File size: 6,363 Bytes
be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd 57d4c99 7954418 be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd e447ff5 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd 8d98025 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 8fdc1bd be16323 f368055 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
---
language:
- tsn # Tswana
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",
}
```
|