📑 Table of Contents

• Best-in-Class Multilingual Models
  • 🗣️✍️ Simba-ASR (Automatic Speech Recognition)
  • 🔊 Simba-TTS (Text-to-Speech)
  • 🔍 Simba-SLID (Spoken Language Identification)
• SimbaBench Data Release & Benchmarking
  • How to Use SimbaBench
  • 📌 ASR Evaluation Configurations
  • 📌 TTS Evaluation Configurations
  • 📌 SLID Evaluation
• Citation

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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.

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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

🎯 TaskAutomatic 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).

🌐 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	✅ Released
🔥Simba-W🔥	Whisper	1.5B	🤗 https://huggingface.co/UBC-NLP/Simba-W	✅ Released
🔥Simba-X🔥	Wav2Vec2	1B	🤗 https://huggingface.co/UBC-NLP/Simba-X	✅ Released
🔥Simba-M🔥	MMS	1B	🤗 https://huggingface.co/UBC-NLP/Simba-M	✅ Released
🔥Simba-H🔥	HuBERT	94M	🤗 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.

fromtransformersimportpipeline# Load Simba-S for ASRasr_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 fileresult=asr_pipeline("https://africa.dlnlp.ai/simba/audio/afr_Lwazi_afr_test_idx3889.wav")
print(result["text"])
# Transcribe audio from audio arrayresult=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:

🔊 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	✅ Released
Simba-TTS-twi-asanti 🔊	MMS-TTS	🤗 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	✅ Released
Simba-TTS-lin 🔊	MMS-TTS	🤗 https://huggingface.co/UBC-NLP/Simba-TTS-lin	✅ Released
Simba-TTS-sot 🔊	MMS-TTS	🤗 https://huggingface.co/UBC-NLP/Simba-TTS-sot	✅ Released
Simba-TTS-tsn 🔊	MMS-TTS	🤗 https://huggingface.co/UBC-NLP/Simba-TTS-tsn	✅ Released
Simba-TTS-xho 🔊	MMS-TTS	🤗 https://huggingface.co/UBC-NLP/Simba-TTS-xho	✅ Released

🧩 Usage Example

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

fromtransformersimportVitsModel, AutoTokenizerimporttorchmodel_name="Simba-TTS-afr"## Simba-TTS-twi-asanti, Simba-TTS-twi-akuapem, Simba-TTS-lin, Simba-TTS-sot, Simba-TTS-tsn, Simba-TTS-xhomodel=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")
withtorch.no_grad():
output=model(**inputs).waveform

The resulting waveform can be saved as a .wav file:

scipy.io.wavfile.write("outputfile.wav", rate=model.config.sampling_rate, data=output.float().numpy())

Or displayed in a Jupyter Notebook / Google Colab:

fromIPython.displayimportAudioAudio(output.numpy(), rate=model.config.sampling_rate)

🔍 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)

SLID Model	Architecture	Hugging Face Card	Status
Simba-SLID-49 🔍	HuBERT	🤗 https://huggingface.co/UBC-NLP/Simba-SLIS-49	✅ Released

🧩 Usage Example

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

fromtransformersimport (
HubertForSequenceClassification,
AutoFeatureExtractor,
AutoProcessor
)
importtorchmodel_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 modeltry:
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 inferencemodel.eval()
audio_arrays= [] ### add your audio arraysample_rate=16000nputs=processor(audio_arrays, sampling_rate=sample_rate, return_tensors="pt", padding=True).to("cuda")
# Different models might have slightly different input formatstry:
logits=model(**inputs).logitsexceptExceptionase:
# Try alternative input format if the first attempt failsif"input_values"ininputs:
logits=model(input_values=inputs.input_values).logitselse:
raisee# Calculate softmax probabilitiesprobs=torch.nn.functional.softmax(logits, dim=-1)
# Get the maximum probability (confidence) for each predictionconfidence_values, pred_ids=torch.max(probs, dim=-1)
# Convert to Python listspred_ids=pred_ids.tolist()
confidence_values=confidence_values.cpu().tolist()
# Get labels from IDspred_labels= [model.config.id2label[i] foriinpred_ids]
print(pred_labels, confidence_values)

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SibmaBench Data Release & Benchmarking

How to Use SimbaBench

To evaluate your model on SimbaBench across all supported tasks (ASR, TTS, and SLID), simply load the corresponding configuration for the task and language you wish to benchmark.

Each task is organized by configuration name (e.g., asr_test_afr, tts_test_wol, slid_61_test). Loading a configuration provides the standardized evaluation split for that specific benchmark.

