Simba-SLID-49

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-SLID (Spoken Language Identification)

• 🎯 Task: Spoken Language Identification — Intelligent input routing.
• 🌍 Language Coverage (49 African languages)

  Akuapim Twi (Akuapim-twi), Asante Twi (Asante-twi), Tunisian Arabic (aeb), Afrikaans (afr), Amharic (amh), Arabic (ara), Basaa (bas), Bemba (bem), Taita (dav), Dyula (dyu), English (eng), Nigerian Pidgin (eng-zul), Ewe (ewe), Fanti (fat), Fon (fon), Pulaar (fuc), Pular (fuf), Ga (gaa), Hausa (hau), Igbo (ibo), Kabyle (kab), Kinyarwanda (kin), Kalenjin (kln), Lingala (lin), Lozi (loz), Luganda (lug), Luo (luo), Western Maninkakan (mlq), South Ndebele (nbl), Northern Sotho (nso), Chichewa (nya), Southern Sotho (sot), Serer (srr), Swati (ssw), Susu (sus), Kiswahili (swa), Swahili (swh), Tigre (tig), Tigrinya (tir), Tonga (toi), Tswana (tsn), Tsonga (tso), Twi (twi), Venda (ven), Wolof (wol), Xhosa (xho), Yoruba (yor), Standard Moroccan Tamazight (zgh), Zulu (zul)

🧩 Usage Example

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

from transformers import (
    HubertForSequenceClassification,
    AutoFeatureExtractor,
    AutoProcessor
)
import torch

model_id = "UBC-NLP/Simba-SLIS_49"
model = HubertForSequenceClassification.from_pretrained(model_id).to("cuda")
# HuBERT models can use either processor or feature extractor depending on the specific model
try:
    processor = AutoProcessor.from_pretrained(model_id)
    print("Loaded Simba-SLIS_49 model with AutoProcessor")
except:
    processor = AutoFeatureExtractor.from_pretrained(model_id)
    print("Loaded Simba-SLIS_49 model with AutoFeatureExtractor")

# Optimize model for inference
model.eval()
audio_arrays = [] ### add your audio array
sample_rate=16000

nputs = processor(audio_arrays, sampling_rate=sample_rate, return_tensors="pt", padding=True).to("cuda")
    
# Different models might have slightly different input formats
try:
    logits = model(**inputs).logits
except Exception as e:
    # Try alternative input format if the first attempt fails
    if "input_values" in inputs:
        logits = model(input_values=inputs.input_values).logits
    else:
        raise e

# Calculate softmax probabilities
probs = torch.nn.functional.softmax(logits, dim=-1)

# Get the maximum probability (confidence) for each prediction
confidence_values, pred_ids = torch.max(probs, dim=-1)

# Convert to Python lists
pred_ids = pred_ids.tolist()
confidence_values = confidence_values.cpu().tolist()
# Get labels from IDs
pred_labels = [model.config.id2label[i] for i in pred_ids]


print(pred_labels, confidence_values)

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",
}