README.md

---
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:
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    dtype: string
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  - name: license_type
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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](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"]
```

## Citation

When using the benchmark, please cite [our paper](https://www.arxiv.org/abs/2408.06370) as well as the original [JamendoLyrics paper](https://arxiv.org/abs/2306.07744):
```bibtex
@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}
}
```