---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: duration
    dtype: float64
  - name: lang
    dtype: string
  - name: lang_prob
    dtype: float64
  - name: mos
    dtype: float64
  - name: hyp_pc
    dtype: string
  - name: hyp_nopc
    dtype: string
  - name: wer
    dtype: float64
  - name: cer
    dtype: float64
  - name: start_cer
    dtype: float64
  - name: end_cer
    dtype: float64
  - name: channel_id
    dtype: string
  - name: video_id
    dtype: string
  - name: speaker
    dtype: string
  - name: start
    dtype: float64
  - name: end
    dtype: float64
  - name: eos_state
    dtype: string
  - name: segments
    list:
    - name: start
      dtype: float64
    - name: end
      dtype: float64
    - name: speaker
      dtype: string
    - name: index
      dtype: int64
    - name: eos_prob
      dtype: float64
    - name: eos_state
      dtype: string
  - name: audio
    dtype: audio
  splits:
  - name: train
    num_bytes: 70318225581
    num_examples: 625192
  download_size: 70029775362
  dataset_size: 70318225581
language:
- fa
license: cc-by-4.0
multilinguality: monolingual
task_categories:
- text-to-speech
- automatic-speech-recognition
pretty_name: PersianVox
tags:
- speech
- persian
- farsi
- tts
- text-to-speech
- audio
- multi-speaker
- zero-shot-tts
size_categories:
- 100K<n<1M
---

<div align="center">

# PersianVox: A Prosody-Aware Approach for Speech Dataset Generation from In-the-Wild Data

[![Demo](https://img.shields.io/badge/🌐_Website-PersianVox-1a73e8)](https://saeedzou.github.io/persianvox-demo/)
[![Paper](https://img.shields.io/badge/📄_Paper-arXiv-b5212f)](https://arxiv.org/abs/2609.19324)
[![Hugging Face](https://img.shields.io/badge/🤗_Hugging_Face-Dataset-ffbd45)](https://huggingface.co/datasets/saeedzou/persianvox)

</div>

**PersianVox** is a 2,400-hour, multi-speaker Persian (Farsi) speech corpus automatically mined from in-the-wild unlabeled data. It is, to date, the largest open-source speech resource for Persian, built to support zero-shot text-to-speech (TTS) research and other speech tasks in low-resource-language settings.

## Dataset Summary

Advancement of zero-shot text-to-speech synthesis is currently hindered for low-resource languages by the scarcity of large-scale, high-fidelity speech datasets. Traditional alignment-based methods require rare verbatim transcripts, while standard in-the-wild pipelines often rely on single-model automatic speech recognition and silence-based segmentation, leading to transcription errors and truncated prosody.

To address these challenges for the Persian language, PersianVox was produced with a fully automated pipeline that generates high-quality speech corpora from in-the-wild data. The pipeline integrates:

- A **prosody-aware segmentation strategy** that uses acoustic turn-detection to preserve linguistic completeness and optimize utterance duration for long-context modeling.
- A **dual-model agreement mechanism**, leveraging two distinct ASR model architectures to filter unreliable transcriptions without any ground-truth transcripts.

The result is a 2,400-hour multi-speaker dataset for Persian, along with the first comparative benchmark of speech-quality-assessment methods for the language (a human-annotated subset is released separately to support that benchmark).

## Dataset Statistics

| Statistic | Value |
|---|---|
| Utterances | 625,192 |
| Total duration | ~2,408.67 hours |
| Speakers | 3,248 |
| Vocabulary size | 212,216 |
| Mean utterance duration | 13.87 s |
| Std. dev. of utterance duration | 6.32 s |

## Supported Tasks

- **Text-to-speech (TTS):** primary intended use — training zero-shot / multi-speaker TTS models on `audio` paired with `hyp_pc`.
- **Automatic speech recognition (ASR):** the transcripts and CER/WER quality fields make this corpus usable for ASR training/evaluation as well.

