README.md

---
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'
paperswithcode_id: jam-alt
---

# 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://arxiv.org/abs/2311.13987

JamALT is a revision of the [JamendoLyrics](https://github.com/f90/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](GUIDELINES.md), 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](https://audioshake.github.io/jam-alt/) for details.

## Loading the data

```python
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.

By default, the dataset comes with audio. To skip loading the audio, use `with_audio=False`.
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_rate` and `mono=True` to control the sampling rate and number of channels.
- `decode=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](https://github.com/audioshake/alt-eval):
```python
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"])
```

For example, the following code can be used to evaluate Whisper:
```python
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:
```python
dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", with_audio=False)["test"]
```