---
language:
  - tr
license: cc-by-4.0
task_categories:
  - text-generation
  - other
tags:
  - turkish
  - text-normalization
  - inverse-text-normalization
  - itn
  - tts
  - asr-post-processing
  - numbers
  - dates
  - synthetic-data
  - rule-based
pretty_name: Turkish Text Normalization (TN/ITN)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: test
        path: data/test.csv
---

# 🇹🇷 Turkish Text Normalization (TN / ITN)

A deterministic, **rule-based** dataset of Turkish **written ↔ spoken** pairs for
**Text Normalization (TN)** and **Inverse Text Normalization (ITN)** — mapping digit/symbol
forms (`1.500 TL`, `%25`, `15.07.2026`) to their fully spoken Turkish words
(`bin beş yüz lira`, `yüzde yirmi beş`, `on beş temmuz iki bin yirmi altı`) and back.

This is a common, high-value preprocessing step for **Turkish ASR post-processing** and
**TTS front-ends**, where numbers, dates, currencies and percentages must be verbalized.

> **Provenance & honesty:** every pair is **generated programmatically** with transparent
> linguistic rules (Turkish cardinal/ordinal number grammar, month names, decimal reading
> conventions). No text is scraped and no private data is used. The full generator is included
> ([`build_dataset.py`](build_dataset.py)) so the dataset is **100% reproducible** from a fixed seed.

---

## 📦 Contents

| Split | Rows |
|-------|-----:|
| Train | 15,398 |
| Test  | 1,711 |
| **Total** | **17,109** |

### Fields

| Column | Description |
|--------|-------------|
| `id` | Row index within the split |
| `category` | One of: `cardinal`, `ordinal`, `decimal`, `percentage`, `currency`, `date`, `time` |
| `written` | Digit / symbol form (the "written" surface form) |
| `spoken` | Fully verbalized Turkish words (lowercase) |

### Category distribution (unique pairs)

| Category | Count | Example (`written` → `spoken`) |
|----------|------:|--------------------------------|
| cardinal   | 4,259 | `2026` → `iki bin yirmi altı` |
| decimal    | 3,497 | `530,5` → `beş yüz otuz virgül beş` |
| date       | 3,082 | `15.07.2026` → `on beş temmuz iki bin yirmi altı` |
| currency   | 3,051 | `1500 TL` → `bin beş yüz lira` |
| ordinal    | 1,683 | `523.` → `beş yüz yirmi üçüncü` |
| time       | 1,101 | `14:30` → `saat on dört buçuk` |
| percentage |   436 | `%25` → `yüzde yirmi beş` |

---

## 🚀 Usage

```python
from datasets import load_dataset

ds = load_dataset("yagmurtuncer/turkish-text-normalization")

# ITN (spoken -> written) or TN (written -> spoken)
ex = ds["train"][0]
print(ex["written"], "→", ex["spoken"])
```

Typical uses:
- Train/evaluate a **seq2seq normalizer** (`written` → `spoken` for TTS, or `spoken` → `written` for ASR)
- Rule-engine **regression tests** for Turkish verbalization
- Data augmentation for Turkish ASR/TTS pipelines

---

## 🏗️ How it was built (reproducible)

```bash
python build_dataset.py   # regenerates data/ deterministically (seed = 42)
python validate.py        # 6 data-quality checks, all must pass
```

The generator implements Turkish number grammar directly:
- **Cardinals** up to the billions (`bin` not `bir bin`, `bir milyon`, correct `yüz`/`bin` scaling)
- **Ordinals** via a vetted last-word suffix map (`dört` → `dördüncü`, `on` → `onuncu`)
- **Dates** with Turkish month names, **times** (incl. `buçuk`), **currency** (TL / $ / € / ₺),
  **percentages** (`yüzde …`), and **decimals** (`virgül …`)

### Conventions
- `spoken` is **lowercased Turkish** (letters + spaces only — enforced by `validate.py`)
- Decimal fractions with a leading zero are read **digit-by-digit** (`0,05` → `sıfır virgül sıfır beş`);
  otherwise the fraction is read as a whole number (`3,14` → `üç virgül on dört`)
- Pairs are **globally de-duplicated** on `(written, spoken)` — counts reflect *unique* examples,
  not inflated repetitions

---

## ✅ Data Quality

`validate.py` enforces: non-empty fields · valid category set · `spoken` charset ·
global `(written, spoken)` uniqueness · all categories present · every `written` contains a digit.
All checks pass on the released data.

---

## ⚠️ Limitations

- Rule-generated: covers standard verbalizations, not every colloquial reading
  (e.g. clock time also has `çeyrek geçe` styles not exhaustively included)
- Small `percentage` count is intentional — whole percentages only span 0–100, and duplicates
  are removed rather than padded
- Not a substitute for a full production normalizer; intended for training, testing and prototyping

---

## 📄 License & Citation

Released under **CC-BY-4.0**.

```bibtex
@misc{tuncer_turkish_text_normalization,
  title  = {Turkish Text Normalization (TN/ITN)},
  author = {Nur Yağmur Tuncer},
  year   = {2026},
  url    = {https://huggingface.co/datasets/yagmurtuncer/turkish-text-normalization}
}
```