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Datasets:
dsfsi-anv
/
za-african-next-voices

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
parquet
Languages:
Zulu
Size:
10K - 100K
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files Community
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metadata
dataset_info:
  config_name: zul
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 48000
    - name: language
      dtype: string
    - name: split
      dtype: string
    - name: audio_id
      dtype: string
    - name: recorder_uuid
      dtype: string
    - name: type
      dtype: string
    - name: system_file_name
      dtype: string
    - name: file_name
      dtype: string
    - name: full_path
      dtype: string
    - name: domain
      dtype: string
    - name: topic
      dtype: string
    - name: scenario
      dtype: string
    - name: transcript
      dtype: string
    - name: duration
      dtype: float64
    - name: size_bytes
      dtype: int64
    - name: microphone_device_id
      dtype: string
    - name: microphone_label
      dtype: string
    - name: signal_to_noise_ratio
      dtype: float64
    - name: document_id
      dtype: string
    - name: source_document
      dtype: string
  splits:
    - name: dev
      num_bytes: 4051375741.6
      num_examples: 1668
    - name: dev_test
      num_bytes: 4342849089.914
      num_examples: 1583
    - name: train
      num_bytes: 70154850373.12
      num_examples: 24869
  download_size: 65973852543
  dataset_size: 78549075204.634
configs:
  - config_name: zul
    data_files:
      - split: dev
        path: zul/dev-*
      - split: dev_test
        path: zul/dev_test-*
      - split: train
        path: zul/train-*
license: cc-by-4.0
extra_gated_prompt: >-
  I agree to use this data with the license conditions and use restrictions. You
  agree to NOT USE the data for creating models for any form of text-to-speech
  (TTS), voice cloning, voice synthesis, or any technology intended to replicate
  or generate human voices.
extra_gated_fields:
  Company: text
  Country: country
  Specific date: date_picker
  I want to use this model for:
    type: select
    options:
      - Research
      - Education
      - label: Other
        value: other
  I agree to use this data with the license conditions and use restrictions: checkbox
task_categories:
  - automatic-speech-recognition
pretty_name: z
size_categories:
  - 1M<n<10M
language:
  - zu

⚠️ IMPORTANT: Work in Progress

This dataset is not final. Releases and updates will continue until end of September 2025.

  • Users must regularly check this repository for new versions, corrections, and updates.
  • For attribution, benchmarking, and publications, always reference the latest available information (as of September 2025 or later).
  • Do not cite or benchmark against old/incomplete versions. Use only the most recent release and documentation.

We appreciate your patience while we expand and improve this dataset. Stay updated by watching this repo.

Swivuriso: ZA-African Next Voices

Swivuriso is a large-scale multilingual speech dataset targeting over 3000 hours of audio across 7 South African languages. The dataset is developed to support Automatic Speech Recognition (ASR) and inclusive speech technologies for low-resource African languages. It combines both scripted and unscripted speech, collected through ethical, community-centered processes.

Dataset Paper: ArXiv - Work in Progress

Language Coverage

Language Target Hours Released
isiZulu 500 ▇▇▇▇▇▁▁▁▁ 47%
isiXhosa 500 ▁▁▁▁▁▁▁▁▁▁ 0%
Sesotho 500 ▁▁▁▁▁▁▁▁▁▁ 0%
Setswana 500 ▁▁▁▁▁▁▁▁▁▁ 0%
Xitsonga 500 ▁▁▁▁▁▁▁▁▁▁ 0%
isiNdebele 250 ▁▁▁▁▁▁▁▁▁▁ 0%
Tshivenda 250 ▁▁▁▁▁▁▁▁▁▁ 0%

Use Restriction:

The persons whose voices are included in this dataset, and the creators and owners of this dataset* do not give consent in any manner or form to, and strictly prohibit any use of this dataset for any form of text-to-speech (TTS), voice cloning, voice synthesis, or any technology or activity intended to replicate, mimic or generate human voices or any technology or activity resulting in the replication, mimicry or generation of human voices.

This dataset includes scripted and unscripted speech across various domains such as agriculture, health, finance, sports, transport, culture, society, and general topics. It is primarily designed for use in automatic speech recognition (ASR) tasks.

Use of this dataset for any form of text-to-speech (TTS), voice cloning, voice synthesis, or any technology intended to replicate or generate human voices is strictly prohibited.

These restrictions are in place until further notice.

You can load a language (e.g., zul) using the 🤗 datasets library

from datasets import load_dataset

# Load isiZulu configuration
ds = load_dataset("dsfsi-anv/za-african-next-voices", "zul")

Available Fields

Field Description
audio 48kHz mono .wav audio file (automatically decoded)
transcript Transcribed utterance
recorder_uuid Unique speaker ID
type Scripted or unscripted
domain Thematic domain (e.g., Health, Agriculture, etc.)
duration Duration of the clip in seconds
gender Speaker gender (if available)
age_range Speaker's age group (e.g., 18–29, 30–39)
...

Splits

All data is split into:

  • train (85%)
  • dev (5%)
  • dev_test (5%)
  • test (5%) 🔒 – reserved for future shared tasks/public leaderboards

All splits are speaker-disjoint to ensure reproducibility and avoid data leakage.

License

Creative Commons Attribution 4.0 International (CC BY 4.0)
You are free to use, share, and adapt the data—with proper attribution to the South Africa NextVoices team.

Intended Use

  • Training/fine-tuning ASR models
  • Developing inclusive speech technology for African users
  • Cross-lingual learning and low-resource transfer
  • Model evaluation and benchmarking

⚠️ Limitations and Ethical Use

  • Dialectal diversity and regional accents may not be fully covered
  • The dataset is not intended for surveillance or other harmful use
  • All use must comply with ethical AI principles

Citations

If you use Swivuriso in your work, please cite both of the below:

Dataset 

@dataset{za-african-next-voices- 2025,
  title     = {The South African Next Voices Multilingual Speech Dataset},
  author    = {Vukosi Marivate and Kayode Olaleye and Sitwala Mundia and Nia Zion Van Wyk and Andinda Bakainga and Unarine Netshifhefhe and Mahmooda Milanzie and Hope Tsholofelo Mogale and Chijioke Okorie and Graham Morrissey and Dale Dunbar and Tsosheletso Chidi and Rooweither Mabuya and Andiswa Bukula and Respect Mlambo and Tebogo Macucwa},
  url       = {https://huggingface.co/datasets/dsfsi-anv/za-african-next-voices/},
  url2      = {https://github.com/dsfsi/za-african-next-voices},
  url3      = {https://www.dsfsi.co.za/za-african-next-voices/},
  publisher = {Data Science for Social Impact Research Group},
  year      = {2025},
  type      = {dataset}
}

Research Paper

Will be available soon. 

@dataset{swivuriso2025,
  title     = {Swivuriso: Creating the South African Next Voices Multilingual Speech Dataset},
  author    = {Vukosi Marivate and Kayode Olaleye and Sitwala Mundia and Nia Zion Van Wyk and Andinda Bakainga and Unarine Netshifhefhe and Mahmooda Milanzie and Hope Tsholofelo Mogale and Chijioke Okorie and Graham Morrissey and Dale Dunbar and Tsosheletso Chidi and Rooweither Mabuya and Andiswa Bukula and Respect Mlambo and Tebogo Macucwa},
  url       = {TBD},
  year      = {2025},
}