README.md

---
pretty_name: Vuk'uzenzele isiXhosa Speech Dataset (ViXSD)
annotations_creators:
- Way With Words
language_creators:
- isiXhosa Community
language:
- xho
tags:
  - Esethu License
  - nlp
  - africa
dataset_info:
  features:
  - name: idx
    dtype: int64
  - name: user_ids
    dtype: string
  - name: accent
    dtype: string
  - name: age_group
    dtype: string
  - name: country
    dtype: string
  - name: transcript
    dtype: string
  - name: nchars
    dtype: int64
  - name: audio_ids
    dtype: string
  - name: audio_paths
    dtype: audio
  - name: duration
    dtype: string
  - name: origin
    dtype: string
  - name: domain
    dtype: string
  - name: split
    dtype: string
  - name: gender
    dtype: string
  - name: title
    dtype: string
  - name: author
    dtype: string
  - name: location_born
    dtype: string
  - name: location_childhood
    dtype: string
  - name: location_current
    dtype: string
  - name: first spoken language
    dtype: string
  - name: multi-lingualism_and_confidence
    dtype: string
  splits:
  - name: dev
    num_bytes: 1856816319.0
    num_examples: 65
  - name: test
    num_bytes: 521585772.0
    num_examples: 26
  - name: train
    num_bytes: 6217050561.0
    num_examples: 304
  download_size: 6454404912
  dataset_size: 8595452652.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
license: other
---

# Dataset Card for Vuk'uzenzele isiXhosa Speech Dataset (ViXSD)

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

### Dataset Summary

Vuk'uzenzele isiXhosa Speech Dataset (ViXSD) contains scripted narration of the Vuk’uzenzele South African Multilingual Corpus. 
ViXSD contains read speech from native speakers accompanied with rich metadata on speaker demographic and linguistic distribution.
ViXSD consists of 395 stereo audio recordings and corresponding transcriptions derived from the Vuk’uzenzele South African Multilingual Corpus. 
It contains a total of 10 hours of narrated speech isiXhosa narrated by 8 speakers (4 male, 4 female) with approximately 39,000 words. 
We split the data into train, dev and test split for ease of use. 

- **Paper:** coming soon 
- **Point of Contact:** [Lelapa AI](mailto:[email protected])
- **Data Sheet:** [Datasheet(pdf)](https://drive.google.com/drive/u/1/search?q=datasheet)
- **License:** [Esethu License](https://huggingface.co/datasets/lelapa/Vukuzenzele_isiXhosa_Speech_Dataset_ViXSD/blob/main/ESETHU_LICENSE.md)


### Languages

```
isiXhosa
```

## How to use

The `datasets` library allows you to load and pre-process your dataset in pure Python. 
The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 

For example:
```python
from datasets import load_dataset, DatasetDict
visxd_train = load_dataset("lelapa/isixhosa_community_dataset", split = 'train')
```

Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
visxd_train = load_dataset("lelapa/isixhosa_community_dataset", split = 'train', streaming=True)
```

### Example scripts

Fine-tune your own Automatic Speech Recognition models on ViXSD with `mms` - [here](https://huggingface.co/blog/mms_adapters).

## Dataset Structure

### Data Instances

A typical data point comprises the `audio_paths` to the audio file and its `transcript`. 
Additional fields include 'idx', 'user_ids', 'accent', 'age_group', 'country', 'nchars', 'audio_ids', 'duration', 'origin', 'domain', 'split', 'gender', 'title', 'author', 'location_born', 'location_childhood', 'location_current', 'first spoken language', 'multi-lingualism_and_confidence'.

