TutlAit v1: a crowdsourced Moroccan Tamazight speech dataset with Arabic transcriptions and regional accent labels
Authors: Mohamed-Amine Chadi, Ezzahra Ait El Arbi, Ismail Khayoub, Aymane Fadili, Yassine Ennhili, Jadjigua Bouali, Hanane Inhid, Mohammed Ameksa, Hajar Mousannif
Organizations: Department of Computer Science, Faculty of Sciences Semlalia, Cadi Ayyad University, Boulevard Prince My Abdellah, B.P. 2390, Marrakech, 40000, Morocco.
Tamazight (Amazigh) is, together with Arabic, one of the two official languages of Morocco, yet it remains severely under-resourced for speech technology: pub licly available labelled audio is scarce, generally lacks information on the regional variety spoken, and is often of uneven transcription quality. This article describes the TutlAit dataset, a corpus of Moroccan Tamazight speech paired with Modern Standard Arabic text and explicit regional accent labels. The data were collected with TutlAit, a purpose-built crowdsourcing web application (React 18 front end, Django 5 / Django REST Framework back-end, PostgreSQL database). Native speakers recruited through targeted LinkedIn and Instagram campaigns created an account, declared their regional variety (Atlas, Souss, Rif or other) and demographic information, and then contributed through two workflows: Text-to Audio, in which an Arabic sentence is displayed and the volunteer records its oral Tamazight rendering in the browser, and Audio-to-Text, in which a Tamazight excerpt is played and the volunteer types its Arabic transcription. A complemen tary set of segments was obtained from freely accessible Tamazight audiovisual media, segmented and annotated with ELAN and imported through a bulk CSV/ZIP pipeline. Every upload is converted server-side to 16kHz mono WAV, hashed with SHA-256 for duplicate rejection, checked for duration bounds and validated by an administrator. The dataset contains 13,384 audio files totalling 75,231 seconds (approximately 20.9 hours, about 3.01GB). The Atlas variety accounts for 9,956 files (14.08h) and the Souss variety for 3,378 files (6.75h); small Rif (22 files) and Kabyle (28 files) subsets are also included. The corpus can be reused for speech recognition, speech translation and accent identification for Moroccan Tamazight.
Figures & tables
Column
Type
Description
annotation_id
integer
Unique identifier of the contribution; matches the WAV file name.
audio_path
string
Relative path of the WAV file inside tutlait_audio.zip .
text
string
Modern Standard Arabic text (source sentence for Text-to-Audio, transcription for Audio-to-Text).
accent
categorical
Regional variety: atlas , souss , rif , kabyle .
mode
categorical
Workflow: text_to_audio , audio_to_text or bulk_import .
duration_s
float
Clip duration in seconds.
Table 1: Columns of the metadata index metadata.csv .
Metric
Value
Number of audio files
13,384
Total duration (seconds)
75,231
Total duration (hours)
≈ 20.9 h
Total size
≈ 3.01 GB
Mean duration per file
5.62 s
Median duration per file
4.03 s
Table 2: Global statistics of the TutlAit dataset.
Duration interval (s)
Number of files
Percentage
(0, 2]
3,153
23.56%
(2, 4]
3,487
26.05%
(4, 6]
2,442
18.25%
(6, 8]
1,590
11.88%
(8, 10]
844
6.31%
(10, 15]
1,049
7.84%
Table 3: Distribution of audio files by duration.
Figure 1: Distribution of audio file durations in the TutlAit dataset.
Regional variety
Number of files
Percentage
Atlas
9,956
74.39%
Souss
3,378
25.24%
Kabyle
28
0.21%
Rif
22
0.16%
Total
13,384
100%
Table 4: Number of audio files per regional variety.
Regional variety
Total duration (hours)
Percentage
Atlas
14.08
67.61%
Souss
6.75
32.42%
Rif
0.05
0.24%
Kabyle
0.02
0.10%
Total
20.90
100%
Table 5: Total recorded duration per regional variety.
Figure 2: Number of audio files per regional variety.
Figure 3: Total recorded duration (hours) per regional variety.
Figure 4: Overall workflow of corpus collection, segmentation and annotation.
Figure 5: General architecture of the TutlAit platform.