dganza
/

Ubuzima_AI

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 ---
 base_model: badrex/w2v-bert-2.0-kinyarwanda-asr
 library_name: peft
 ---
-# Model Card for Model ID
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 ### Framework versions
-- PEFT 0.11.1

 ---
 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-w2v-bert-lora")
+model = model.merge_and_unload()          # fold LoRA in for faster inference
+processor = AutoProcessor.from_pretrained("dganza/ubuzima-w2v-bert-lora")
+```
+## 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