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 40 speaker-language pairs across all 22 Indian languages. 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: 40 speaker-language pairs available now in all 22 Indian languages.

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

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Dataset Statistics

Language	Speaker	Hours	Utterances
Assamese	Female	29.09	15,084
Assamese	Male	29.23	16,614
Bengali	Female	29.69	15,570
Bengali	Male	27.06	15,639
Bodo	Female	27.32	16,329
Bodo	Male	24.99	13,162
Dogri	Female	25.69	13,177
Dogri	Male	22.35	10,332
Gujarati	Female	25.39	12,151
Gujarati	Male	26.01	15,799
Hindi	Male	23.78	13,464
Kannada	Female	27.02	14,913
Kannada	Male	27.59	15,999
Kashmiri	Female	10.95	6,653
Kashmiri	Male	25.34	16,059
Konkani	Female	26.33	17,585
Konkani	Male	25.43	19,324
Maithili	Female	21.79	12,446
Maithili	Male	29.34	12,917
Malayalam	Female	26.42	16,974
Malayalam	Male	25.22	16,877
Manipuri	Male	23.55	12,579
Marathi	Female	28.80	15,472
Marathi	Male	26.55	14,483
Nepali	Female	28.74	16,016
Nepali	Male	26.36	15,239
Odia	Female	24.07	11,757
Odia	Male	23.85	13,611
Punjabi	Female	26.72	13,420
Punjabi	Male	29.12	15,620
Sanskrit	Female	25.27	12,757
Sanskrit	Male	26.20	11,002
Santali	Male	25.04	13,513
Sindhi	Female	24.61	13,588
Tamil	Female	29.94	19,871
Tamil	Male	26.18	16,790
Telugu	Female	27.28	15,404
Telugu	Male	24.98	15,006
Urdu	Female	26.23	13,980
Urdu	Male	26.03	15,023
Total		1035.51	582,199

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License

CC-BY-4.0

Citation

If you use this dataset, please cite:

@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},
}