Blame for README.md

---
language:
  - ur
license: cc-by-nc-4.0
pretty_name: "UrduTTS: 92-Hour Urdu Speech Corpus with Three-Way Transcriptions"
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-to-speech
  - automatic-speech-recognition
annotations_creators:
  - machine-generated
language_creators:
  - found
multilinguality:
  - monolingual
tags:
  - urdu
  - speech
  - text-to-speech
  - tts
  - asr
  - low-resource
  - phonemes
  - ipa
  - romanization
  - south-asian-languages
---

<div align="center">

# UrduTTS

### A Studio-Quality Urdu Speech Corpus with Urdu, Phonemized, and Romanized Transcriptions

**91.9 hours · 57,873 utterances · 44.1 kHz · 3 aligned text representations**

[![Paper](https://img.shields.io/badge/LREC%202026-Paper-b31b1b.svg)](https://github.com/KAABSHAHID/UrduTTS)
[![GitHub](https://img.shields.io/badge/GitHub-Code%20%26%20Pipeline-181717.svg?logo=github)](https://github.com/KAABSHAHID/UrduTTS)
[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)

</div>

---

## Dataset Summary

UrduTTS is the largest openly available Urdu text-to-speech corpus with **three aligned text
representations** for every utterance: native **Urdu script**, **Phonemized** (IPA) text, and
**Romanized** (Latin) text.

Urdu is spoken by roughly 250 million people but is badly under-resourced in speech technology.
Existing corpora are small, unreleased, or crowd-sourced with highly variable audio quality.
UrduTTS provides 92 hours of clean, consistent read speech — and, critically, ships the phonemized
layer that lets researchers move past the lossy Romanized transcriptions most Urdu TTS work has
relied on.

Released with our LREC 2026 paper, which uses it to show that **phonemized input outperforms
romanized input on every metric measured**, for both VITS and Glow-TTS.

## Why three representations?

Urdu is phonetically rich: aspirated/unaspirated contrasts (/k/ vs /kʰ/), retroflex consonants
(ٹ، ڈ، ڑ), nasalized vowels, and pharyngeal consonants from Arabic and Persian. Romanized Urdu —
the input format most prior neural Urdu TTS systems used — encodes none of this reliably and has no
orthographic standard: خواب ("dream") is written *khawab*, *khwaab*, or *khwab* depending on the writer.

| Representation | Field | Example |
| :--- | :--- | :--- |
| **Urdu** (Perso-Arabic) | `transcription` | جس کی مثال بندگانِ نفس میں ناممکن ہے |
| **Phonemized** (IPA) | `phonemes` | `ɟɪs ki mɪsˈaːl bˌəndəɡˈaːn nˈafs mˈẽ nˈaːmʊmkˌɪn hɛ` |
| **Romanized** (Latin) | `roman` | `JS KY MSAL BNDGAN NFS MYN NAMMKN HE` |

*(English: "Whose example is impossible among the slaves of the self.")*

The Romanized form drops the nasalized vowel in `mˈẽ`, the long vowels in `mɪsˈaːl`, and every stress
mark. Quantifying that loss is what UrduTTS was built for. The clearest case: بڑی ("big") and بری
("bad") differ only by the retroflex ڑ, and both romanize to `BRY` — a contrast the model cannot
learn because the input does not encode it.

---

## Quickstart

```python
from datasets import load_dataset

ds = load_dataset("XKaab/UrduTTS")

sample = ds["train"][0]
sample["audio"]          # {'array': ndarray, 'sampling_rate': 44100, 'path': ...}
sample["transcription"]  # Urdu script
sample["phonemes"]       # IPA
sample["roman"]          # Romanized Latin
```

Stream it instead of downloading ~88 GB:

```python
ds = load_dataset("XKaab/UrduTTS", split="train", streaming=True)
sample = next(iter(ds))
```

Resample to 22.05 kHz (the rate used for the paper's TTS training):

```python
from datasets import Audio
ds = ds.cast_column("audio", Audio(sampling_rate=22050))
```

Play a sample in a notebook:

```python
from IPython.display import Audio as Play
s = ds["train"][0]["audio"]
Play(s["array"], rate=s["sampling_rate"])
```

