metadata

task_categories:
  - automatic-speech-recognition
multilinguality:
  - multilingual
language:
  - en
  - fr
  - de
  - es
tags:
  - music
  - lyrics
  - evaluation
  - benchmark
  - transcription
pretty_name: 'JamALT: A Formatting-Aware Lyrics Transcription Benchmark'

JamALT: A Formatting-Aware Lyrics Transcription Benchmark

Dataset description

Project page: https://audioshake.github.io/jam-alt/
Source code: https://github.com/audioshake/alt-eval
Paper: https://ismir2023program.ismir.net/lbd_343.html

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.

See the project website for details.

Loading the data

from datasets import load_dataset
dataset = load_dataset("audioshake/jam-alt")["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.

Other arguments can be specified to control audio loading:

with_audio=False to skip loading audio.
sampling_rate and mono=True to control the sampling rate and number of channels.
decode_audio=False to skip decoding the audio and just get the MP3 file paths.

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="v1.0.0")["test"]
# transcriptions: list[str]
compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])

By default, the dataset includes the audio, allowing you to run transcription directly. For example, the following code can be used to evaluate Whisper:

dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["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:

dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", with_audio=False)["test"]