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Datasets:
Niger-Volta-LTI
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IroyinSpeech-Yoruba-TTS-test

Tasks:
Text-to-Speech
Modalities:
Audio
Text
Formats:
parquet
Languages:
Yoruba
Size:
< 1K
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metadata
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: transcript
      dtype: string
    - name: transcript_no_diacritics
      dtype: string
  splits:
    - name: male
      num_bytes: 48393851
      num_examples: 100
    - name: female
      num_bytes: 70695334
      num_examples: 100
  download_size: 117652455
  dataset_size: 119089185
configs:
  - config_name: default
    data_files:
      - split: male
        path: data/male-*
      - split: female
        path: data/female-*
license: cc
task_categories:
  - text-to-speech
language:
  - yo
pretty_name: Yoruba-TTS-Test
size_categories:
  - n<1K

IroyinSpeech TTS dataset

IroyinSpeech TTS provides an high-quality speech synthesis dataset for Yorùbá language including male and female genders.

Dataset Description

  • Curated by: YorubaVoice Team
  • **Funded by [optional]: Imminent
  • Shared by [optional]: [More Information Needed]
  • Language(s) (NLP): Yorùbá
  • License: CC-BY-NC 4.0

Dataset Sources [optional]

Uses

For automatic speech synthesis or text-to-speech of both male and female gender

Citation 

@inproceedings{ogunremi-etal-2024-iroyinspeech,
    title = "{{\`I}}r{\`o}y{\`i}n{S}peech: A Multi-purpose {Y}or{\`u}b{\'a} Speech Corpus",
    author = "Ogunremi, Tolulope  and
      Tubosun, Kola  and
      Aremu, Anuoluwapo  and
      Orife, Iroro  and
      Adelani, David Ifeoluwa",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.812/",
    pages = "9296--9303",
    abstract = "We introduce {\`I}r{\`o}y{\`i}nSpeech corpus{---}a new dataset influenced by a desire to increase the amount of high quality, freely available, contemporary Yor{\`u}b{\'a} speech data that can be used for both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) tasks. We curated about 23,000 text sentences from the news and creative writing domains with an open license i.e., CC-BY-4.0 and asked multiple speakers to record each sentence. To encourage more participatory approach to data creation, we provide 5 000 utterances from the curated sentences to the Mozilla Common Voice platform to crowd-source the recording and validation of Yor{\`u}b{\'a} speech data. In total, we created about 42 hours of speech data recorded by 80 volunteers in-house, and 6 hours validated recordings on Mozilla Common Voice platform. Our evaluation on TTS shows that we can create a good quality general domain single-speaker TTS model for Yor{\`u}b{\'a} with as little 5 hours of speech by leveraging an end-to-end VITS architecture. Similarly, for ASR, we obtained a WER of 21.5."
}
}