Example:

fromdatasetsimportload_datasetdata=load_dataset("UBC-NLP/SimbaBench_dataset", "asr_test_afr")

DatasetDict({
test: Dataset({
features: ['split', 'benchmark_id', 'audio', 'text', 'duration_s', 'lang_iso3', 'lang_name'],
num_rows: 1000
})
})

data['test'][0]

{'split': 'test',
'benchmark_id': 'afr_Lwazi_afr_test_idx3889',
'audio': {'path': None,
'array': array([ 4.27246094e-04, 7.62939453e-04, 6.71386719e-04, ...,
-3.05175781e-04, -2.13623047e-04, -6.10351562e-05]),
'sampling_rate': 16000},
'text': 'watter, verontwaardiging sou daar, in ons binneste gewees het?',
'duration_s': 5.119999885559082,
'lang_iso3': 'afr',
'lang_name': 'Afrikaans'}

📌 ASR Evaluation Configurations

Config Name	Language	ISO	# Samples	# Hours
asr_test_Akuapim-twi	Akuapim-twi	Akuapim-twi	1,000	1.35
asr_test_Asante-twi	Asante-twi	Asante-twi	1,000	0.97
asr_test_afr	Afrikaans	afr	1,000	0.87
asr_test_amh	Amharic	amh	581	1.12
asr_test_bas	Basaa	bas	582	0.76
asr_test_bem	Bemba	bem	1,000	2.15
asr_test_dav	Taita	dav	878	1.17
asr_test_dyu	Dyula	dyu	59	0.10
asr_test_fat	Fanti	fat	1,000	1.38
asr_test_fon	Fon	fon	1,000	0.66
asr_test_fuc	Pulaar	fuc	100	0.10
asr_test_fuf	Pular	fuf	129	0.03
asr_test_gaa	Ga	gaa	1,000	1.52
asr_test_hau	Hausa	hau	681	0.89
asr_test_ibo	Igbo	ibo	5	0.01
asr_test_kab	Kabyle	kab	1,000	1.05
asr_test_kin	Kinyarwanda	kin	1,000	1.50
asr_test_kln	Kalenjin	kln	1,000	1.50
asr_test_loz	Lozi	loz	399	0.91
asr_test_lug	Ganda	lug	1,000	1.65
asr_test_luo	Luo (Kenya and Tanzania)	luo	1,000	1.31
asr_test_mlq	Western Maninkakan	mlq	182	0.04
asr_test_nbl	South Ndebele	nbl	1,000	1.12
asr_test_nso	Northern Sotho	nso	1,000	0.88
asr_test_nya	Nyanja	nya	428	1.31
asr_test_sot	Southern Sotho	sot	1,000	0.82
asr_test_srr	Serer	srr	899	2.84
asr_test_ssw	Swati	ssw	1,000	0.93
asr_test_sus	Susu	sus	210	0.05
asr_test_swa	Swahili	swa	1,000	1.23
asr_test_tig	Tigre	tig	185	0.33
asr_test_tir	Tigrinya	tir	7	0.01
asr_test_toi	Tonga (Zambia)	toi	463	1.47
asr_test_tsn	Tswana	tsn	1,000	0.82
asr_test_tso	Tsonga	tso	1,000	0.99
asr_test_twi	Twi	twi	12	0.02
asr_test_ven	Venda	ven	1,000	0.92
asr_test_wol	Wolof	wol	1,000	1.19
asr_test_xho	Xhosa	xho	1,000	0.92
asr_test_yor	Yoruba	yor	359	0.42
asr_test_zgh	Standard Moroccan Tamazight	zgh	197	0.22
asr_test_zul	Zulu	zul	1,000	1.10

📌 TTS Evaluation Configurations

Config Name	Language	ISO	# Samples	# Hours
tts_test_ewe	Ewe	ewe	66	0.29
tts_test_kin	Kinyarwanda	kin	1,053	1.30
tts_test_Asante-twi	Asante-twi	Asante-twi	64	0.18
tts_test_yor	Yoruba	yor	40	0.13
tts_test_wol	Wolof	wol	4,001	4.12
tts_test_hau	Hausa	hau	124	0.24
tts_test_lin	Lingala	lin	63	0.28
tts_test_xho	Xhosa	xho	242	0.31
tts_test_tsn	Tswana	tsn	238	0.36
tts_test_afr	Afrikaans	afr	293	0.34
tts_test_sot	Southern Sotho	sot	210	0.33
tts_test_Akuapim-twi	Akuapim-twi	Akuapim-twi	83	0.22

📌 SLID Evaluation

Config Name	Language Scope	# Samples	# Hours
slid_61_test	61 Languages	21,817	34.36

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",
}