README.md

---
license: cc-by-4.0
language:
  - ar
  - en
tags:
  - text-to-speech
  - tts
  - levantine-arabic
  - code-switching
  - synthetic
  - arabic-dialect
size_categories:
  - 10K<n<100K
task_categories:
  - text-to-speech
pretty_name: "Lahgtna Levantine TTS — Synthetic Levantine Arabic & Code-Switching"
dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 24000
    - name: text
      dtype: string
    - name: speaker_id
      dtype: string
    - name: speaker_name
      dtype: string
    - name: gender
      dtype: string
    - name: sentence_type
      dtype: string
  splits:
    - name: train
      num_examples: 50000
---

# 🎙️ Lahgtna Levantine TTS

**Synthetic Levantine Arabic + English Code-Switching speech dataset.**
Generated using [Lahgtna-OmniVoice](https://github.com/Oddadmix/Lahgtna-OmniVoice),
a fine-tuned zero-shot TTS model for Levantine Arabic dialect.

---

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| Total utterances | **50,000** |
| Total speakers | **10** (5 male, 5 female) |
| Pure Levantine Arabic | **44,154** utterances |
| Code-switching (AR+EN) | **5,846** utterances |
| Sampling rate | **24,000 Hz** |
| Estimated total duration | **~66.8 hours** |

### Per-Speaker Breakdown

| Speaker ID | Name | Gender | Utterances | Pure AR | Code-Switch | Est. Hours |
|------------|------|--------|------------|---------|-------------|------------|
| spk_01_male | Badr | male | 5,000 | 4,429 | 571 | 6.86h |
| spk_02_male | Mohamed | male | 5,000 | 4,433 | 567 | 6.71h |
| spk_03_male | Saad | male | 5,000 | 4,419 | 581 | 6.28h |
| spk_04_male | Rami | male | 5,000 | 4,393 | 607 | 7.05h |
| spk_05_male | Fadi | male | 5,000 | 4,428 | 572 | 6.42h |
| spk_06_female | Amina | female | 5,000 | 4,379 | 621 | 5.97h |
| spk_07_female | Fatma | female | 5,000 | 4,420 | 580 | 5.88h |
| spk_08_female | Lamyaa | female | 5,000 | 4,426 | 574 | 7.36h |
| spk_09_female | Mona | female | 5,000 | 4,428 | 572 | 7.53h |
| spk_10_female | Haneen | female | 5,000 | 4,399 | 601 | 6.71h |

---

## 📝 Data Collection & Processing

### 1. Text Data Sources

The 50,000 sentences were collected from:

| Source | Type | Count |
|--------|------|-------|
| [GU-CLASP Shami Corpus](https://github.com/GU-CLASP/shami-corpus) | Real Levantine Arabic (Syrian, Lebanese, Palestinian, Jordanian) | ~44,000 |
| Synthetic code-switching templates | Levantine Arabic + English (tech/daily life) | ~6,000 |

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

| Property | Value |
|----------|-------|
| Model | [`oddadmix/lahgtna-omnivoice-v2`](https://huggingface.co/oddadmix/lahgtna-omnivoice-v2) |
| Base architecture | [OmniVoice](https://github.com/Oddadmix/Lahgtna-OmniVoice) (k2-fsa/OmniVoice fine-tune) |
| Fine-tuning | Levantine Arabic dialect (apc — ISO 639-3) |
| Generation mode | Zero-shot voice cloning from reference audio |
| Language code | `apc` (North Levantine Arabic) |
| Output sample rate | 24,000 Hz |
| Generation parameters | temperature=0.7, top_p=0.7, repetition_penalty=1.2 |

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

```python
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](https://creativecommons.org/licenses/by/4.0/) — Free to use with attribution.

## 👤 Author

[![HuggingFace](https://img.shields.io/badge/🤗-mohammedaly22-FFD21E)](https://huggingface.co/mohammedaly22)

## 🔗 Related

- [Lahgtna-OmniVoice model](https://github.com/Oddadmix/Lahgtna-OmniVoice)
- [oddadmix/lahgtna-omnivoice-v2](https://huggingface.co/oddadmix/lahgtna-omnivoice-v2)
- [GU-CLASP/shami-corpus](https://github.com/GU-CLASP/shami-corpus)
- [leva-tts training repo](https://github.com/mohammedaly22/leva-tts)