---
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](https://www.yorubavoice.com/about/)
- **Funded by [optional]: [Imminent](https://imminent.translated.com/making-machines-speak-yoruba)
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** Yorùbá
- **License:** CC-BY-NC 4.0

### Dataset Sources [optional]

- **Repository:** [NigerVolta YorubaVoice GitHub](https://github.com/Niger-Volta-LTI/yoruba-voice)
- **Paper [optional]:** [IroyinSpeech](https://aclanthology.org/2024.lrec-main.812/) published at LREC-COLING 2024
- **Demo [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."
}
}
```