Datasets:
task_categories:
- automatic-speech-recognition
multilinguality:
- multilingual
language:
- en
- fr
- de
- es
tags:
- music
- lyrics
- evaluation
- benchmark
- transcription
pretty_name: 'JamALT: A Readability-Aware Lyrics Transcription Benchmark'
paperswithcode_id: jam-alt
dataset_info:
- config_name: all
features:
- name: name
dtype: string
- name: text
dtype: string
- name: language
dtype: string
- name: license_type
dtype: string
- name: audio
dtype: audio
splits:
- name: test
num_bytes: 409411912
num_examples: 79
download_size: 409150043
dataset_size: 409411912
- config_name: de
features:
- name: name
dtype: string
- name: text
dtype: string
- name: language
dtype: string
- name: license_type
dtype: string
- name: audio
dtype: audio
splits:
- name: test
num_bytes: 107962802
num_examples: 20
download_size: 107942102
dataset_size: 107962802
- config_name: en
features:
- name: name
dtype: string
- name: text
dtype: string
- name: language
dtype: string
- name: license_type
dtype: string
- name: audio
dtype: audio
splits:
- name: test
num_bytes: 105135091
num_examples: 20
download_size: 105041371
dataset_size: 105135091
- config_name: es
features:
- name: name
dtype: string
- name: text
dtype: string
- name: language
dtype: string
- name: license_type
dtype: string
- name: audio
dtype: audio
splits:
- name: test
num_bytes: 105024257
num_examples: 20
download_size: 104979012
dataset_size: 105024257
- config_name: fr
features:
- name: name
dtype: string
- name: text
dtype: string
- name: language
dtype: string
- name: license_type
dtype: string
- name: audio
dtype: audio
splits:
- name: test
num_bytes: 91289764
num_examples: 19
download_size: 91218543
dataset_size: 91289764
configs:
- config_name: all
data_files:
- split: test
path: parquet/all/test-*
default: true
- config_name: de
data_files:
- split: test
path: parquet/de/test-*
- config_name: en
data_files:
- split: test
path: parquet/en/test-*
- config_name: es
data_files:
- split: test
path: parquet/es/test-*
- config_name: fr
data_files:
- split: test
path: parquet/fr/test-*
JamALT: 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://www.arxiv.org/abs/2408.06370
- Extended abstract (ISMIR 2023 LBD): https://arxiv.org/abs/2311.13987
JamALT is a revision of the JamendoLyrics dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark.
The lyrics have been revised according to the newly compiled annotation guidelines, which include rules about spelling, punctuation, and formatting.
The audio is identical to the JamendoLyrics dataset.
However, only 79 songs are included, as one of the 20 French songs (La_Fin_des_Temps_-_BuzzBonBon) has been removed due to concerns about potentially harmful content.
Note: The dataset is not time-aligned as it does not easily map to the timestamps from JamendoLyrics. To evaluate automatic lyrics alignment (ALA), please use JamendoLyrics directly.
See the project website for details.
Loading the data
from datasets import load_dataset
dataset = load_dataset("audioshake/jam-alt", split="test")
A subset is defined for each language (en, fr, de, es);
for example, use load_dataset("audioshake/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.
The load_dataset function also accepts a columns parameter, which can be useful for example if you want to skip downloading the audio (see the example below).
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("audioshake/jam-alt", revision="v1.1.0", 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("audioshake/jam-alt", revision="v1.1.0", 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("audioshake/jam-alt", revision="v1.1.0", split="test", columns=["name", "text", "language", "license_type"])
Citation
When using the benchmark, please cite our paper as well as the original JamendoLyrics paper:
@misc{cifka-2024-jam-alt,
author = {Ond\v{r}ej C\'ifka and
Hendrik Schreiber and
Luke Miner and
Fabian-Robert St\"oter},
title = {Lyrics Transcription for Humans: A Readability-Aware Benchmark},
booktitle = {Proceedings of the 25th International Society for
Music Information Retrieval Conference},
year = 2024,
publisher = {ISMIR},
note = {to appear; preprint arXiv:2408.06370}
}
@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}
}