Datasets:
File size: 3,333 Bytes
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---
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."
}
}
``` |