213 lines
8.4 KiB
Markdown
213 lines
8.4 KiB
Markdown
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---
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language:
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- am # Amharic
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- ar # Arabic
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- tw # Asante Twi
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- bm # Bambara
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- fr # French
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- lg # Ganda
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- ha # Hausa
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- ig # Igbo
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- rw # Kinyarwanda
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- kg # Kongo
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- ln # Lingala
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- lu # Luba-Katanga
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- mg # Malagasy
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- nso # Northern Sotho
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- ny # Nyanja
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- om # Oromo
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- pt # Portuguese
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- sn # Shona
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- so # Somali
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- st # Southern Sotho
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- sw # Swahili
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- ss # Swati
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- ti # Tigrinya
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- ts # Tsonga
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- tn # Tswana
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- ak # Twi
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- ve # Venda
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- wo # Wolof
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- xh # Xhosa
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- yo # Yoruba
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- zu # Zulu
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- tzm # Tamazight
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- sg # Sango
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- din # Dinka
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- ee # Ewe
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- fo # Fon
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- luo # Luo
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- mos # Mossi
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- umb # Umbundu
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license: cc-by-4.0
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tags:
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- automatic-speech-recognition
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- audio
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- speech
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- african-languages
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- multilingual
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- simba
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- low-resource
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- speech-recognition
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- asr
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datasets:
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- UBC-NLP/SimbaBench
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metrics:
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- wer
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- cer
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library_name: transformers
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pipeline_tag: automatic-speech-recognition
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---
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<div align="center">
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<img src="https://africa.dlnlp.ai/simba/images/VoC_simba" alt="VoC Simba Models Logo">
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[](https://aclanthology.org/2025.emnlp-main.559/)
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[](https://africa.dlnlp.ai/simba/)
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[](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
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[](https://github.com/UBC-NLP/simba)
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[](https://huggingface.co/collections/UBC-NLP/simba-speech-series)
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[](https://huggingface.co/datasets/UBC-NLP/SimbaBench_dataset)
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</div>
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## *Bridging the Digital Divide for African AI*
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**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
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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.
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- **Unified Suite:** Models optimized for African languages.
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- **Superior Accuracy:** Outperforms generic multilingual models by leveraging SimbaBench's high-quality, domain-diverse datasets.
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- **Multitask Capability:** Designed for high performance in ASR (Automatic Speech Recognition) and TTS (Text-to-Speech).
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- **Inclusion-First:** Specifically built to mitigate the "digital divide" by empowering speakers of underrepresented languages.
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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.
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### 🗣️✍️ Simba-ASR
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> **The New Standard for African Speech-to-Text**
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**🎯 Task** `Automatic Speech Recognition` — Powering high-accuracy transcription across the continent.
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**🌍 Language Coverage (43 African languages)**
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> **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`).
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**🏗️ Base Architectures**
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- **Simba-S** (SeamlessM4T-v2-MT) — *Top Performer*
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- **Simba-W** (Whisper-v3-large)
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- **Simba-X** (Wav2Vec2-XLS-R-2b)
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- **Simba-M** (MMS-1b-all)
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- **Simba-H** (AfriHuBERT)
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🌐 Explore the Frontier
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| **ASR Models** | **Architecture** | **#Parameters** | **🤗 Hugging Face Model Card** | **Status** |
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|---------|:------------------:| :------------------:| :------------------:|:------------------:|
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| 🔥**Simba-S**🔥| SeamlessM4T-v2 | 2.3B | 🤗 [https://huggingface.co/UBC-NLP/Simba-S](https://huggingface.co/UBC-NLP/Simba-S) | ✅ Released |
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| 🔥**Simba-W**🔥| Whisper | 1.5B | 🤗 [https://huggingface.co/UBC-NLP/Simba-W](https://huggingface.co/UBC-NLP/Simba-W) | ✅ Released |
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| 🔥**Simba-X**🔥| Wav2Vec2 | 1B | 🤗 [https://huggingface.co/UBC-NLP/Simba-X](https://huggingface.co/UBC-NLP/Simba-X) | ✅ Released |
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| 🔥**Simba-M**🔥| MMS | 1B | 🤗 [https://huggingface.co/UBC-NLP/Simba-M](https://huggingface.co/UBC-NLP/Simba-M) | ✅ Released |
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| 🔥**Simba-H**🔥| HuBERT | 94M | 🤗 [https://huggingface.co/UBC-NLP/Simba-H](https://huggingface.co/UBC-NLP/Simba-H) | ✅ Released |
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* **Simba-S** emerged as the best-performing ASR model overall.
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**🧩 Usage Example**
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You can easily run inference using the Hugging Face `transformers` library.
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```python
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from transformers import pipeline
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# Load Simba-S for ASR
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asr_pipeline = pipeline(
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"automatic-speech-recognition",
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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`
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)
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##### Load the multilingual African adapter (Only for `UBC-NLP/Simba-M`)
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asr_pipeline.model.load_adapter("multilingual_african") # Only for `UBC-NLP/Simba-M`
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###########################
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# Transcribe audio from file
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result = asr_pipeline("https://africa.dlnlp.ai/simba/audio/afr_Lwazi_afr_test_idx3889.wav")
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print(result["text"])
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# Transcribe audio from audio array
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result = asr_pipeline({
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"array": audio_array,
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"sampling_rate": 16_000
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})
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print(result["text"])
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```
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#### Example Outputs
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Using the same audio file with different Simba models:
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```python
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# Simba-S
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{'text': 'watter verontwaardiging sou daar, in ons binneste gewees het.'}
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```
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```python
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# Simba-W
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{'text': 'watter veronwaardigingsel daar, in ons binneste gewees het.'}
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```
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```python
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# Simba-X
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{'text': 'fator fr on ar taamsodr is'}
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```
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```python
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# Simba-M
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{'text': 'watter veronwaardiging sodaar in ons binniste gewees het'}
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```
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```python
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# Simba-H
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{'text': 'watter vironwaardiging so daar in ons binneste geweeshet'}
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```
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Get started with Simba models in minutes using our interactive Colab notebook: [](https://github.com/UBC-NLP/simba/edit/main/simba_models.ipynb)
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## Citation
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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.
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```bibtex
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@inproceedings{elmadany-etal-2025-voice,
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title = "Voice of a Continent: Mapping {A}frica{'}s Speech Technology Frontier",
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author = "Elmadany, AbdelRahim A. and
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Kwon, Sang Yun and
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Toyin, Hawau Olamide and
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Alcoba Inciarte, Alcides and
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Aldarmaki, Hanan and
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Abdul-Mageed, Muhammad",
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editor = "Christodoulopoulos, Christos and
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Chakraborty, Tanmoy and
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Rose, Carolyn and
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Peng, Violet",
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booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
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month = nov,
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year = "2025",
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address = "Suzhou, China",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2025.emnlp-main.559/",
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doi = "10.18653/v1/2025.emnlp-main.559",
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pages = "11039--11061",
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ISBN = "979-8-89176-332-6",
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}
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```
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