| --- |
| language: |
| - am |
| - ar |
| - tw |
| - bm |
| - fr |
| - lg |
| - ha |
| - ig |
| - rw |
| - kg |
| - ln |
| - lu |
| - mg |
| - nso |
| - ny |
| - om |
| - pt |
| - sn |
| - so |
| - st |
| - sw |
| - ss |
| - ti |
| - ts |
| - tn |
| - ak |
| - ve |
| - wo |
| - xh |
| - yo |
| - zu |
| - tzm |
| - sg |
| - din |
| - ee |
| - fo |
| - luo |
| - mos |
| - umb |
| 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_simba" alt="VoC Simba Models 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 |
|
|
| 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-ASR |
| > **The New Standard for African Speech-to-Text** |
|
|
| **🎯 Task** `Automatic Speech Recognition` — Powering high-accuracy transcription across the continent. |
|
|
| **🌍 Language Coverage (43 African languages)** |
| > **Amharic** (`amh`), **Arabic** (`ara`), **Asante Twi** (`asanti`), **Bambara** (`bam`), **Baoulé** (`bau`), **Bemba** (`bem`), **Ewe** (`ewe`), **Fanti** (`fat`), **Fon** (`fon`), **French** (`fra`), **Ganda** (`lug`), **Hausa** (`hau`), **Igbo** (`ibo`), **Kabiye** (`kab`), **Kinyarwanda** (`kin`), **Kongo** (`kon`), **Lingala** (`lin`), **Luba-Katanga** (`lub`), **Luo** (`luo`), **Malagasy** (`mlg`), **Mossi** (`mos`), **Northern Sotho** (`nso`), **Nyanja** (`nya`), **Oromo** (`orm`), **Portuguese** (`por`), **Shona** (`sna`), **Somali** (`som`), **Southern Sotho** (`sot`), **Swahili** (`swa`), **Swati** (`ssw`), **Tigrinya** (`tir`), **Tsonga** (`tso`), **Tswana** (`tsn`), **Twi** (`twi`), **Umbundu** (`umb`), **Venda** (`ven`), **Wolof** (`wol`), **Xhosa** (`xho`), **Yoruba** (`yor`), **Zulu** (`zul`), **Tamazight** (`tzm`), **Sango** (`sag`), **Dinka** (`din`). |
|
|
| **🏗️ Base Architectures** |
|
|
| - **Simba-S** (SeamlessM4T-v2-MT) — *Top Performer* |
| - **Simba-W** (Whisper-v3-large) |
| - **Simba-X** (Wav2Vec2-XLS-R-2b) |
| - **Simba-M** (MMS-1b-all) |
| - **Simba-H** (AfriHuBERT) |
| |
| 🌐 Explore the Frontier |
| |
| | **ASR Models** | **Architecture** | **#Parameters** | **🤗 Hugging Face Model Card** | **Status** | |
| |---------|:------------------:| :------------------:| :------------------:|:------------------:| |
| | 🔥**Simba-S**🔥| SeamlessM4T-v2 | 2.3B | 🤗 [https://huggingface.co/UBC-NLP/Simba-S](https://huggingface.co/UBC-NLP/Simba-S) | ✅ Released | |
| | 🔥**Simba-W**🔥| Whisper | 1.5B | 🤗 [https://huggingface.co/UBC-NLP/Simba-W](https://huggingface.co/UBC-NLP/Simba-W) | ✅ Released | |
| | 🔥**Simba-X**🔥| Wav2Vec2 | 1B | 🤗 [https://huggingface.co/UBC-NLP/Simba-X](https://huggingface.co/UBC-NLP/Simba-X) | ✅ Released | |
| | 🔥**Simba-M**🔥| MMS | 1B | 🤗 [https://huggingface.co/UBC-NLP/Simba-M](https://huggingface.co/UBC-NLP/Simba-M) | ✅ Released | |
| | 🔥**Simba-H**🔥| HuBERT | 94M | 🤗 [https://huggingface.co/UBC-NLP/Simba-H](https://huggingface.co/UBC-NLP/Simba-H) | ✅ Released | |
|
|
| * **Simba-S** emerged as the best-performing ASR model overall. |
|
|
|
|
| **🧩 Usage Example** |
|
|
| You can easily run inference using the Hugging Face `transformers` library. |
|
|
| ```python |
| from transformers import pipeline |
| |
| # Load Simba-S for ASR |
| asr_pipeline = pipeline( |
| "automatic-speech-recognition", |
| model="UBC-NLP/Simba-S" #Simba mdoels `UBC-NLP/Simba-S`, `UBC-NLP/Simba-W`, `UBC-NLP/Simba-X`, `UBC-NLP/Simba-H`, `UBC-NLP/Simba-M` |
| ) |
| |
| ##### Load the multilingual African adapter (Only for `UBC-NLP/Simba-M`) |
| asr_pipeline.model.load_adapter("multilingual_african") # Only for `UBC-NLP/Simba-M` |
| ########################### |
| |
| # Transcribe audio from file |
| result = asr_pipeline("https://africa.dlnlp.ai/simba/audio/afr_Lwazi_afr_test_idx3889.wav") |
| print(result["text"]) |
| |
| |
| # Transcribe audio from audio array |
| result = asr_pipeline({ |
| "array": audio_array, |
| "sampling_rate": 16_000 |
| }) |
| print(result["text"]) |
| |
| ``` |
|
|
| #### Example Outputs |
|
|
| Using the same audio file with different Simba models: |
|
|
| ```python |
| # Simba-S |
| {'text': 'watter verontwaardiging sou daar, in ons binneste gewees het.'} |
| ``` |
|
|
| ```python |
| # Simba-W |
| {'text': 'watter veronwaardigingsel daar, in ons binneste gewees het.'} |
| ``` |
|
|
| ```python |
| # Simba-X |
| {'text': 'fator fr on ar taamsodr is'} |
| ``` |
|
|
| ```python |
| # Simba-M |
| {'text': 'watter veronwaardiging sodaar in ons binniste gewees het'} |
| ``` |
|
|
| ```python |
| # Simba-H |
| {'text': 'watter vironwaardiging so daar in ons binneste geweeshet'} |
| ``` |
|
|
| Get started with Simba models in minutes using our interactive Colab notebook: [](https://github.com/UBC-NLP/simba/blob/main/simba_models.ipynb) |
|
|
|
|
| ## 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", |
| } |
| |
| ``` |
|
|
|
|
