Whisper-AfroRad-FR

Whisper Small FR - Radiologie (AfroRad)

This model is a fine-tuned version of openai/whisper-small adapted for medical radiology dictation in the Afro-French context. It was specifically optimized for French-speaking African regions.

Model Description

The model focuses on two main adaptations:

1. Acoustic Adaptation: Capturing the phonetic nuances of French-speaking African regions to improve recognition of local accents.
2. Medical Terminology: Stabilizing technical radiology terms (Spine, Shoulder, Thorax, Mammography, CT scans) in a dictation context.

It uses LoRA (Low-Rank Adaptation) via the adapters library, specifically targeting the first 4 layers of the Encoder (for acoustic/accent adaptation) and the full Decoder (for medical jargon and linguistic structure).

Training and Evaluation Data

• Training Dataset: ~4.5 hours of specialized radiology recordings (562 audios).

<!-- - **Segmentation Strategy**: To handle audios longer than Whisper's 30s limit, we used: - Segment Duration: 25 seconds - Overlap: 5 seconds - Proportional text-to-audio alignment. -->

Training Procedure

Training Hyperparameters

• Learning Rate: 3e-5 (Global)
  • Encoder (L0-L3): 8e-6
  • Decoder: 4e-5
• Optimizer: AdamW 8-bit (bnb.optim.AdamW8bit)
• Batch Size: 12 (train), 8 (eval)
• Max Steps: 1,700

Training Results

Final Performance:

• Best WER: 6.29% (Step 900)
• Final WER: 8.19% (Step 1400)

The model shows strong convergence with excellent generalization to unseen medical terminology and regional accent variations.

Performance on the Test Set

The model was evaluated on the AfroRadVoice-FR test split (75 audio files, independent of training), using identical decoding settings (temperature = 0.0) across all models for a fair comparison.

Framework Versions

• Transformers 4.47.0+
• Adapters 1.0.0+
• PyTorch 2.6.0+
• Datasets 3.6.0
• Python 3.10+

Citation

If you use this model in your research, please cite:

@misc{whisper-afrorad-fr,
  author = {StephaneBah},
  title = {Whisper-AfroRad-FR: Medical Radiology ASR for Afro-French Context},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\\url{https://huggingface.co/StephaneBah/Whisper-AfroRad-FR}}
}