File size: 3,546 Bytes
418d7c9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4899028
 
 
 
 
 
 
 
 
 
 
 
418d7c9
4899028
 
 
f4e0f01
4899028
 
 
 
 
 
 
 
 
 
 
dcb502e
 
 
 
 
4899028
 
 
 
 
 
 
 
 
be3fe1b
 
 
4899028
 
 
4aafd28
 
9bc8458
 
edd88c2
 
4aafd28
4899028
 
 
 
dcb502e
 
 
 
 
 
 
 
4899028
 
 
51d81cd
4899028
 
 
 
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
---
dataset_info:
  config_name: full_dataset
  features:
  - name: audio
    dtype: audio
  - name: filename
    dtype: string
  - name: duration
    dtype: string
  - name: country
    dtype: string
  - name: city
    dtype: string
  - name: msa_or_dialect
    dtype: string
  - name: emotion
    dtype: string
  - name: keep_or_skip
    dtype: string
  - name: confidence
    dtype: string
  - name: audio_type
    dtype: string
  - name: annotators
    dtype: string
  - name: timestamp
    dtype: string
  splits:
  - name: train
    num_bytes: 4191724917
    num_examples: 6907
  download_size: 3980038261
  dataset_size: 4191724917
configs:
- config_name: full_dataset
  data_files:
  - split: train
    path: full_dataset/train-*
license: cc-by-4.0
task_categories:
- audio-classification
language:
- ar
tags:
- arabic
- dialect-identification
- speech
- radio
- geolocation
pretty_name: ARCADE
---

# ARCADE: Arabic Radio Corpus for Audio Dialect Evaluation

ARCADE is a city-scale corpus of Arabic radio speech designed for fine-grained dialect identification. The dataset contains 6,907 annotations for 3,790 unique audio segments collected from radio streams spanning 58 cities across 19 Arab countries.

## Dataset Description

Each 30-second audio clip is annotated with:
- **City and Country**: Fine-grained geographic labels at the city level
- **MSA or Dialect**: Whether the speech is Modern Standard Arabic, dialectal, mixed, or not applicable
- **Emotion**: Speaker emotion (neutral, happiness, anger, etc.)
- **Audio Type**: Single speaker, multiple speakers, music/no speech, or Quran recitation
- **Keep or Skip**: Whether the clip is suitable for dialect modeling
- **Confidence**: Annotator confidence level (sure, unsure, no idea)

The filename alone is not a unique identifier and may appear across multiple cities. To obtain a unique key, concatenate the filename with the corresponding city name.

A detailed description of the dataset is provided in the accompanying paper: [https://arxiv.org/abs/2601.02209](https://arxiv.org/abs/2601.02209)


## Intended Uses

- Fine-grained Arabic dialect identification at the city level
- Sociolinguistic studies of regional speech variation
- Multi-task learning combining dialect, emotion, and speaker classification
- Robustness evaluation under domain and channel shift

## Dataset Statistics

- **Total annotations**: 6,907
- **Total unique audio segments**: 3,790
- **Cities**: 58
- **Countries**: 19
- **Clip duration**: 30 seconds

## Usage

```
from datasets import load_dataset
ds = load_dataset("riotu-lab/ARCADE-full")
```

## Citation

If you use this dataset, please cite:
```bibtex
@misc{nacar2026arcadecityscalecorpusfinegrained,
      title={ARCADE: A City-Scale Corpus for Fine-Grained Arabic Dialect Tagging}, 
      author={Omer Nacar and Serry Sibaee and Adel Ammar and Yasser Alhabashi and Nadia Samer Sibai and Yara Farouk Ahmed and Ahmed Saud Alqusaiyer and Sulieman Mahmoud AlMahmoud and Abdulrhman Mamdoh Mukhaniq and Lubaba Raed and Sulaiman Mohammed Alatwah and Waad Nasser Alqahtani and Yousif Abdulmajeed Alnasser and Mohamed Aziz Khadraoui and Wadii Boulila},
      year={2026},
      eprint={2601.02209},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.02209}, 
}
```

📄 **Paper**: [arXiv:2601.02209](https://arxiv.org/abs/2601.02209) 

## License

This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license for non-commercial academic and research use.