README.md

---
language:
  - lin  # Lingala
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">

[![EMNLP 2025 Paper](https://img.shields.io/badge/EMNLP_2025-Paper-B31B1B?style=for-the-badge&logo=arxiv&logoColor=B31B1B&labelColor=FFCDD2)](https://aclanthology.org/2025.emnlp-main.559/)
[![Official Website](https://img.shields.io/badge/Official-Website-2EA44F?style=for-the-badge&logo=googlechrome&logoColor=2EA44F&labelColor=C8E6C9)](https://africa.dlnlp.ai/simba/)
[![SimbaBench](https://img.shields.io/badge/SimbaBench-Benchmark-8A2BE2?style=for-the-badge&logo=googlecharts&logoColor=8A2BE2&labelColor=E1BEE7)](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E?style=for-the-badge&logoColor=black&labelColor=FFF9C4)](https://huggingface.co/collections/UBC-NLP/simba-speech-series)

</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())

```