README.md

---
language:
- fa
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
task_ids:
- speech-recognition
pretty_name: ParsVoice - Persian Speech Recognition Dataset
tags:
- persian
- farsi
- speech
- audio
- asr
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: transcription
    dtype: string
  - name: transcription_with_punctuation
    dtype: string
  - name: speaker_id
    dtype: string
  - name: book_id
    dtype: string
  - name: is_complete
    dtype: bool
  - name: duration_seconds
    dtype: float64
  - name: snr_db
    dtype: float64
  - name: quality_score
    dtype: float64
  - name: segment_type
    dtype: string
  - name: was_smart_trimmed
    dtype: bool
  - name: speaker_embedding
    list: float64
  - name: embedding_confidence
    dtype: float64
  splits:
  - name: train
    num_bytes: 323754892
    num_examples: 916
  download_size: 318080676
  dataset_size: 323754892
---

# ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis

[![Paper](https://img.shields.io/badge/arXiv-2510.10774-b31b1b.svg)](https://arxiv.org/abs/2510.10774)
[![Dataset](https://img.shields.io/badge/🤗%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/MohammadJRanjbar/ParsVoice)

## 📋 Overview

ParsVoice is the largest high-quality Persian speech dataset designed specifically for text-to-speech (TTS) applications. The dataset addresses the critical gap in Persian speech technologies by providing a comprehensive corpus with speaker diversity and audio quality comparable to major English corpora.

## 🎯 Key Features

- **1,804 hours** of high-quality speech data
- **470+ unique speakers** with diverse characteristics
- **Multi-speaker TTS** optimized content
- **High-quality audio-text alignment** using automated pipeline
- **Naturalness MOS**: 3.6/5
- **Speaker Similarity SMOS**: 4.0/5

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| Total Audio Duration | 1,804 hours (high-quality subset) |
| Raw Audio Processed | 3,526 hours |
| Number of Speakers | 470+ |
| Source Material | 2,000 audiobooks |
| Language | Persian (Farsi) |
| Audio Format | WAV |
| Sample Rate | 22.05 kHz |

## 🔬 Research Paper

This dataset is introduced in our paper:

**ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis**

*Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili, Azadeh Shakery*

University of Tehran

📄 **Read the full paper**: [arXiv:2510.10774](https://arxiv.org/abs/2510.10774)

### Abstract

Existing Persian speech datasets are typically smaller than their English counterparts, which creates a key limitation for developing Persian speech technologies. We address this gap by introducing ParsVoice, the largest Persian speech corpus designed specifically for text-to-speech (TTS) applications. We created an automated pipeline that transforms raw audiobook content into TTS-ready data, incorporating components such as a BERT-based sentence completion detector, a binary search boundary optimization method for precise audio-text alignment, and audio-text quality assessment frameworks tailored to Persian. The pipeline processes 2,000 audiobooks, yielding 3,526 hours of clean speech, which was further filtered into a 1,804-hour high-quality subset suitable for TTS, featuring more than 470 speakers. To validate the dataset, we fine-tuned XTTS for Persian, achieving a naturalness Mean Opinion Score (MOS) of 3.6/5 and a Speaker Similarity Mean Opinion Score (SMOS) of 4.0/5, demonstrating ParsVoice's effectiveness for training multi-speaker TTS systems.

## 📦 Dataset Access

> **⚠️ Important Notice**: 
> 
> A representative subset of the ParsVoice dataset is currently available for preview and research purposes. 
> 
> **Full dataset access will be granted after the paper is accepted for publication.** 
> 
> For early access requests or collaboration inquiries, please contact the authors.

## 🏗️ Dataset Structure

Each sample in the dataset contains:
```python
{
    "audio": Audio feature,
    "text": str,              # Transcript of the audio
    "speaker_id": str,        # Unique speaker identifier
    "duration": float,        # Audio duration in seconds
    "speaker_gender": str,    # Speaker gender (M/F)
}
```

## 💻 Usage

### Loading the Dataset
```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("MohammadJRanjbar/ParsVoice")

# Access a sample
sample = dataset['train'][0]
print(f"Text: {sample['text']}")
print(f"Duration: {sample['duration']} seconds")
print(f"Speaker: {sample['speaker_id']}")
```

### Example: Training a TTS Model
```python
from datasets import load_dataset
import torch

# Load dataset
dataset = load_dataset("MohammadJRanjbar/ParsVoice", split="train")

# Your TTS training code here
for sample in dataset:
    audio = sample["audio"]["array"]
    text = sample["text"]
    # Process for TTS training
```

## 🔧 Data Processing Pipeline

The ParsVoice dataset was created using an automated pipeline that includes:

1. **BERT-based sentence completion detector** for text segmentation
2. **Binary search boundary optimization** for precise audio-text alignment
3. **Quality assessment frameworks** tailored for Persian speech
4. **Multi-stage filtering** to ensure high-quality TTS data

## 🎯 Applications

This dataset is suitable for:

- Text-to-Speech (TTS) synthesis
- Voice cloning and conversion
- Speaker recognition
- Speech enhancement
- Persian language model development
- Multi-speaker synthesis research

## 📈 Benchmark Results

We validated the dataset by fine-tuning XTTS for Persian:

| Metric | Score |
|--------|-------|
| Naturalness (MOS) | 3.6/5 |
| Speaker Similarity (SMOS) | 4.0/5 |

These results demonstrate ParsVoice's effectiveness for training high-quality multi-speaker TTS systems.

## 📜 Citation

If you use this dataset in your research, please cite:
```bibtex
@article{ranjbar2024parsvoice,
  title={ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis},
  author={Ranjbar Kalahroodi, Mohammad Javad and Faili, Heshaam and Shakery, Azadeh},
  journal={arXiv preprint arXiv:2510.10774},
  year={2024}
}
```

## 📄 License

This dataset is released under [specify license - e.g., CC BY-NC 4.0, MIT, etc.]. Please refer to the LICENSE file for more details.

## 👥 Authors

- **Mohammad Javad Ranjbar Kalahroodi** - University of Tehran
- **Heshaam Faili** - University of Tehran
- **Azadeh Shakery** - University of Tehran

## 📧 Contact

For questions, issues, or collaboration opportunities:

- Open an issue on this repository
- Email: [contact email]
- Project Page: [if available]

## 🙏 Acknowledgments

We thank all contributors and the University of Tehran for supporting this research.

## 📊 Dataset Card

- **Curated by**: Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili, Azadeh Shakery
- **Language**: Persian (Farsi)
- **License**: [To be specified]
- **Paper**: [arXiv:2510.10774](https://arxiv.org/abs/2510.10774)

---

**Note**: This is a research dataset. Please ensure compliance with applicable laws and ethical guidelines when using this data.