---
license: cc-by-nc-4.0
language:
- ar
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- arabic
- arabic-tts
- tts
- fusha
- msa
- benchmark
- evaluation
- speech-synthesis
pretty_name: Fasih-TTS Benchmark & Evaluation
size_categories:
- n<1K
---

# Fasih-TTS-Benchmark

Evaluation audio and objective scores for the
[**Fasih-TTS-V1**](https://huggingface.co/NightPrince/Fasih-TTS-V1) Arabic (MSA / Fusha)
text-to-speech model. Every clip is the model's own output, paired with its reference text, its
Whisper-large-v3 transcription, and per-clip **WER / CER**.

## Contents

| Split (`test_set`) | Clips | Purpose | Mean CER |
|---|:--:|---|:--:|
| `silma_msa` | 10 | SILMA open-source Arabic TTS benchmark (MSA sentences) | 2.0% |
| `samples` | 3 | General showcase (greeting, fiqh, reflection) | 0.6% |
| `consistency` | 8 | Varied domain sentences | 1.0% |
| `variance` | 4 | Same sentence synthesized ×4 (run-to-run stability) | 2.0% |
| `stress` | 6 | Hard cases: long text, numbers, lists, term-heavy | 2.1% |

**31 clips**, 24 kHz mono. `metadata.csv` columns: `file_name, text, test_set, wer_pct, cer_pct,
asr_transcription`.

## SILMA benchmark result

On the [SILMA Open-Source Arabic TTS Benchmark](https://huggingface.co/spaces/silma-ai/opensource-arabic-tts-benchmark)
(MSA), Fasih-TTS-V1 ranks **#1 by intelligibility** (lowest WER/CER via Whisper-large-v3):

| Rank | Model | WER% | CER% |
|:--:|--|:--:|:--:|
| **1** | **Fasih-TTS-V1** | **6.5** | **2.0** |
| 2 | XTTS (base) | 10.3 | 6.4 |
| 3 | silma_tts | 11.1 | 6.0 |
| 4 | chatterbox | 12.8 | 6.6 |
| 5 | omnivoice | 15.3 | 7.2 |
| 6 | habibi_specialized | 21.9 | 9.7 |

Full 6-model per-clip provenance: `silma_all_models_detailed.csv`. WER/CER measure
**intelligibility**, not naturalness — SILMA's own benchmark is a human auditory comparison.

## Load

```python
from datasets import load_dataset
ds = load_dataset("NightPrince/Fasih-TTS-Benchmark")
print(ds["train"][0])  # {'audio': ..., 'text': ..., 'test_set': ..., 'cer_pct': ...}
```

## Methodology & reproduction

Audio synthesized with Fasih's production front-end (auto-diacritization via CATT + number
expansion + chunking); scored with Whisper-large-v3 against normalized references
(diacritics-stripped, digits expanded). Reproduce with `scripts/silma_benchmark.py`,
`scripts/silma_compare.py`, `scripts/build_eval_dataset.py` in the
[model's GitHub repo](https://github.com/NightPrinceY/Fasih-TTS-V1).

## License & copyright

Audio is output of Fasih-TTS-V1, a derivative of Coqui XTTS v2 (Coqui Public Model License —
**non-commercial**). Released under **CC-BY-NC-4.0**. Copyright 2026
**Yahya Elnawasany (NightPrince)** — https://nightprincey.github.io/Portfolio-App/