metadata

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: string
    - name: wav_path
      dtype: string
    - name: audio
      dtype: audio
  splits:
    - name: train
      num_bytes: 217686663443.905
      num_examples: 371695
    - name: test
      num_bytes: 25694594778.8
      num_examples: 41300
  download_size: 252940653597
  dataset_size: 243381258222.705
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 28 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: 28 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	29.09	15,085
Assamese	Male	29.23	16,614
Bengali	Female	29.69	15,570
Bengali	Male	27.07	15,641
Bodo	Female	27.32	16,329
Bodo	Male	24.99	13,163
Dogri	Female	25.69	13,178
Dogri	Male	22.35	10,332
Gujarati	Female	25.39	12,148
Kannada	Female	27.02	14,915
Kannada	Male	27.60	16,002
Konkani	Female	26.33	17,585
Maithili	Male	29.34	12,918
Malayalam	Female	26.42	16,974
Malayalam	Male	25.22	16,877
Marathi	Female	28.81	15,473
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	13,422
Punjabi	Male	29.12	15,620
Sanskrit	Female	25.27	12,757
Sanskrit	Male	26.20	11,002
Tamil	Female	29.94	19,871
Telugu	Female	27.29	15,406
Telugu	Male	24.98	15,007
Total		750.62	412,995

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},
}