Hugging Face's logo

Datasets:
CLEAR-Global
/
twb-voice-1.0

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
parquet
Languages:
Hausa
Kanuri
Arabic
Size:
10K - 100K
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files Community
1

You need to agree to share your contact information to access this dataset

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

To access this dataset, you must read and agree to the Data Sharing Agreement. By checking the box below, you confirm that:

  • You agree to the terms outlined in the Data Sharing Agreement.
  • You understand that by downloading the dataset, you become a data controller, responsible for complying with applicable data protection laws, including acting on any data subject rights requests (such as deletion or correction).
  • If you redistribute this dataset, you will ensure that the recipients will use it under the same license and Data Sharing Agreement.
  • In the event the dataset is updated in the future or if CLEAR Global receives a data deletion request, we will notify all registered downloaders. It is your responsibility as a data controller to implement such updates or deletions in your environment and other datasets that you might have distributed.

Log in or Sign Up to review the conditions and access this dataset content.

TWB Voice Dataset v1.0

Dataset Summary

TWB Voice 1.0 is a multilingual speech corpus containing read speech data in three languages from Nigeria: Hausa, Shuwa Arabic, and Kanuri. This dataset was created as part of the TWB Voice project by CLEAR Global (formerly Translators without Borders) to support automatic speech recognition (ASR) development for underrepresented languages.

Languages

  • Hausa (hau): Major language spoken in Nigeria, Niger, and neighboring regions. Also widely used as a lingua franca across West Africa
  • Shuwa Arabic (shu): Variety of Arabic spoken by the Shuwa Arab people in the Lake Chad region
  • Kanuri (kau): Language spoken around Lake Chad in Nigeria, Niger, Chad, and Cameroon

Supported Tasks

  • Automatic Speech Recognition (ASR): Primary intended use case
  • Speaker Recognition: Demographic metadata included for speaker analysis
  • Language Identification: Multi-language corpus suitable for language ID tasks
  • Speech Synthesis: Can also be used for TTS model training but you can also check our TTS-focused datasets for
    • Hausa (Link TBA)
    • Kanuri (Link TBA)

Dataset Structure

Data Instances

Each data instance contains:

  • Audio file path and audio data
  • Sentence text
  • Speaker demographic information (age range, gender, education level, country of origin)
  • Recording metadata

Data Fields

  • id: Unique recording identifier
  • path: Audio file path
  • sentence: Read text prompt
  • task_id: Task identifier for the recorded text prompt
  • sentence_source: Source of the text content
  • user_id: Speaker identifier
  • age: Speaker age category (teens (18+), twenties, thirties, forties, fifties, sixties, seventies, over eighty)
  • gender: Speaker gender
  • duration: Audio duration in seconds
  • locale: Language code
  • variant: Language variant/dialect
  • education_level: Speaker’s education level
  • country_of_origin: Speaker’s country of origin
  • created_at: Recording timestamp

Data Splits

Each language configuration contains:

  • train: Training set (~80% of approved recordings)
  • dev: Development/validation set (~10% of approved recordings)
  • test: Test set (~10% of approved recordings)
  • rejected: Recordings that failed quality review
  • pending: Recordings awaiting quality review

Splits are created to ensure no speaker overlap between train/dev/test sets while maintaining approximately 80/10/10 duration ratios.

Dataset Statistics

Hours summary by language and split:

train dev test total approved pending rejected total collected
hau 43.79 4.03 6.56 54.38 1.48 2.25 58.11
kau 33.18 6.27 5.11 44.56 0.37 7.04 51.97
shu 10.03 1.24 1.24 12.51 1.97 0.28 14.76
TOTAL 87.00 11.54 12.91 111.45 3.81 9.57 124.84

Hours approved by gender and language:

male female total
hau 38.82 15.56 54.38
kau 22.54 22.02 44.56
shu 12.40 0.12 12.51
TOTAL 73.76 37.70 111.45

Usage

Loading the Dataset 

from datasets import load_dataset

# Load specific language
hausa_dataset = load_dataset("CLEAR-Global/twb-voice-1.0", "hau")
shuwa_dataset = load_dataset("CLEAR-Global/twb-voice-1.0", "shu")
kanuri_dataset = load_dataset("CLEAR-Global/twb-voice-1.0", "kau")

# Load specific split
train_data = load_dataset("CLEAR-Global/twb-voice-1.0", "hau", split="train")

# Stream data for large datasets
dataset = load_dataset("CLEAR-Global/twb-voice-1.0", "hau", streaming=True)

Dataset Creation

Data Collection

The dataset was collected through TWB Voice platform coordinated by CLEAR Global. Native speakers were asked to read prompted text in their respective languages.

Quality Control

  • All recordings, except for those in rejected and pending splits, underwent human review by native speakers.
  • Only approved recordings are included in train/dev/test splits
  • Rejected recordings and those pending for review are preserved in separate splits for analysis

Ethical Considerations

  • All speakers consented to data collection and open publishing
  • Speaker identities are anonymized using user IDs
  • No attempts should be made to identify individual speakers
  • Data should be used responsibly for technology development

Citation 

@dataset{twb_voice_2024,
  title = {TWB Voice 1.0},
  author = {CLEAR Global},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/CLEAR-Global/twb-voice-1.0}
}

License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

If your intended use of this dataset may be commercial in nature (such as building models into services that you monetize in any way), or you are unsure whether your use complies with the non-commercial restriction, especially for initiatives related to social good or public benefit, we encourage you to contact us to discuss potential licensing options or permissions. We are open to supporting impactful uses beyond the standard license terms. Contact us at [email protected], including a link to the dataset in question and a brief description of your intended use.

Acknowledgments

This dataset was created by CLEAR Global with support from the Patrick J. McGovern Foundation.

Contact

For questions about this dataset, please contact CLEAR Global or open an issue in the dataset repository.

Downloads last month
12