## Dataset Structure

### Data Instances

Each row is a single utterance: an audio clip, its transcript(s), quality/filtering scores, and provenance metadata linking it back to the source video and merged sub-segments.

### Data Fields

| Field | Type | Description |
|---|---|---|
| `audio` | `Audio` | The utterance's audio clip. |
| `duration` | `float64` | Duration of the clip, in seconds. |
| `lang` | `string` | Detected language; always `"fa"` — non-Persian samples were filtered out. |
| `lang_prob` | `float64` | Language-detection confidence from Whisper Large V3; all retained samples have `lang_prob` > 0.95. |
| `mos` | `float64` | Predicted Mean Opinion Score (speech quality), measured with [SCOREQ](https://arxiv.org/abs/2410.06675). All samples with `mos` less than 3.5 were filtered out. |
| `hyp_pc` | `string` | Punctuated transcript hypothesis from the authors' proprietary ASR model. This is the transcript used for TTS training. |
| `hyp_nopc` | `string` | Transcript hypothesis from a second, punctuation-less ASR model, used as an independent check against `hyp_pc`. |
| `wer` | `float64` | Word Error Rate between `hyp_pc` and `hyp_nopc`, used for filtering. |
| `cer` | `float64` | Character Error Rate between `hyp_pc` and `hyp_nopc`, used for filtering. |
| `start_cer` | `float64` | CER computed on just the first 7 characters of the utterance. |
| `end_cer` | `float64` | CER computed on just the last 7 characters of the utterance. |
| `channel_id` | `string` | ID of the source channel the sample was drawn from. |
| `video_id` | `string` | ID of the source video. |
| `speaker` | `string` | Speaker identifier. |
| `start` | `float64` | Start time of the clip within the source video. |
| `end` | `float64` | End time of the clip within the source video. |
| `eos_state` | `string` | `"complete"` or `"incomplete"` — whether the utterance ends in a complete sentence, as judged by [smart-turn-v3](https://huggingface.co/pipecat-ai/smart-turn-v3). A dedicated merging strategy built around this signal was used to increase the proportion of complete utterances. |
| `segments` | `list` | The sub-segments (each with `start`, `end`, `speaker`, `index`, `eos_prob`, `eos_state`) that were merged together to form this utterance. |

### Data Filtering

Samples were filtered based on empirically chosen thresholds (see paper for details):

- `cer` < 12.5
- `wer` < 15
- `start_cer` / `end_cer` (edge CER) < 50

### Data Splits

| Split | Examples |
|---|---|
| `train` | 625,192 |

## Dataset Creation

### Source Data

Utterances were mined from unlabeled, publicly available web video/audio, identified by `channel_id` and `video_id`, and segmented using a prosody-aware, turn-detection-based strategy rather than simple silence-based segmentation.

### Annotations

All transcripts and quality labels (`hyp_pc`, `hyp_nopc`, `wer`, `cer`, `start_cer`, `end_cer`, `mos`, `eos_state`, `lang`, `lang_prob`) were produced automatically by the pipeline described above — no human transcription was used to build the main corpus. A separate, human-annotated subset was additionally released to benchmark speech-quality-assessment methods for Persian (see paper).

## Considerations for Using the Data

- Transcripts are machine-generated (ASR) rather than human-verified; use the provided `wer`/`cer`/`mos` fields to apply your own quality thresholds if the defaults don't suit your use case.
- Released under CC BY 4.0 — redistribution and commercial use are permitted with attribution.

## License

This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.

## Citation

If you use this dataset, please cite the accompanying paper:

```bibtex
@misc{zouashkiani2026persianvoxprosodyawareapproachspeech,
      title={PersianVox: A Prosody-Aware Approach for Speech Dataset Generation from In-the-Wild Data}, 
      author={Saeedreza Zouashkiani and Soheil Khalesi and Saman Soleimani Roudi and Sajjad Amini and Shahrokh Ghaemmaghami},
      year={2026},
      eprint={2609.19324},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2609.19324}, 
}
```