Datasets:
File size: 2,922 Bytes
f3bad19 dcb71db f3bad19 8b477b5 d23c839 5767e79 43e58a9 5767e79 ba4f51d dcb71db 891a66b d23c839 8b477b5 8bfdf6b 8b477b5 f78bf44 d017675 f78bf44 1282903 f78bf44 b0df395 f57f9a9 8b477b5 265a800 8b477b5 265a800 8b477b5 265a800 8b477b5 265a800 3c97952 265a800 ba23be3 265a800 8b477b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
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](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.
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.
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](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"])
```
By default, the dataset includes the audio, allowing you to run transcription directly.
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"]
``` |