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---
language:
- afr # Afrikaans
license: cc-by-4.0
tags:
- automatic-speech-recognition
- audio
- speech
- african-languages
- multilingual
- simba
- low-resource
- speech-recognition
- asr
datasets:
- UBC-NLP/SimbaBench
metrics:
- wer
- cer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
<div align="center">
<img src="https://africa.dlnlp.ai/simba/images/VoC_logo.png" alt="VoC Logo">
[](https://aclanthology.org/2025.emnlp-main.559/)
[](https://africa.dlnlp.ai/simba/)
[](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
[](https://github.com/UBC-NLP/simba)
[](https://huggingface.co/collections/UBC-NLP/simba-speech-series)
[](https://huggingface.co/datasets/UBC-NLP/SimbaBench_dataset)
</div>
## *Bridging the Digital Divide for African AI*
**Voice of a Continent** is a comprehensive open-source ecosystem designed to bring African languages to the forefront of artificial intelligence. By providing a unified suite of benchmarking tools and state-of-the-art models, we ensure that the future of speech technology is inclusive, representative, and accessible to over a billion people.
## Best-in-Class Multilingual Models
<img src="https://africa.dlnlp.ai/simba/images/VoC_simba" alt="VoC Simba Models Logo">
Introduced in our EMNLP 2025 paper *[Voice of a Continent](https://aclanthology.org/2025.emnlp-main.559/)*, the **Simba Series** represents the current state-of-the-art for African speech AI.
- **Unified Suite:** Models optimized for African languages.
- **Superior Accuracy:** Outperforms generic multilingual models by leveraging SimbaBench's high-quality, domain-diverse datasets.
- **Multitask Capability:** Designed for high performance in ASR (Automatic Speech Recognition) and TTS (Text-to-Speech).
- **Inclusion-First:** Specifically built to mitigate the "digital divide" by empowering speakers of underrepresented languages.
The **Simba** family consists of state-of-the-art models fine-tuned using SimbaBench. These models achieve superior performance by leveraging dataset quality, domain diversity, and language family relationships.
### 🔊 Simba-TTS (Text-to-Speech)
* **🎯 Task:** `Text-to-Speech` — Natural Voice Synthesis.
**🌍 Language Coverage (7 African languages)**
> **Afrikaans** (`afr`), **Asante Twi** (`asanti`), **Akuapem Twi** (`akuapem`), **Lingala** (`lin`), **Southern Sotho** (`sot`), **Tswana** (`tsn`), **Xhosa** (`xho`)
| **TTS Model** | **Architecture** | **Hugging Face Card** | **Status** |
| :--- | :--- | :---: | :---: |
| **Simba-TTS-afr** 🔊 | MMS-TTS | 🤗 [https://huggingface.co/UBC-NLP/Simba-TTS-afr](https://huggingface.co/UBC-NLP/Simba-TTS-afr) | ✅ Released |
| **Simba-TTS-twi-asanti** 🔊 | MMS-TTS | 🤗 [https://huggingface.co/UBC-NLP/Simba-TTS-twi-asanti](https://huggingface.co/UBC-NLP/Simba-TTS-twi-asanti) | ✅ Released |
| **Simba-TTS-twi-akuapem** 🔊 | MMS-TTS | 🤗 [https://huggingface.co/UBC-NLP/Simba-TTS-twi-akuapem](https://huggingface.co/UBC-NLP/Simba-TTS-twi-akuapem) | ✅ Released |
| **Simba-TTS-lin** 🔊 | MMS-TTS | 🤗 [https://huggingface.co/UBC-NLP/Simba-TTS-lin](https://huggingface.co/UBC-NLP/Simba-TTS-lin) | ✅ Released |
| **Simba-TTS-sot** 🔊 | MMS-TTS | 🤗 [https://huggingface.co/UBC-NLP/Simba-TTS-sot](https://huggingface.co/UBC-NLP/Simba-TTS-sot) | ✅ Released |
| **Simba-TTS-tsn** 🔊 | MMS-TTS | 🤗 [https://huggingface.co/UBC-NLP/Simba-TTS-tsn](https://huggingface.co/UBC-NLP/Simba-TTS-tsn) | ✅ Released |
| **Simba-TTS-xho** 🔊 | MMS-TTS | 🤗 [https://huggingface.co/UBC-NLP/Simba-TTS-xho](https://huggingface.co/UBC-NLP/Simba-TTS-xho) | ✅ Released |
**🧩 Usage Example**
You can easily run inference using the Hugging Face `transformers` library.
```python
from transformers import VitsModel, AutoTokenizer
import torch
model_name="Simba-TTS-afr" ## Simba-TTS-twi-asanti, Simba-TTS-twi-akuapem, Simba-TTS-lin, Simba-TTS-sot, Simba-TTS-tsn, Simba-TTS-xho
model = VitsModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
text = "Ons noem hierdie deeltjies sub-atomiese deeltjies" #example of Afrikaans (afr) language
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a .wav file:
```python
scipy.io.wavfile.write("outputfile.wav", rate=model.config.sampling_rate, data=output.float().numpy())
```
## Citation
If you use the Simba models or SimbaBench benchmark for your scientific publication, or if you find the resources in this website useful, please cite our paper.
```bibtex
@inproceedings{elmadany-etal-2025-voice,
title = "Voice of a Continent: Mapping {A}frica{'}s Speech Technology Frontier",
author = "Elmadany, AbdelRahim A. and
Kwon, Sang Yun and
Toyin, Hawau Olamide and
Alcoba Inciarte, Alcides and
Aldarmaki, Hanan and
Abdul-Mageed, Muhammad",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.559/",
doi = "10.18653/v1/2025.emnlp-main.559",
pages = "11039--11061",
ISBN = "979-8-89176-332-6",
}
```
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