ckb-g2p 2.0.1

Central Kurdish Grapheme-to-Phoneme (G2P) converter and Syllabifier for TTS.

Central Kurdish G2P (ckb_g2p)

A linguistically accurate Grapheme-to-Phoneme (G2P) converter and Syllabifier for Central Kurdish (Sorani).

Designed specifically for training modern Text-to-Speech (TTS) models (VITS, FastSpeech2, Glow-TTS) by providing robust phonetization, stress marking, and syllable boundaries.

(کوردی) دەربارەی پڕۆژە

ئەم پڕۆژەیە ئامرازێکی پێشکەوتووە بۆ گۆڕینی دەقی کوردی (سۆرانی) بۆ فۆنێم و بڕگە. بەتایبەت دیزاین کراوە بۆ سیستەمەکانی دروستکردنی دەنگ و ڕاهێنانی مۆدێلەکانی زیرەکی دەستکرد.

---

🌟 Why Use This?

Generic G2P tools often fail on Kurdish phonology. ckb_g2p solves these specific challenges:

Feature	Problem in Generic Tools	Solution in ckb_g2p
Palatalization	Treats all 'k' and 'g' the same.	Distinguishes Heavy (Postalveolar t͡ʃ, d͡ʒ) vs Light (Dental t̪͡ʃ̟, d̪͡ʒ̟) based on vowel context.
Schwa Insertion	Fails on clusters like "grft".	Automatically inserts Bizroka (/ɪ/) to fix illegal consonant clusters (gɪ.ɾɪft).
Geminate Consonants	Merges double letters.	Preserves true geminates or splits them if phonologically required (e.g., dat̪͡ʃɛnn → da.t̪͡ʃ̟ɛ.ˈnɪn).
Stress (Prosody)	Ignores stress.	Smartly assigns stress (ˈ). Handles Negative Verb shifts (nachu → ˈna.t̪͡ʃ̟uː) vs Nouns (kurd → kurd).
Complex Onsets	Incorrectly splits clusters.	Respects valid onsets like kw and cy (wusha → wu.ʃa).

🔗 Live Demos

• G2P Playground: ckb-g2p.streamlit.app
• Text Normalizer: ckb-textify.streamlit.app

---

📦 Installation

pip install ckb_g2p

Dependencies: This library automatically installs ckb-textify for normalizing numbers (1991 → hazar...), dates, and symbols.

---

🚀 Usage

Basic Conversion

from ckb_g2p import Converter

# Default: Normalization=ON, Pauses=ON, Stress=OFF
converter = Converter()

text = "کوردستان"
ipa = converter.syllabify(text)
print(ipa)
# Output: kuɾ.dɪs.tän

TTS-Ready Output (With Stress)

For training TTS models, you want explicit stress markers and pause tokens.

# Enable stress marking
converter = Converter(use_stress=True, use_pause_markers=True)

# Handles negative verbs correctly (Stress on first syllable)
text = "نەچوو بۆ بازاڕ, لە ساڵی 1991."
ipa = converter.syllabify(text)

print(ipa)
# Output: ˈna.t̪͡ʃ̟uː bo̞ bä.ˈzäɾ | la sä.ˈɫiː ha.ˈzäɾ w no̞.ˈsad w na.ˈwa.du ˈjak ||

Configuration Options

Argument	Type	Default	Description
use_stress	bool	False	Adds primary stress marker (ˈ) to the appropriate syllable.
use_pause_markers	bool	True	Converts punctuation to IPA boundaries (| short, || long).
normalize	bool	True	Uses ckb-textify to convert numbers/symbols to text before processing.

---

🗣️ Phoneme Inventory

We use a precise IPA set to capture allophonic variations critical for natural speech synthesis.

Consonants (Key Distinctions)

Grapheme	IPA	Type	Description
چ	t̪͡ʃ̟	Light (Dental)	Standard "ch". Tongue tip touches teeth.
ک	t͡ʃ	Heavy (Postalveolar)	Palatalized /k/ before front vowels (i, e, y). Like English "Chair".
ج	d̪͡ʒ̟	Light (Dental)	Standard "j". Tongue tip touches teeth.
گ	d͡ʒ	Heavy (Postalveolar)	Palatalized /g/ before front vowels. Like English "Jack".
ڵ	ɫ	Velarized	"Dark L", distinct from clear l.
ڕ	r	Trill	Rolled R, distinct from tap ɾ.

---

🛠️ Customizing Pronunciation

If the rule-based engine fails on a specific word (e.g., a foreign name), you can manually override it by editing src/ckb_g2p/resources/exceptions.csv inside the package or locally mapping exceptions before processing.

---

🤝 Contributing

Contributions are welcome!

1. Fork the repository.
2. Create a feature branch.
3. Submit a Pull Request.

👨‍💻 Author

Developed by Razwan M. Haji.

Special thanks to the open-source community and the contributors of ckb-textify.