```python
{'idx': 2,
 'user_ids': 'xho_reader_003',
 'accent': 'xho',
 'age_group': '30 - 39',
 'country': 'ZA',
 'transcript': 'URakwena uthi le mali ichithwa kule nkqubo iyongezwa rhoqo ngonyaka kulungiselelwa umba wokunyuka kwamaxabiso ezinto, ke ngoko imali ebekelwe i-NSNP yowama-2017/18 imalunga neebhiliyoni eziyi-6.4 zeerandi. I-NSNP ilicebo lexesha elifutshane lokugxotha ikati eziko kwaye inegalelo elihle kakhulu kubomi babafundi.Abantwana abaninzi baphuma kumakhaya angathathi ntweni, angenamali yokuthenga into esiwa phantsi kwempumlo, kwaye abanye abantwana oku kutya bakufumana esikolweni, yiyo yodwa abayityayo ngosuku. Ukutya abakufumana esikolweni kuyabanceda abafundi kwizifundo zabo, bakwazi ukuhlala bemamele bethatha nenxaxheba kwizifundo zabo, utshilo. URakwena wongeze ngelithi amanani abantwana abaya esikolweni enyukile futhi abantwana basihamba kakuhle isikolo ngenxa yale nkqubo yokutyiswa kwabantwana ezikolweni. ',
 'nchars': 813,
 'audio_ids': 'cds_xho_001_2',
 'audio_paths': {'path': 'cds_xho_001_2.wav',
  'array': array([ 0.00334167,  0.00346375,  0.003479  , ..., -0.00041199,
         -0.00045776, -0.00048828]),
  'sampling_rate': 44100},
 'duration': '0:00:55',
 'origin': "Vuk'uzenzele-2018-01-ed1",
 'domain': 'General',
 'split': 'train',
 'gender': 'Female',
 'title': 'Abantwana abahluthiyo baqhuba kakuhle kwizifundo zabo',
 'author': 'More Matshediso',
 'location_born': 'Eastern Cape, South Africa',
 'location_childhood': 'Western Cape, South Africa',
 'location_current': 'Western Cape, South Africa',
 'first spoken language': 'isiXhosa',
 'multi-lingualism_and_confidence': 'English - 10; isiXhosa - 10; isiZulu - 9'}
```

### Data Fields


`idx` (`int64`): An id for the audio file

`user_ids` (`string`): An id for the speaker

`accent` (`string`): Accent of the speaker 'xho'

`age_group` (`string`): Age group of the speaker

`country` (`string`): The locale of the speaker 

`transcript` (`string`): The sentence the user was prompted to speak

`audio_ids` (`string`): Unique name of the audio file

`audio_paths` (`audio`): The path to the audio file

`duration` (`string`): Duration of the audio file (00:00:00)

`origin` (`string`): Reference to Vuk'uzenzele article

`domain` (`string`): Domain of the transcript ('General')

`split` (`string`): Train/Dev/Test

`gender` (`string`): The gender of the speaker

`title` (`string`): Title of the Vuk'uzenzele article

`author` (`string`): Author of the Vuk'uzenzele article

`location_childhood` (`string`): Author of the Vuk'uzenzele article

`location_current` (`string`): Author of the Vuk'uzenzele article

`first spoken language` (`string`): Author of the Vuk'uzenzele article

`multi-lingualism_and_confidence` (`string`): Author of the Vuk'uzenzele article


### Data Splits

| Split | Participants | Gender | Age | Total Duration | Size | Avg Duration | SNR  |
|---------------------------------|----------------------------------------|-----------------|--------------|-------------------------|------------------|-----------------------|---------------|
|                                 |                                        | male/female     | <29/<39/<49  | h:mm:ss                 | \#transcriptions | mm:ss per file        | dB            |
| train                           | 4                                      | 2/2             | 1/3/0        | 7:47:01                 | 304              | 01:32                 | -0.01         |
| dev                             | 2                                      | 1/1             | 0/2/0        | 1:33:00                 | 65               | 01:26                 | 0.44          |
| test                            | 2                                      | 1/1             | 0/1/1        | 0:40:13                 | 26               | 01:33                 | -0.15         |


## Dataset Creation

The dataset contains scripted narration of the Vuk’uzenzele South African Multilingual Corpus.

The audios were narrated by equal representation of male and females with education levels ranging from NQF Level 5 to Bachelors Degrees. 
The speakers are within the age range of 18-40 years, with 12.5\% being between 18–29 years, 75.5\% between 30–39 years and 12.5\% being 40 years. 
We focus primarily on isiXhosa with occasional code-switching where the script included non-isiXhosa terms (e.g., organizational names in English). 
We designed our selection process to reflect inclusivity within isiXhosa-speaking South African society. 
Where a person was born, has grown up and lives has a big impact on their accent as well as the vocabulary they will use when speaking their language.

### Personal and Sensitive Information

The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in the Common Voice dataset.

## Considerations for Using the Data

### Social Impact of Dataset

isiXhosa, a Bantu language spoken by over 9 million people in South Africa. 
isiXhosa is notably underserved despite its sizable population. 

### Discussion of Biases

The creation of this isiXhosa dataset necessitates careful consideration of ethical issues, particularly regarding data privacy, cultural representation, and linguistic bias. 
The dataset is derived from publicly available material, ensuring compliance with copyright and data protection regulations. 
Personally identifiable information (PII) is either absent or anonymised to safeguard individual privacy.

Given the dataset’s focus on news type articles, it may exhibit thematic and linguistic biases. 
Efforts have been made to preserve dialectal diversity and cultural authenticity, yet idiomatic expressions and culturally specific references may require careful interpretation in downstream NLP applications. 
Researchers should be cognisant of potential stereotypical or skewed linguistic representations when applying models trained on this corpus.

### Ethical considerations

Given the dataset’s focus on news type articles, it may exhibit thematic and linguistic biases. 
Efforts have been made to preserve dialectal diversity and cultural authenticity, yet idiomatic expressions and culturally specific references may require careful interpretation in downstream NLP applications. 
Researchers should be cognisant of potential stereotypical or skewed linguistic representations when applying models trained on this corpus.

## Additional Information

### Licensing Information

Esethu License.

The dataset was developed with an ethical approach to data licensing as a central consideration in the release process. 
The creators of the dataset are committed to ensuring that the primary beneficiaries of the dataset are: 
(a) the contributors, in this case, the isiXhosa community, and (b) the broader African language technology ecosystem. 

We have developed a novel license for this dataset. 
This license consists of two components: a commercial/proprietary license and an open license. 
In essence, the license does not restrict research use and permits commercial use by African entities. However, non-African commercial entities are required to pay a licensing fee. 

Proceeds from the dataset are legally mandated to be reinvested in the creation of more isiXhosa data through a local data creation partner, which will be released under the same license. 
This ensures a circular system that not only promotes the generation of additional isiXhosa data, benefiting the isiXhosa community through language technology opportunities, but also guarantees that the revenue generated from the dataset supports fairly paid work for isiXhosa speakers. 
By ensuring that the data is accessible to African commercial entities, we aim to foster innovation within the African language ecosystem while preserving opportunities for research on the language.


### Citation Information

```
@misc{rajab2025esethuframeworkreimaginingsustainable,
      title={The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages}, 
      author={Jenalea Rajab and Anuoluwapo Aremu and Everlyn Asiko Chimoto and Dale Dunbar and Graham Morrissey and Fadel Thior and Luandrie Potgieter and Jessico Ojo and Atnafu Lambebo Tonja and Maushami Chetty and Onyothi Nekoto and Pelonomi Moiloa and Jade Abbott and Vukosi Marivate and Benjamin Rosman},
      year={2025},
      eprint={2502.15916},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.15916}, 
}
```