---
base_model: badrex/w2v-bert-2.0-kinyarwanda-asr
library_name: peft
license: apache-2.0
language:
- rw
tags:
- automatic-speech-recognition
- kinyarwanda
- lora
- health
pipeline_tag: automatic-speech-recognition
---

# UBUZIMA AI — Kinyarwanda ASR LoRA adapter

A LoRA adapter over [badrex/w2v-bert-2.0-kinyarwanda-asr](https://huggingface.co/badrex/w2v-bert-2.0-kinyarwanda-asr),
fine-tuned for a Kinyarwanda voice health assistant. Part of the UBUZIMA AI capstone
project at the African Leadership University, Kigali.

## Model details

- **Developed by:** Ganza Didier (African Leadership University)
- **Model type:** LoRA adapter for Wav2Vec2-BERT 2.0 CTC speech recognition
- **Language:** Kinyarwanda (rw)
- **License:** Apache 2.0
- **Fine-tuned from:** badrex/w2v-bert-2.0-kinyarwanda-asr

### Adapter configuration

r = 32, alpha = 64, dropout 0.05, target modules `linear_q` and `linear_v`.
3,145,728 trainable parameters (0.54% of the 583.6M base). Feature extractor and
feature projection frozen.

## Training

Fine-tuned in two sequential stages on pooled Afrivoice (health-domain) and Common
Voice (general-domain) Kinyarwanda speech:

- **Stage 1:** 100 hours at a 1:1 Afrivoice:Common Voice ratio.
- **Stage 2:** warm-started from Stage 1, 500 hours at a 1:4 ratio. This adapter is
  the Stage 2 result.

Training regime: fp16 mixed precision on an NVIDIA T4 (Google Colab).

## Evaluation

| Test set | n clips | WER | 95% CI | CER |
|---|---|---|---|---|
| Afrivoice (in-domain, health) | 1,560 | 6.76% | [6.44, 7.11] | 1.39% |
| Common Voice (cross-domain, general) | 16,205 | 31.29% | [30.86, 31.72] | 8.99% |

WER = word error rate, CER = character error rate. 95% confidence intervals from
500-resample bootstrapping.

**Contamination note.** The base badrex checkpoint was pretrained on Kinyarwanda
audio that included portions of the Digital Umuganda health corpus from which the
Afrivoice subset is drawn. The in-domain figure therefore reflects both this
adaptation and residual pretraining familiarity; the Common Voice figure is the
cleaner cross-domain measure of what the adapter added.

## How to use

```python
from transformers import Wav2Vec2BertForCTC, AutoProcessor
from peft import PeftModel

base = "badrex/w2v-bert-2.0-kinyarwanda-asr"
model = Wav2Vec2BertForCTC.from_pretrained(base)
model = PeftModel.from_pretrained(model, "dganza/Ubuzima_AI")
model = model.merge_and_unload()          # fold LoRA in for faster inference
processor = AutoProcessor.from_pretrained("dganza/Ubuzima_AI")
```

## Intended use and limitations

Research prototype for Kinyarwanda health-*information* delivery. **Not a medical
device; does not diagnose.** Dialect coverage is limited to the standard Kinyarwanda
represented in the training corpora. Cross-domain performance (Common Voice, 31% WER)
is substantially weaker than in-domain (Afrivoice, 7% WER), so out-of-domain use
should expect that gap.

## Citation

Ganza, D. (2026). *UBUZIMA AI: A Kinyarwanda voice health assistant* [Capstone
project]. African Leadership University. Supervisor: Emmanuel Adjei.

### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2