---
dataset_info:
- config_name: tts
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: prompt_text
    dtype: string
  - name: prompt_audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: speaker_id
    dtype: string
  - name: source_id
    dtype: int64
  - name: prompt_id
    dtype: int64
  - name: source_split
    dtype: string
  splits:
  - name: fa
    num_bytes: 336194003.0
    num_examples: 2098
  download_size: 6871992
  dataset_size: 336194003.0
- config_name: tts_validation
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: prompt_text
    dtype: string
  - name: prompt_audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: speaker_id
    dtype: string
  - name: source_id
    dtype: int64
  - name: prompt_id
    dtype: int64
  - name: source_split
    dtype: string
  splits:
  - name: fa
    num_bytes: 296643213.0
    num_examples: 1851
  download_size: 6209505
  dataset_size: 296643213.0
configs:
- config_name: tts
  data_files:
  - split: fa
    path: tts/fa-*
- config_name: tts_validation
  data_files:
  - split: fa
    path: tts_validation/fa-*
language:
- fa
source_datasets:
- shekar-ai/Neyshekar
task_categories:
- text-to-speech
license: cc0-1.0
---
# Neyshekar Persian TTS evaluation

Reformatted from [shekar-ai/Neyshekar](https://huggingface.co/datasets/shekar-ai/Neyshekar),
revision `30696ada746a66bb3047c984744b5ecfd60fe4e9`, under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).
Source authors: Ahmad Amirivojdan, Farzad Nadiri, Abolfazl Alizadeh and Shaghayegh Yaraghi.
See [Neyshekar (2026)](https://arxiv.org/abs/2609.14542). Do not attempt to identify speakers.

When requested, `tts/fa` uses the predefined **test** split and `tts_validation/fa` uses
**validation**, for development only. No training recordings are used. Requested source
splits are checked for disjoint speaker IDs. This does not establish disjointness from any
evaluated model's training data; model authors must disclose Neyshekar training overlap.

Within each split, select one 3–10 second prompt per speaker, closest to 5 seconds,
ties broken by ascending source ID. Targets are the remaining 1–30 second recordings'
transcripts, deduplicated per speaker. All selected prompt IDs and normalized prompt
texts are excluded from targets across that split. Speakers without a prompt and a
distinct target are omitted. Pairing uses no ASR/TTS quality scores. Rows are ordered
by speaker ID, then numeric target ID. Speaker distribution follows the source and
is not balanced. This is an automatically constructed evaluation set, not a manually
audited TTS benchmark.

**Changes:** prompt/target pairing, filtering, and conversion to the leaderboard's
`id`, `text`, `prompt_text`, `prompt_audio` schema, with source/prompt IDs and split
provenance. Original transcripts and prompt WAV bytes are unchanged. Target recordings
are not copied into the evaluation set. Preparation script:
[`prepare_neyshekar_eval.py`](https://github.com/huggingface/open-tts-leaderboard/blob/main/scripts/prepare_neyshekar_eval.py).

Scoring: Qwen3-ASR (`fa`), Persian normalized WER, RTFx, and WavLM SIM for cloning.
UTMOS22 is English-only and is not reported here. Normalization is applied only at
scoring time, never to the synthesis input. See the repository's Persian evaluation docs.

## Preparation report

The SHA-256 covers ordered `[target_id, prompt_id]` pairs, encoded as compact JSON.

```json
{
  "tts": {
    "source_split": "test",
    "source_rows": 2149,
    "prompt_id_or_text_excluded": 31,
    "duplicate_target_text": 20,
    "output_rows": 2098,
    "output_speakers": 30,
    "pairs_sha256": "b170ed14adc3718ed9fb2d958456c203c3127c0a528e4d0ed7b120bd49414bfe"
  },
  "tts_validation": {
    "source_split": "validation",
    "source_rows": 1886,
    "prompt_id_or_text_excluded": 28,
    "duplicate_target_text": 7,
    "output_rows": 1851,
    "output_speakers": 26,
    "pairs_sha256": "24ce4e740a466a478421bd90a1bbabf8627fb8e12d1ee1cfa55937a49796c757"
  }
}
```

## Citation

All credit for the recordings and transcripts goes to the Neyshekar authors. If you use this
evaluation set, please cite the source corpus:

```bibtex
@article{amirivojdan2026neyshekar,
  title={Neyshekar: An Open Persian Read-Speech Corpus for Automatic Speech Recognition},
  author={Amirivojdan, Ahmad and Nadiri, Farzad and Alizadeh, Abolfazl and Yaraghi, Shaghayegh},
  journal={arXiv preprint arXiv:2609.14542},
  year={2026},
  doi={10.48550/arXiv.2609.14542},
  url={https://arxiv.org/abs/2609.14542}
}
```