals
- PR - Happy Daze : Vocal content primarily from highly processed vocal samples
- PR - Oh No : Vocal content primarily from highly processed vocal samples
- Skelpolu - Resurrection : Vocal content primarily from highly processed vocal samples
- Timboz - Pony : Lyrics unintelligble due to screamed enunciation style
- Triviul feat The Fiend - Widows : Three overlapping vocal lines that could not be separated into lead and backing vocals
Dataset Description
Paper: The dataset was introduced in the paper Exploiting Music Source Separation for Automatic Lyrics Transcription with Whisper" published at the Workshop Artificial Intelligence For Music at ICME 2025
- Funding: This work was supported by InnovateUK [Grant Number 1010280]
- License: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
Citation
BibTeX:
@inproceedings{syed-2025-mss-alt,
author = {Jaza Syed and
Ivan Meresman-Higgs and
Ond{\v{r}}ej C{\'{\i}}fka and
Mark Sandler},
title = {Exploiting Music Source Separation for Automatic Lyrics Transcription with {Whisper}},
booktitle = {2025 {IEEE} International Conference on Multimedia and Expo Workshops (ICMEW)},
publisher = {IEEE},
year = {2025},
note = {In press}
}