---
language:
- ne
task_categories:
- text-to-speech
size_categories:
- 1K<n<10K
license: mit
tags:
- nepali
- tts
- speech
- mos
- benchmark
---

# NepTTS-Bench Dataset

First comprehensive benchmark for evaluating Nepali text-to-speech systems.

## Contents

- **sentences.json** — 365 phonologically-designed Nepali sentences with metadata
- **audio/** — TTS outputs from 12 systems (~2000 audio files)
- **results/** — Pre-computed evaluation results (SCOREQ, Chirp2, MMS, XLS-R, Whisper)
- **baselines.json** — Aggregate scores for all baseline systems
- **model/** — NepaliMOS predictor checkpoint (Spearman 0.587)

## Systems Evaluated

| System | Human MOS | Type |
|--------|----------|------|
| Natural Speech | 3.91 | Human |
| TingTing Asmita | 3.49 | Nepali-specific |
| ElevenLabs v3 | 3.48 | Cloud |
| Piper | 3.47 | Open source |
| TingTing Subina | 3.42 | Nepali-specific |
| Edge TTS Hemkala | 3.31 | Cloud (Microsoft) |
| Edge TTS Sagar | 3.28 | Cloud (Microsoft) |
| Gemini Flash | 3.19 | Cloud (Google) |
| gTTS | 2.56 | Cloud (Google Translate) |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("ampixa/neptts-bench")
```

Or use the evaluation CLI:

```bash
pip install neptts-eval
neptts-eval --wav_dir ./my_tts_outputs/
```

## Citation

```bibtex
@article{neptts-bench-2026,
  title={NepTTS-Bench: A Comprehensive Benchmark for Nepali Text-to-Speech Evaluation},
  author={Ampixa},
  year={2026}
}
```

## Links

- **GitHub:** [Ampixa/neptts-bench](https://github.com/Ampixa/neptts-bench)
- **Rate TTS:** [tts.ampixa.com/rating](https://tts.ampixa.com/rating)
- **Paper:** Coming soon