---

## Dataset Structure

### Fields

| Field | Type | Description |
| :--- | :--- | :--- |
| `audio` | `Audio` | Mono 16-bit PCM WAV, 44.1 kHz |
| `transcription` | `string` | Native Urdu script (Perso-Arabic, Nastaliq) |
| `phonemes` | `string` | IPA phoneme sequence with stress marks, from eSpeak NG |
| `roman` | `string` | Deterministic Latin transliteration |

### Splits

| Split | Utterances | Duration | Share |
| :--- | ---: | ---: | ---: |
| `train` | 40,511 | 64.21 h | 70% |
| `eval` | 11,575 | 18.41 h | 20% |
| `test` | 5,787 | 9.24 h | 10% |
| **Total** | **57,873** | **91.86 h** | |

Utterance-level random split, `seed=42`, reproducible via
[`data_pipeline/06_build_dataset.py`](https://github.com/KAABSHAHID/URDUTTS/blob/main/data_pipeline/06_build_dataset.py).

### Audio properties

| Property | Value |
| :--- | :--- |
| Format | WAV, 16-bit PCM, mono |
| Sampling rate | 44,100 Hz |
| Mean clip length | 5.71 s |
| Clip length range | 99.9% fall within 2–15 s (56 outliers, 0.19–41 s) |
| Mean words / chars per utterance | 15.35 / 67.10 |
| Speakers | 4 (all male) — see limitations |

---

## How the corpus was built

Every stage is a runnable script in
[the GitHub repository](https://github.com/KAABSHAHID/URDUTTS/tree/main/data_pipeline).

1. **Sourcing** — public-domain recordings (LibriVox, Internet Archive, Loyal Books), filtered for
   speaker consistency, pronunciation quality, and absence of background noise.
2. **Segmentation** — split on silence with pydub: a pause longer than **400 ms** below **-15 dBFS**
   marks a boundary; clips outside 2–15 s are rejected.
3. **Boundary synchronization** — 100 ms is trimmed from the end of each clip and prepended to the
   next within the same recording, so hard cuts do not sever word onsets.
4. **Transcription** — Urdu script via a Whisper-based pipeline (`faster-whisper`).
5. **Romanization** — deterministic grapheme-to-Latin transliteration with a fixed table.
6. **Phonemization** — IPA from **eSpeak NG** (`ur`), with stress marks preserved.

---

## Benchmark results

VITS and Glow-TTS trained from scratch with Coqui TTS, once per text representation. Everything
except the input representation is held constant.

### Objective (500 samples per system)

| System | MCD Plain ↓ | MCD DTW ↓ | MCD DTW-SL ↓ | SNR (dB) ↑ | WER (%) ↓ | CER (%) ↓ |
| :--- | ---: | ---: | ---: | ---: | ---: | ---: |
| **VITS-Ph** | **17.00 ± 2.71** | **7.97 ± 1.66** | **9.35 ± 2.19** | **61.86** | **12.3** | **5.3** |
| VITS-Rm | 17.53 ± 2.96 | 8.53 ± 1.74 | 9.98 ± 2.26 | 35.39 | 15.1 | 6.5 |
| **Glow-Ph** | **17.71 ± 2.01** | **6.74 ± 0.98** | **7.42 ± 1.26** | **10.40** | **12.5** | **5.8** |
| Glow-Rm | 18.83 ± 2.12 | 7.07 ± 1.07 | 7.86 ± 1.44 | 10.27 | 25.0 | 11.8 |

### Subjective (MOS, 40 native listeners, 95% CI)

| System | MOS |
| :--- | ---: |
| Reference (ground truth) | 4.79 ± 0.12 |
| **VITS-Phonemized** | **4.38 ± 0.24** |
| VITS-Romanized | 3.86 ± 0.36 |
| **GlowTTS-Phonemized** | **3.34 ± 0.28** |
| GlowTTS-Romanized | 2.64 ± 0.31 |

Phonemized input wins on every metric for both architectures: **+9.6% MOS for VITS** and **+26.5%
MOS for Glow-TTS**. The clearest case is Glow-TTS intelligibility, where WER doubles under
Romanization (25.0% vs 12.5%) — Romanized text cannot encode the retroflex and aspiration contrasts
the model needs.

