---
dataset_info:
  features:
  - name: filename
    dtype: string
  - name: text
    dtype: string
  - name: language
    dtype: string
  - name: gender
    dtype: string
  - name: style
    dtype: string
  - name: duration
    dtype: float32
  - name: audio
    dtype:
      audio:
        sampling_rate: 48000
  splits:
  - name: train
    num_bytes: 141202886206.96
    num_examples: 282940
  - name: test
    num_bytes: 6557537480.55
    num_examples: 13701
  download_size: 183686217593
  dataset_size: 147760423687.50998
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: cc-by-4.0
task_categories:
- text-to-speech
language:
- as
- bn
- kn
- ml
- mr
- ne
- ta
- pa
- te
- sa
pretty_name: rasa
---

# Rasa: Towards Building an Expressive Multilingual Text-To-Speech Dataset for Indian Languages


**Funded by**: Bhashini, Ministry of Electronics and Information Technology, Government of India  
**Supported by**: EkStep Foundation and Nilekani Philanthropies  

## Overview

We introduce **Rasa**, the first high-quality **multilingual expressive Text-to-Speech (TTS) dataset** for any Indian language. It comprises a minimum of 20 hours per speaker with a target of covering 
a female and male voice for each of the 22 officially recognized languages of India. In our initial 
version, we explore a practical recipe for collecting high-quality data for resource-constrained 
languages, prioritizing easily obtainable neutral data alongside smaller amounts of expressive data. 
This approach enables us to extend our dataset to encompass a diverse array of speaking styles and contexts. 
These include neutral readings from Wikipedia and IndicTTS texts, expressive speech capturing the six 
Ekman emotions (happy, sad, angry, fear, disgust, and surprise), as well as command-based interactions 
from platforms like Alexa, BigBasket, UMANG, and DigiPay. Additionally, Rasa includes natural conversations 
on various topics, news-reading, and narration from book readings. Currently, we release the data for 
8 speaker-language pairs. Through this release, we aim to provide a valuable resource for developing 
expressive TTS models in multilingual settings for the officially recognized languages of India.


### Key Features
- **Multilingual Coverage**: Covers diverse Indian languages
- **Expressive Speech**: Includes **Ekman emotions** (happy, sad, angry, fear, disgust, and surprise)
- **Multiple Speaking Styles**:
  - Neutral speech from Wikipedia texts
  - Command-based interactions from Alexa, BigBasket, UMANG, and DigiPay
  - Natural conversations on various topics
  - News reading and narration from book readings
- **High-Quality Data**: 48 KHz, Mono
- **Current Release**: 20 speaker-language pairs available now

Through this release, we aim to provide a **valuable resource for multilingual expressive TTS models**, helping advance text-to-speech synthesis for Indian languages.

---

## Dataset Statistics

| Language  | Speaker | Hours   | Utterances |
|-----------|---------|---------|------------|
| Assamese  | Female | 25.8    | 15,046     |
| Assamese  | Male   | 28.55   | 16,623     |
| Bengali   | Female | 27.13   | 15,575     |
| Bengali   | Male   | 26.22   | 15,665     |
| Bodo      | Female | 27.23   | 16,329     |
| Bodo      | Male   | 23.52   | 13,167     |
| Dogri     | Male   | 20.39   | 10,069     |
| Dogri     | Female | 24.36   | 13,816     |
| Kannada   | Female | 26.94   | 14,915     |
| Kannada   | Male   | 27.53   | 16,002     |
| Malayalam | Female | 26.43   | 17,017     |
| Maithili  | Male   | 24.94   | 10,513     |
| Marathi   | Female | 27.56   | 15,478     |
| Marathi   | Male   | 25.37   | 14,493     |
| Nepali    | Female | 28.65   | 16,016     |
| Punjabi   | Male   | 24.87   | 15,003     |
| Punjabi   | Female | 21.52   | 11,430     |
| Sanskrit  | Male   | 26.24   | 11,030     |
| Tamil     | Female | 28.60   | 14,402     |
| Telugu    | Female | 20.64   | 11,907     |

---

## License
CC-BY-4.0

## Citation
If you use this dataset, please cite:
```bibtex
@inproceedings{ai4bharat2024rasa,
  author={Praveen Srinivasa Varadhan and Ashwin Sankar and Giri Raju and Mitesh M. Khapra},
  title={{Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings}},
  year=2024,
  booktitle={Proc. INTERSPEECH 2024},
}
```