---
license: cc-by-nc-sa-4.0
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- africa
- numbers
- names
- read-speech
pretty_name: Afri-names
dataset_info:
  size: 8.92h
  samples: 6307
---

# Afri-names: Read Speech Dataset of Numbers and African Named Entities

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This work is licensed under a  
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/  
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png  
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg  



### Overview

**Afri-names** is a curated African-accented read speech dataset comprising **6,307 single-speaker audio samples**, totaling **8.92 hours** of speech data. Each sample is densely populated with **numbers** or **African named entities** or **voice commands (with African named entities)**, making it ideal for **ASR (Automatic Speech Recognition)** and **Named Entity Recognition (NER)** tasks. The recordings span 12 distinct African accents across 4 countries.



### Dataset Summary

|                                | Numbers | Names | Commands | Combined |
|--------------------------------|----------------|--------------|--------------|----------|
| **Num. of Samples**            | 914              | 3,121  |  2,272        | 6,307    |
| **Avg. Duration per Sample (seconds)**   | 15.43              | 2.52  |   4.46       | 5.64 |
| **Total Duration (hrs)**       | 3.92              | 2.18  |     2.81      | 8.92     |
| **Countries Represented**      | 2           | 3      | 3    | 4        |
| **Distinct Accents**           | 4              | 6   |  5       | 12       |
| **Age Range (yrs)**                  | 19–55              | 19–55 |      19-40     | 19–55|



### Use Cases

- Evaluation of **ASR systems** on African-accented English
- Testing **Named Entity Recognition (NER)** pipelines on spoken data



### Dataset Structure

Each row in the dataset includes:

- `file_name`: Path to the `.wav` audio file
- `transcript`: The provided transcript corresponding to the audio
- `domain`: Either `"numbers"`, `"names"` or `"commands"`
- `accent`: Primary accent of the speaker
- `country`: Country code of the speaker (e.g., NG, ZA)
- `age_group`: Age group of the speaker
- `duration`: Duration of the recording in seconds

All recordings are single-speaker, recorded at **16kHz** in **mono-channel** format.



### Accents and Countries

- **Accents**: Ijaw, Yoruba, Igbo, Shona, Akan, Swahili, Kikuyu, Kamba, Hausa, Zulu, Tswana  
- **Countries**: Nigeria (NG), South Africa (ZA), Ghana (GH), Kenya (KE)



### Data Collection and Processing

- **Collection Method**: Text prompts (names or numbers) were provided to contributors, who recorded the corresponding audios in varied, but generally quiet, acoustic environments.
- **Annotation**: As texts were predefined, all transcripts are clean and verified. No additional timestamp-level annotation is provided.



### Data Split

This release is a **test-only** dataset and does not include predefined train/dev splits.



### How to Load the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("intronhealth/afri-names")