UBC-NLP / Simba-S

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 , 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

• Simba-S emerged as the best-performing ASR model overall.

🧩 Usage Example

You can easily run inference using the Hugging Face transformers library.

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:

# Simba-S
{'text': 'watter verontwaardiging sou daar, in ons binneste gewees het.'}

# Simba-W
{'text': 'watter veronwaardigingsel daar, in ons binneste gewees het.'}

# Simba-X
{'text': 'fator fr on ar taamsodr is'}

# Simba-M
{'text': 'watter veronwaardiging sodaar in ons binniste gewees het'}

# Simba-H
{'text': 'watter vironwaardiging so daar in ons binneste geweeshet'}

Get started with Simba models in minutes using our interactive Colab notebook:

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.

@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",
}