Datasets:
task_categories:
- automatic-speech-recognition
multilinguality:
- multilingual
language:
- en
- fr
- de
- es
tags:
- music
- lyrics
- evaluation
- benchmark
- transcription
- pnc
pretty_name: 'Jam-ALT: A Readability-Aware Lyrics Transcription Benchmark'
paperswithcode_id: jam-alt
configs:
- config_name: all
data_files:
- split: test
path:
- metadata.jsonl
- subsets/*/audio/*.mp3
default: true
- config_name: de
data_files:
- split: test
path:
- subsets/de/metadata.jsonl
- subsets/de/audio/*.mp3
- config_name: en
data_files:
- split: test
path:
- subsets/en/metadata.jsonl
- subsets/en/audio/*.mp3
- config_name: es
data_files:
- split: test
path:
- subsets/es/metadata.jsonl
- subsets/es/audio/*.mp3
- config_name: fr
data_files:
- split: test
path:
- subsets/fr/metadata.jsonl
- subsets/fr/audio/*.mp3
Jam-ALT: A Readability-Aware Lyrics Transcription Benchmark
Dataset description
- Project page: https://audioshake.github.io/jam-alt/
- Source code: https://github.com/audioshake/alt-eval
- Paper (ISMIR 2024): https://doi.org/10.5281/zenodo.14877443
- Paper (arXiv): https://arxiv.org/abs/2408.06370
- Extended abstract (ISMIR 2023 LBD): https://arxiv.org/abs/2311.13987
- Related datasets: https://huggingface.co/jamendolyrics
Jam-ALT is a revision of the JamendoLyrics dataset (79 songs in 4 languages), intended for use as an automatic lyrics transcription (ALT) benchmark.
It has been published in the ISMIR 2024 paper (full citation below):
📄 Lyrics Transcription for Humans: A Readability-Aware Benchmark
👥 O. Cífka, H. Schreiber, L. Miner, F.-R. Stöter
🏢 AudioShake
The lyrics have been revised according to the newly compiled annotation guidelines, which include rules about spelling and formatting, as well as punctuation and capitalization (PnC). The audio is identical to the JamendoLyrics dataset.
💥 New: The dataset now has line-level timings. They were added in the paper 📄 Exploiting Music Source Separation for Automatic Lyrics Transcription with Whisper by J. Syed, I. Meresman-Higgs, O. Cífka, and M. Sandler, presented at the 2025 ICME Workshop AI for Music.
Note: The dataset is not time-aligned at the word level. To evaluate automatic lyrics alignment (ALA), please use JamendoLyrics, which is the standard benchmark for that task.
See the project website for details and the JamendoLyrics community for related datasets.
Loading the data
from datasets import load_dataset
dataset = load_dataset("jamendolyrics/jam-alt", split="test")
A subset is defined for each language (en, fr, de, es);
for example, use load_dataset("jamendolyrics/jam-alt", "es") to load only the Spanish songs.
To control how the audio is decoded, cast the audio column using dataset.cast_column("audio", datasets.Audio(...)).
Useful arguments to datasets.Audio() are:
sampling_rateandmono=Trueto control the sampling rate and number of channels.decode=Falseto skip decoding the audio and just get the MP3 file paths and contents.
Running the benchmark
The evaluation is implemented in our alt-eval package:
from datasets import load_dataset
from alt_eval import compute_metrics
dataset = load_dataset("jamendolyrics/jam-alt", revision="v1.3.0", split="test")
# transcriptions: list[str]
compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
For example, the following code can be used to evaluate Whisper:
dataset = load_dataset("jamendolyrics/jam-alt", revision="v1.3.0", split="test")
dataset = dataset.cast_column("audio", datasets.Audio(decode=False)) # Get the raw audio file, let Whisper decode it
model = whisper.load_model("tiny")
transcriptions = [
"\n".join(s["text"].strip() for s in model.transcribe(a["path"])["segments"])
for a in dataset["audio"]
]
compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
Alternatively, if you already have transcriptions, you might prefer to skip loading the audio column:
dataset = load_dataset("jamendolyrics/jam-alt", revision="v1.3.0", split="test").remove_columns("audio")
Citation
When using the benchmark, please cite our ISMIR paper as well as the original JamendoLyrics paper. For the line-level timings, please cite the ICME workshop paper.
@inproceedings{cifka-2024-jam-alt,
author = {Ond{\v{r}}ej C{\'{\i}}fka and
Hendrik Schreiber and
Luke Miner and
Fabian{-}Robert St{\"{o}}ter},
title = {Lyrics Transcription for Humans: {A} Readability-Aware Benchmark},
booktitle = {Proceedings of the 25th International Society for
Music Information Retrieval Conference},
pages = {737--744},
year = 2024,
publisher = {ISMIR},
doi = {10.5281/ZENODO.14877443},
url = {https://doi.org/10.5281/zenodo.14877443}
}
@inproceedings{durand-2023-contrastive,
author={Durand, Simon and Stoller, Daniel and Ewert, Sebastian},
booktitle={2023 {IEEE} International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages},
year={2023},
pages={1-5},
address={Rhodes Island, Greece},
doi={10.1109/ICASSP49357.2023.10096725}
}
@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 = {to appear}
}
Contributions
The transcripts, originally from the JamendoLyrics dataset, were revised by Ondřej Cífka, Hendrik Schreiber, Fabian-Robert Stöter, Luke Miner, Laura Ibáñez, Pamela Ode, Mathieu Fontaine, Claudia Faller, April Anderson, Constantinos Dimitriou, and Kateřina Apolínová. Line-level timings were automatically transferred from JamendoLyrics and manually corrected by Ondřej Cífka and Jaza Syed to fit the revised transcripts.