Datasets:

Sebssihakim
/

Clean_One_Speaker_STT_Split_EN_AR

license: cc-by-nc-sa-4.0
language:
  - ar
  - en
task_categories:
  - automatic-speech-recognition
pretty_name: Clean One-Speaker STT (AR dialectal + MSA + EN)
size_categories:
  - 10K<n<100K

Clean_One_Speaker_STT_Split_EN_AR

Mixed STT dataset — Saudi dialectal Arabic + Modern Standard Arabic + English — with train/validation/test splits, built for fine-tuning multilingual ASR (e.g. Whisper) without catastrophic forgetting of English.

Composition

Dialectal Arabic (~72%) — Sebssihakim/Clean_One_Speaker_SADA_Split (cleaned single-speaker SADA; SDAIA / Saudi Broadcasting Authority, CC BY-NC-SA 4.0)
MSA (~8%) — FLEURS ar_eg, full official splits (CC-BY; read MSA), plus the MSA rows already present in SADA
English (~17%) — LibriSpeech clean (CC-BY), sampled from its official splits (train.100 / validation / test); transcripts lowercased

Columns

audio (16 kHz), text, language (ar/en), dialect (SADA dialect name, ModernStandardArabic, or English)

Split methodology

SADA partitions reused as-is (stratified by dialect, seed 42, Maghrebi removed). FLEURS and LibriSpeech samples come from their own official splits (seed 42), so no cross-split leakage is possible. SADA splits are segment-level: the same show/speaker may appear in more than one split.

License & attribution

SADA is CC BY-NC-SA 4.0 (SDAIA & Saudi Broadcasting Authority); this derivative keeps the same license. LibriSpeech (Panayotov et al., 2015) and FLEURS (Conneau et al., 2022) are CC-BY 4.0.