license: cc-by-4.0
task_categories:
- text-classification
language:
- ar
size_categories:
- 10K<n<600K
pretty_name: 'Habibi: A Multi-Dialect, Multi-National Arabic Song Lyrics Corpus'
dataset_info:
features:
- name: songID
dtype: int32
- name: Singer
dtype: string
- name: SongTitle
dtype: string
- name: SongWriter
dtype: string
- name: Composer
dtype: string
- name: LyricsOrder
dtype: int32
- name: Lyrics
dtype: string
- name: SingerNationality
dtype: string
- name: SongDialect
dtype: string
splits:
- name: train
- name: validation
- name: test
Habibi: A Multi-Dialect, Multi-National Arabic Song Lyrics Corpus
Habibi is the first large-scale open corpus of Arabic song lyrics, comprising more than 30,000 songs performed by artists from 18 Arab countries. The collection represents six major Arabic dialect groups, segmented into over 520,000 verses and containing more than 3.5 million words.
The corpus was designed to support computational research in Arabic dialect identification, country-of-origin classification, authorship analysis, and wider studies in Arabic language variation within contemporary music.
🌍 Corpus Overview
The Habibi corpus brings together lyrics from across the Arab world, reflecting the linguistic and cultural diversity of modern Arabic music. Each song includes:
- Singer name
- Song title
- Country of origin
- Dialect category
(Egyptian, Gulf, Levantine, Iraqi, Sudanese, Maghrebi) - Writer and composer (where available)
- Lyrics segmented into verses
All lyrics were collected using a Web-as-Corpus approach and manually curated to maintain a clean, noise-free dataset free of emojis, hashtags, and social-media artefacts.
Key Statistics
| Item | Count |
|---|---|
| Songs | 30,072 |
| Verses (sentences) | 527,870 |
| Words | 3.57 million |
| Singers | 1,765 |
| Writers | 3,789 |
| Composers | 2,463 |
| Countries | 18 |
| Dialects | 6 |
🗂 Dialects and Geographic Coverage
Songs are categorised into six dialect groups widely recognised across modern Arabic music:
- Egyptian
- Gulf
- Levantine
- Iraqi
- Sudanese
- Maghrebi
Although singers often perform in dialects other than their own, the corpus assigns dialect based on the singer’s country of origin, following conventions used by major Arabic music platforms (Anghami, Spotify, Deezer).
📁 File Formats
The corpus is distributed in several machine-readable formats to support a range of NLP tasks:
1. CSV (primary format)
Contains all metadata fields and verse-level text in UTF-8.
2. Annotated TXT
Each song is provided as a structured, UTF-8 annotated document with lightweight XML-style tags identifying metadata and verse boundaries.
3. JSON & XML
Converted directly from the CSV, preserving all metadata and text segmentation.
🔧 Recommended Use-Cases
Researchers may use Habibi to explore:
- Arabic dialect identification (sentence- or song-level)
- Country-of-origin classification
- Authorship attribution (singers, songwriters)
- Linguistic variation in modern Arabic music
- Embeddings training using domain-specific lyrics
- Sociolinguistic analysis of themes, vocabulary, and regional patterns
The dataset has demonstrated strong performance across classical and deep-learning models, including CNN, LSTM, CLSTM, BiGRU, and BiLSTM architectures.
📝 Data Split
This dataset is provided with standard train/validation/test splits created using an 80/10/10 random split from the master file habibi.csv.
import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv("habibi.csv")
train, temp = train_test_split(df, test_size=0.2, random_state=42)
val, test = train_test_split(temp, test_size=0.5, random_state=42)
train.to_csv("train.csv", index=False)
val.to_csv("validation.csv", index=False)
test.to_csv("test.csv", index=False)
📊 Benchmarks
Extensive experiments were conducted on:
- Binary dialect classification
- Multi-class dialect classification (3–6 classes)
- Binary country-of-origin classification
- Multi-class country classification (3–18 classes)
Models tested include:
- Naïve Bayes, Logistic Regression, SVM
- CNN, LSTM, BiLSTM, CLSTM, BiGRU
- FastText Arabic word embeddings
- In-house Habibi CBOW embeddings (300-dimensional)
The word-level CNN model achieved the strongest deep-learning results, while Naïve Bayes consistently performed best among classical models on multi-class tasks.
📦 Download
All formats (CSV, TXT, JSON, XML) as well as Habibi’s in-house word embeddings are available for free research use.
Original repository:
http://ucrel-web.lancaster.ac.uk/habibi/
📝 Citation
If you use the Habibi corpus, please cite:
El-Haj, M. (2020).
Habibi – a multi-dialect multi-national Arabic song lyrics corpus.
In Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC 2020), pp. 1318–1326.
URL: https://www.lancaster.ac.uk/staff/elhaj/docs/habibi.pdf
BibTeX
@inproceedings{elhaj2020habibi,
title={Habibi--a multi dialect multi national Arabic song lyrics corpus},
author={El-Haj, Mahmoud},
booktitle={Proceedings of the Twelfth Language Resources and Evaluation Conference},
pages={1318--1326},
year={2020}
}