Datasets:
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---
dataset_info:
features:
- name: dyu_id
dtype: string
- name: fr_id
dtype: string
- name: dyu
dtype: string
- name: fr
dtype: string
- name: en
dtype: string
- name: gender
dtype: string
- name: age_group
dtype: int64
- name: duration
dtype: float64
- name: sampling_rate
dtype: int64
- name: country
dtype: string
- name: commonvoice_split
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 44100
splits:
- name: train
num_bytes: 3987571272.8
num_examples: 8065
- name: dev
num_bytes: 949449909.48
num_examples: 1471
- name: test
num_bytes: 918543551.584
num_examples: 1393
download_size: 4929377555
dataset_size: 5855564733.864
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
task_categories:
- automatic-speech-recognition
- translation
language:
- bm
size_categories:
- 1K<n<10K
---
# Koumankan4Dyula: A Speech-To-Text Translation Corpus For Dyula Language
## Overview
The Koumankan4Dyula corpus consists of approximately 15 hours i.e. 10,929 recordings of Dioula language audio along with the corresponding texts and their translations into French and English.
Dioula is a low-resource language spoken by over 16.4 million people in several West African countries.
This corpus is part of the Koumankan project, which offers a scalable and cost-effective method to expand the CommonVoice dataset to include the Dyula language and other African languages.
It will serve as a benchmark for training models for automatic speech translation in Dioula as well as for automatic speech recognition or machine translation models from Dioula to French and English.
## Data Splits
| split |percentage|Nm. records| Num hour |
|:-----:|:--------:|----------:|---------:|
| Train | 73% | 8065 | 8h 9m |
| Valid | 14% | 1471 | 1h 36m |
| Test | 13% | 1393 | 44m 36s |
||
### Maintenance
* This dataset is supposed to be actively maintained.
### Benchmarks:
- `Coming soon`
## License
- `CC-BY-SA-4.0`
## Version
- `1.0.0`
## Acknowledgements
This dataset collection efforts have been supported by International Development Research Centre (IDRC) and Swedish International Development Cooperation Agency (SIDA),
managed by African Center for Technology Studies (ACTS) in collaboration with the Université Virtuelle de Côte d'Ivoire (UVCI) through the programme Artificial Intelligence for Development (AI4D) Africa.
## Contacts
- `ismael21.kone@uvci.edu.ci`
- `info@data354.com` |