---
library_name: transformers
license: cc-by-4.0
language:
- ki
metrics:
- wer
- cer
base_model:
- facebook/w2v-bert-2.0
pipeline_tag: automatic-speech-recognition
---


<div align="center" style="line-height: 1;">
  <h1>Automatic Speech Recognition for Kikuyu</h1>
  <a href="https://creativecommons.org/licenses/by/4.0/deed.en" style="margin: 2px;">
    <img alt="License" src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

### Model Description 🍍

This model is a fine-tuned version of Wav2Vec2-BERT 2.0 for Kikuyu automatic speech recognition (ASR). It was trained on the 100+ hours of transcribed speech, covering Health, Government, Finance, Education, and Agriculture domains. The in-domain WER for this ASR model is below 25.0%. 

- **Developed by:** Badr al-Absi
- **Model type:** Speech Recognition (ASR)
- **Language:** Kikuyu (kik)
- **License:** CC-BY-4.0
- **Finetuned from:** facebook/w2v-bert-2.0



### Model Architecture

- **Base model:** Wav2Vec2-BERT 2.0
- **Architecture:** transformer-based with convolutional feature extractor
- **Parameters:** ~600M (inherited from base model)
- **Objective:** connectionist temporal classification (CTC)

### Funding
The development of this model was supported by [CLEAR Global](https://clearglobal.org/) and [Gates Foundation](https://www.gatesfoundation.org/). 


### Citation

```bibtex
@misc{w2v_bert_kikuyu_asr,
  author = {Badr M. Abdullah},
  title = {Adapting Wav2Vec2-BERT 2.0 for Kikuyu ASR},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/badrex/w2v-bert-2.0-swahili-asr}
}

```

### Model Card Contact

For questions or issues, please contact via the Hugging Face model repository in the community discussion section.