Africanvoice/African_voices_hausa

🇳🇬 WaZoBiaSpeech: 1,000+ Hour Hausa (hau) Corpus

Version: 30 Nov 2025

NOTE: This dataset is subject to regular Updates, corrections, and expansions. Please check this repository regularly for the latest release.

🌍 Dataset Overview

WaZoBiaSpeech is a large-scale, high-quality, fully transcribed speech dataset for Hausa (hau). This corpus is designed to accelerate the development of speech technology in African contexts, promoting linguistic diversity and supporting low-resource machine learning research.

The data includes both scripted and unscripted recordings collected through ethical, community-centered processes with broad demographic coverage.

Current Language Coverage: Hausa (hau)

🛠️ Dataset Structure and Features

The dataset is provided with the following features:

Summary Statistics (Nigerian Pidgin)

• —Total Clips: 47,661
• —Total Hours: 509.53 h
• —Gender Split: 50.3% Male / 49.7% Female

💻 Loading the Dataset (Hugging Face)

The dataset is configured for easy loading of the Hausa (hau) subset.

Recommended Environment

pip install --upgrade datasets[audio]
pip install --upgrade ffmpeg ffmpeg-python

Standard Load

from datasets import load_dataset

# Load the full training split
ds = load_dataset("Africanvoice/African_voices_hausa", "hau", split="train")

# Load a specific split (e.g., development)
ds_dev = load_dataset("Africanvoice/African_voices_hausa", "hau", split="dev")

Streaming Mode (for memory efficiency)

from datasets import load_dataset

# Load the dev_test split in streaming mode
ds_stream = load_dataset(
    "Africanvoice/African_voices_hausa", 
    "hau", 
    split="dev_test", 
    streaming=True
)

🚀 Intended Use & Applications

This dataset is specifically designed for the following purposes:

• —Automatic Speech Recognition (ASR) Training
• —NLP for low-resource African languages
• —Cross-lingual learning and transfer learning research
• —Evaluation of multilingual ASR systems
• —Linguistic research and accent/dialect modeling

⛔ Use Restrictions & Limitations

To protect speaker privacy and prevent potential voice misuse, the following uses are strictly enforced:

Strictly Prohibited Uses 🛑

• —Voice cloning or adaptation (Text-to-Speech/TTS)
• —Voice biometrics, speaker identification, or mimicry
• —Surveillance, profiling, or any commercial application that relies on identifying or imitating individuals

Limitations ⚠️

• —Regional accent variation, while broad, is not entirely exhaustive
• —Spontaneous (unscripted) speech segments may include natural, low-level background noise
• —Not suitable for biometric or forensic use

⚖️ License and Citation

License

This dataset is released under the Creative Commons Attribution 4.0 (CC BY 4.0) license.

Citation

@dataset{wazobiaspeech-2025,
  title   = {WaZoBiaSpeech: A 2,500-Hour Multilingual Speech Corpus for Hausa, Igbo, Nigerian Pidgin, and Yoruba},
  author  = {EqualyzAI and African Voices Team},
  year    = {2025},
  url     = {https://huggingface.co/datasets/Africanvoice/African_voices_hausa},
  note    = {Hausa subset (hau) version},
  type    = {dataset}
}

📧 Contact & Support

For questions, issues, or collaboration inquiries, please open an issue on the repository or contact the maintainers directly.

Last Updated: November 30, 2025 Maintainer: Data Science Nigeria / EqualyzAI