Datasets:

Salesteq
/

arabic-dialect-text

license: other
license_name: mixed-source-research-use
language:
  - ar
task_categories:
  - text-to-speech
  - automatic-speech-recognition
tags:
  - arabic
  - dialectal-arabic
  - saudi
  - tts
  - phonemization
  - ipa
  - code-switching
pretty_name: Arabic Dialectal Text (gathered, lang-coded, IPA-enriched)
private: true

Arabic Dialectal Text — gathered, lang-coded, IPA-enriched

A deduplicated collection of dialectal Arabic sentences assembled from openly-downloadable sources, every line tagged with a BCP-47 lang code. Saudi Arabic is the focus, but all labelled dialects are retained. Built as the text side of a Saudi TTS / phonemizer pipeline.

Files

all.tsv — the corpus: id<TAB>lang<TAB>source<TAB>text.
all.enriched.tsv — adds two phonetic columns: id<TAB>lang<TAB>source<TAB>text<TAB>diac<TAB>ipa, where
- diac is dialect-aware diacritization (arbtok rawi-lattice fusion: the bundled rawi ensemble scores dialect-licensed hypotheses under a joint beam; written diacritics are hard constraints), and
- ipa is arbtok's pausal dialect-aware transcription of the sentence, routed by the line's lang code — e.g. قهوة → Najdi ɡahawa (ar-SA), Egyptian ʔahwa (ar-EG), MSA qahwa. Pipeline accuracy on the blind-verified Arabic gold: PER 0.015 on vocalized input, 0.177 on bare input (vs 0.357 for espeak-ng ar).
summary.txt, provenance.json — per-lang / per-source counts, licenses, and status.

Split by dialect by filtering the lang column (e.g. ar-SA, ar-EG, ar-x-lav).

Provenance and licensing

Sources vary in license (several research-use or CC-BY-NC; some unspecified) — see provenance.json. This repo is private and intended for internal research use; verify each source's terms before any redistribution. The diac and ipa columns are provisional (the diacritizer is MSA-trained; dialect phonology is approximate — Saudi is routed to Najdi) and are meant as input to human phonetic QA, not as final labels.