---

## Intended Uses

**Direct use** — Urdu TTS and ASR, grapheme-to-phoneme and transliteration research, Urdu phonetics
and prosody, and as a template for building resources in other under-resourced languages.

**Out of scope**

- **Commercial use of any kind** — the license is CC BY-NC 4.0
- Voice cloning or speaker impersonation of the corpus speakers
- Generating deceptive synthetic speech attributed to a real person

---

## Limitations

Please read before building on the corpus.

- **All four speakers are male, and speaker labels are not included in this release.** Any voice
  trained on URDUTTS will be male, and per-speaker filtering or multi-speaker conditioning is not
  possible from the released fields alone. This is the corpus's most significant limitation.
- **Transcriptions are ASR-generated, not hand-verified.** Urdu-script transcripts come from a
  Whisper-based pipeline and inherit its errors, which are non-trivial for Urdu. Because the
  Romanized and Phonemized layers are derived from those transcripts, an ASR error propagates to all
  three representations.
- **9.8% of `roman` values contain residual Arabic diacritics** (5,669 rows — mostly U+0650 kasra and
  U+0670 superscript alef), e.g. `SFRِ HJ`. The transliteration table does not map combining marks,
  so they pass through. Strip them with `normalize_urdu()` from
  [`data_pipeline/urdu_romanizer.py`](https://github.com/KAABSHAHID/URDUTTS/blob/main/data_pipeline/urdu_romanizer.py),
  or filter with `re.sub(r'[^\x00-\x7F]', '', roman)`.
- **Read speech in a formal register.** Sources are audiobook and literary recordings.
  Conversational Urdu, code-switching, and spontaneous speech are absent.
- **Romanization is a fixed convention, not a standard.** It is deterministic and internally
  consistent, which is what makes the phonemized-vs-romanized comparison controlled. It deliberately
  reflects the lossiness of real Roman Urdu and is not a proposal for how Roman Urdu should be written.
- **Phonemization inherits eSpeak NG's Urdu limitations.** Its Urdu front-end is rule-based and
  context-insensitive; short-vowel restoration in Perso-Arabic script is genuinely ambiguous
  (پر can be *par* or *pur*), so some phonemizations will be contextually wrong.

### Relation to the paper

The paper's Table 2 reports **88.59 hours / 55,450 utterances at 22.05 kHz** with a per-speaker
breakdown. This release is a slightly larger build — **91.86 hours / 57,873 utterances** — and
stores audio at its native **44.1 kHz** rather than the 22.05 kHz used for TTS training; resample on
load with `cast_column` as shown above. Speaker labels used for the paper's per-speaker table are
not part of the released fields.

---

## Ethics

All source audio came from publicly available, public-domain recordings. The dataset contains no
personally identifiable information and was anonymized before release. Synthetic Urdu speech built
from this corpus should be disclosed as synthetic wherever it is deployed, and should not be used to
produce speech attributed to real, identifiable individuals.

---

## Citation

```bibtex
@inproceedings{shahid-izharuddin-2026-urdutts,
    title     = {A Benchmark Dataset and Comparative Evaluation of Phonemized and
                 Romanized Urdu for Text-to-Speech},
    author    = {Shahid, M Kaab Bin and Izharuddin, Muhammed},
    booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation
                 Conference (LREC 2026)},
    year      = {2026},
    pages     = {10982--10993},
    publisher = {European Language Resources Association (ELRA)},
    url       = {https://github.com/KAABSHAHID/URDUTTS}
}
```

## License

**CC BY-NC 4.0** — share and adapt for non-commercial purposes with attribution.
See [creativecommons.org/licenses/by-nc/4.0](https://creativecommons.org/licenses/by-nc/4.0/).
Code in the companion repository is MIT-licensed.


Code, pipeline, and training recipes: **[github.com/KAABSHAHID/URDUTTS](https://github.com/KAABSHAHID/URDUTTS)**