datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
arabic-msa-25k-saudi-male-tashkeel
Arabic MSA 25K — Saudi Male (Tashkeel)
25,000 fully-diacritized Arabic MSA text + audio pairs, rendered with a single
Saudi male neural voice at 48 kHz / 16-bit PCM, across 10 thematic categories.
Dataset Summary
arabic-msa-25k-saudi-male-tashkeel is a 25,000-clip Modern Standard Arabic (MSA)
speech corpus with matching diacritized text (full tashkeel / ḥarakāt). Every clip
is synthesized by the single voice ar-SA-HamedNeural (Azure Neural TTS, Saudi
Arabic male) at 48… See the full description on the dataset page: https://huggingface.co/datasets/HeshamHaroon/arabic-msa-25k-saudi-male-tashkeel.egyptian-arabic-tts-diacritized
Egyptian Arabic TTS Corpus (Diacritized)
97,163 utterances / ~334 hours of Egyptian Arabic speech at 24 kHz, with
diacritized transcripts — the short vowels that Arabic script does not
write.
Why diacritics
Arabic is an abjad: short vowels are unwritten, so كتب may be kataba,
kutiba, or kutub. A TTS model with no Arabic pretraining cannot infer which,
and guesses — which native listeners hear as a foreign accent with constant
mispronunciation.
This is invisible to… See the full description on the dataset page: https://huggingface.co/datasets/Rabe3/egyptian-arabic-tts-diacritized.Moroccan-Arabic-Multimodal-Emotion-Recognition
MDER-MA — Moroccan Arabic Multimodal Emotion Recognition (TTS-aligned repackaging)
A repackaging of the MDER-MA dataset that pairs every audio clip with its Arabic (Moroccan dialect / Darija) transcript and ships speaker-disjoint train/validation/test splits.
Original dataset: Ouali, S. & El Garouani, S. (2025). MDER-MA: A multimodal dataset for emotion recognition in low-resource Moroccan Arabic language. Data in Brief. DOI: 10.1016/j.dib.2025.112005. Mendeley:… See the full description on the dataset page: https://huggingface.co/datasets/FatimahEmadEldin/Moroccan-Arabic-Multimodal-Emotion-Recognition.Arabic-Emotional-Audio-Dataset-Baved
BAVED — Basic Arabic Vocal Emotions Dataset (TTS-ready repackaging)
A re-packaged, transcript-aligned version of the Basic Arabic Vocal Emotions Dataset (BAVED) with explicit Arabic transcripts, English glosses, speaker metadata, and speaker-disjoint train/validation/test splits.
Original dataset: Aouf Yacine, Basic Arabic Vocal Emotions Dataset (BAVED), GitHub: https://github.com/40uf411/Basic-Arabic-Vocal-Emotions-Dataset. This repackaging adds metadata; all audio is unchanged.… See the full description on the dataset page: https://huggingface.co/datasets/FatimahEmadEldin/Arabic-Emotional-Audio-Dataset-Baved.arabic-english-code-switching-review-annotations
Review Annotations for Arabic-English Code-Switching Speech
This metadata-only dataset publishes review decisions and transcript-correction deltas for MohamedRashad/arabic-english-code-switching. It contains no human audio, no local file paths, no raw review notes, and no copies of unchanged upstream transcripts.
The annotations are pinned to upstream revision 4a3bffc45219c35949470de32b8d4cb328b0ce11 and join by upstream_row_index.
Coverage and outcomes
The… See the full description on the dataset page: https://huggingface.co/datasets/abdo1819/arabic-english-code-switching-review-annotations.egyptian-arabic-stt-data
Egyptian Arabic STT Dataset
Synthetic Egyptian Arabic speech dataset generated by the
Synthetic Egyptian Speech Data Pipeline.
Samples are human-reviewed and quality-validated using Whisper ASR (WER/CER).
Dataset Statistics
Metric
Value
Total samples
50
Total duration
85.2s (0.02h)
Dialect validated
50 / 50
Average WER
0.4136
Average CER
0.1642
Topics
food_ordering
Fields
Field
Type
Description
id
string
Deterministic… See the full description on the dataset page: https://huggingface.co/datasets/Khaledtelbahnasy/egyptian-arabic-stt-data.droidnexus-arabic-editorial-speech-scorecard-mini
DroidNexus Arabic Editorial Speech Scorecard Mini
A public DroidNexus Labs scorecard dataset for Arabic speech workflows: representative editorial scenarios, latency targets, overlap pressure, and the metric stack that decides whether a transcript is usable.
Why this exists
This dataset is the first public speech artifact layer for DroidNexus Labs. It publishes representative editorial workloads and evaluation pressure before claiming a full source-audio benchmark.… See the full description on the dataset page: https://huggingface.co/datasets/driodnexus/droidnexus-arabic-editorial-speech-scorecard-mini.