mohammedaly22/lahgtna-levantine-tts

🎙️ Lahgtna Levantine TTS

Synthetic Levantine Arabic + English Code-Switching speech dataset. Generated using Lahgtna-OmniVoice, a fine-tuned zero-shot TTS model for Levantine Arabic dialect.

📊 Dataset Statistics

Per-Speaker Breakdown

📝 Data Collection & Processing

1. Text Data Sources

The 50,000 sentences were collected from:

The Shami corpus provides authentic dialectal text from four Levantine sub-dialects:

• —Syrian (syrian.txt) — 34,491 sentences
• —Lebanese (Lebenees.txt) — 9,905 sentences
• —Palestinian (Palestinian.txt) — 9,545 sentences
• —Jordanian (jordinian.txt) — 6,007 sentences

Code-switching sentences follow natural Levantine-English mixing patterns:

هَلَّق عم أشتغل على the project اللي حكيتلك عنه
والله the meeting كتير important، لازم نحضّر مِنِيح

2. Text Normalization & Partial Diacritization

Before synthesis, each sentence was processed through:

Step 1 — Unicode cleanup: NFC normalization, tatweel removal, alef unification

Step 2 — Number verbalization: Levantine Arabic number words

• —3 كتب → تلاتة كتب
• —$50 → خمسين دولار

Step 3 — Partial diacritization on homographs only: The key design decision: instead of full diacritization, we apply diacritics only to ambiguous homographs that could be mispronounced. This makes the model robust to both diacritized and undiacritized input at inference time.

Diacritized homograph examples:

هَلَّق  (now — vs هَلَقَ = he shaved, MSA)
ضَلّ   (remained, Levantine — vs ضَلَّ = went astray, MSA)
مِشْ   (not, Levantine negation)
بِدِّي (I want, Levantine bi-imperfect)

Step 4 — ه → ة correction: Levantine Arabic informal writing uses ه where standard orthography uses ة (ta marbuta). A comprehensive rule-based corrector fixes feminine nouns, adjectives, and proper names while preserving genuine ه in verb+pronoun forms and الله compounds:

• —هالضحكه الحلوه → هالضحكة الحلوة ✅
• —والله → والله (preserved — contains الله) ✅
• —فيه، عليه، معه → preserved (pronoun suffixes) ✅

Step 5 — Levantine lexicon overrides (148 entries in CSV): Common Levantine dialect words get dialect-correct diacritization via an editable CSV file (data/levantine_lexicon.csv) — no code changes needed to add new words.

3. TTS Synthesis — Lahgtna-OmniVoice

Each speaker was cloned from a 5–15 s reference recording of a real Levantine speaker. The 10 speakers were generated in parallel across 4× NVIDIA H100 GPUs using Python multiprocessing, with each GPU handling 2–3 speakers simultaneously.

📁 Dataset Structure

train/
  audio       — Audio feature at 24 kHz
  text        — Levantine Arabic transcript (partial diacritics on homographs)
  speaker_id  — e.g. "spk_01_male"
  speaker_name— e.g. "Badr"
  gender      — "male" | "female"
  sentence_type — "pure_levantine" | "code_switching"

🔧 Usage

from datasets import load_dataset

ds = load_dataset("mohammedaly22/lahgtna-levantine-tts", split="train")

# Play sample
sample = ds[0]
print(sample["text"])          # transcript
print(sample["speaker_name"]) # e.g. "Badr"
print(sample["sentence_type"]) # "pure_levantine" or "code_switching"

# Audio: sample["audio"]["array"] at 24000 Hz

📜 License

CC BY 4.0 — Free to use with attribution.

👤 Author


🔗 Related

• —Lahgtna-OmniVoice model
• —oddadmix/lahgtna-omnivoice-v2
• —GU-CLASP/shami-corpus
• —leva-tts training repo