Datasets:
metadata
dataset_info:
- config_name: nbl
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
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num_examples: 2527
- name: dev_test
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num_examples: 2690
- name: train
num_bytes: 71213815817.238
num_examples: 43703
download_size: 67061672724
dataset_size: 78633301266.45201
- config_name: sot
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
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num_examples: 3930
- name: dev_test
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num_examples: 3719
- name: train
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num_examples: 66389
download_size: 137589622154
dataset_size: 166305310218.982
- config_name: tsn
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
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num_examples: 4949
- name: dev_test
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num_examples: 5269
- name: train
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num_examples: 84383
download_size: 134497521300
dataset_size: 168278549084.274
- config_name: tso
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
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num_examples: 3252
- name: dev_test
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num_examples: 4394
- name: train
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num_examples: 67119
download_size: 137973560845
dataset_size: 164785656611.71
- config_name: ven
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
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num_examples: 2360
- name: dev_test
num_bytes: 3438082385.95
num_examples: 1655
- name: train
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num_examples: 36344
download_size: 71383445668
dataset_size: 69858699344.022
- config_name: xho
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
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num_examples: 3768
- name: dev_test
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num_examples: 3639
- name: train
num_bytes: 162178591913.852
num_examples: 62786
download_size: 140078097026
dataset_size: 178715827377.986
- 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: 8160647223.72
num_examples: 3068
- name: dev_test
num_bytes: 8882670407.5
num_examples: 3025
- name: train
num_bytes: 146787407533.58
num_examples: 50386
download_size: 142055351071
dataset_size: 163830725164.8
configs:
- config_name: nbl
data_files:
- split: dev
path: nbl/dev-*
- split: dev_test
path: nbl/dev_test-*
- split: train
path: nbl/train-*
- config_name: sot
data_files:
- split: dev
path: sot/dev-*
- split: dev_test
path: sot/dev_test-*
- split: train
path: sot/train-*
- config_name: tsn
data_files:
- split: dev
path: tsn/dev-*
- split: dev_test
path: tsn/dev_test-*
- split: train
path: tsn/train-*
- config_name: tso
data_files:
- split: dev
path: tso/dev-*
- split: dev_test
path: tso/dev_test-*
- split: train
path: tso/train-*
- config_name: ven
data_files:
- split: dev
path: ven/dev-*
- split: dev_test
path: ven/dev_test-*
- split: train
path: ven/train-*
- config_name: xho
data_files:
- split: dev
path: xho/dev-*
- split: dev_test
path: xho/dev_test-*
- split: train
path: xho/train-*
- 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: za-african-next-voices
size_categories:
- 1M<n<10M
language:
- zul
- sot
- xho
- tsn
- tso
- nde
- ven
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 | ▇▇▇▇▇▇▇▇▇▇ 100% |
| isiXhosa | 500 | ▇▇▇▇▇▇▇▇▇▇ 100% |
| Sesotho | 500 | ▇▇▇▇▇▇▇▇▇▇ 100% |
| Setswana | 500 | ▇▇▇▇▇▇▇▇▇▇ 100% |
| Xitsonga | 500 | ▇▇▇▇▇▇▇▇▇▇ 100% |
| isiNdebele | 250 | ▇▇▇▇▇▇▇▇▇▇ 100% |
| Tshivenda | 250 | ▇▇▇▇▇▇▇▇▇▇ 100% |
- Note that 5% is reserved for future shared tasks/public leaderboards
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")
You can also load the dataset in streaming mode, which is useful when working with large datasets:
ds = load_dataset("dsfsi-anv/za-african-next-voices", "zul", split="dev_test", streaming=True)
Downloading CSV Files Only - Colab Example
from huggingface_hub import hf_hub_download, login
import os
from google.colab import userdata
# Define the repository ID
repo_id = "dsfsi-anv/za-african-next-voices"
# Define variables for split and lang_code
split = "train"
lang_code = "sot"
# Authenticate with Hugging Face using the token from Colab's user data
try:
hf_token = userdata.get('HF_TOKEN')
login(token=hf_token)
print("Successfully authenticated with Hugging Face.")
except Exception as e:
print(f"Error authenticating with Hugging Face: {e}")
# Define the paths of the files to download using the variables
file_paths = [
f"{split}/{lang_code}/meta.csv",
f"{split}/{lang_code}/transcripts.csv",
]
# Download the files
for file_path in file_paths:
try:
downloaded_file_path = hf_hub_download(repo_id=repo_id, filename=file_path, repo_type="dataset", local_dir = "./")
print(f"Downloaded {file_path} to {downloaded_file_path}")
except Exception as e:
print(f"Error downloading {file_path}: {e}")
🔧 Troubleshooting
If you encounter audio decoding errors (e.g., frames with 0 channels, missing decoder attributes), make sure you have compatible versions of the required libraries installed. The dataset has been tested and works with the following:
pip install --upgrade datasets[audio]==3.6.0
pip install --upgrade ffmpeg
pip install --upgrade ffmpeg-python
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 = {Marivate, Vukosi and
Olaleye, Kayode and
Mundia, Sitwala and
Bakainga, Andinda and
Netshifhefhe, Unarine Leo and
Milanzie, Mahmooda and
Mogale, Hope and
SINDANE, THAPELO and
Abdulrasaq, Zainab and
Mokgosi, Kesego and
Okorie, Chijioke and
van Wyk, Nia Zion and
Morrissey, Graham and
Dunbar, Dale and
Smit, Francois and
Chidi, Tsosheletso and
Mabuya, Rooweither and
Bukula, Andiswa and
MLAMBO, RESPECT and
Macucwa, Solomon Tebogo and
Abdulmumin, Idris and
Rananga, Seani},
url2 = {https://github.com/dsfsi/za-african-next-voices},
url3 = {https://www.dsfsi.co.za/za-african-next-voices/},
year = {2025},
type = {dataset},
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.17776289},
url = {https://doi.org/10.5281/zenodo.17776289},
}
Research Paper
@article{marivate2025swivuriso0,
title = {Swivuriso: The South African Next Voices Multilingual Speech Dataset},
author = {Vukosi Marivate and Kayode Olaleye and Sitwala Mundia and Andinda Bakainga and Unarine Netshifhefhe and Mahmooda Milanzie and Tsholofelo Hope Mogale and Thapelo Sindane and Zainab Abdulrasaq and Kesego Mokgosi and Chijioke Okorie and Nia Zion Van Wyk and Graham Morrissey and Dale Dunbar and Francois Smit and Tsosheletso Chidi and Rooweither Mabuya and Andiswa Bukula and Respect Mlambo and Tebogo Macucwa and Idris Abdulmumin and and Seani Rananga},
year = {2025},
journal = {arXiv preprint arXiv: 2512.02201}
}