arbtok 0.0.0a4

Rule-based Arabic (MSA) text-to-IPA with tashkeel diacritization — an orthography2ipa G2P plugin

arbtok

Rule-based Arabic (MSA) text→IPA with tashkeel diacritization — a downstream Arabic engine built on orthography2ipa.

A context-sensitive token tree (sentence → words → characters) implements the morpho-phonological rules that table lookups cannot express: sun-letter assimilation, hamzat al-waṣl elision, tanwīn pausal forms, tāʾ marbūṭa, mater-lectionis vowel lengthening, definite-article waṣl, and idgham/iqlab nasal assimilation. Bare (undiacritized) text is diacritized first via text2tashkeel — a model picker over bundled ONNX diacritization models.

Honesty note: the gold IPA reference set was LLM-generated and has not been validated by a native MSA speaker. If you speak MSA, pull requests are very welcome.

Installation

pip install arbtok

Usage

arbtok is built on orthography2ipa (spec data and the shared G2PPlugin/WordContext base types) and owns the Arabic pipeline — orthography2ipa stays the language-agnostic base library.

Engine class

from arbtok.tokenizer import Sentence

Sentence("اَلسَّلَامُ عَلَيْكُمْ").ipa

Bare text is handled by diacritizing first:

from arbtok.plugin import ArbtokG2PPlugin

plugin = ArbtokG2PPlugin()
plugin.transcribe("كتاب جميل")    # auto-tashkeel + IPA

Diacritization only

from arbtok.tashkeel import TashkeelDiacritizer   # wraps text2tashkeel

TashkeelDiacritizer().diacritize("كتاب جميل")

Quality benchmarks

The test suite pins a gold sentence set (CER target ≤ 5% against the reference transcriptions) and benchmarks against espeak-ng. See tests/test_ipa_fuzzy.py and docs/ for details.