---
title: Fonbench Leaderboard
emoji: 🌍
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 6.19.0
python_version: '3.13'
app_file: app.py
pinned: false
license: mit
short_description: ASR benchmark for Fon (low-resource African tonal language)
---

# 🎤 FonBench — ASR Benchmark and Adaptation for Fon

Interactive leaderboard comparing six pre-trained multilingual ASR models on the **Fon language** (low-resource tonal language of Benin, ~8M speakers), and four adaptation strategies (LoRA and full fine-tuning) on the best model.

## 📊 Phase 1 — Zero-shot evaluation
Six state-of-the-art multilingual ASR models evaluated without adaptation: **MMS-1b**, **XLSR-53**, **OmniASR-CTC**, **AfriHuBERT**, **SeamlessM4T**, **Whisper-small**.

## 🏆 Phase 2 — Adaptation
Two adaptation strategies (**LoRA** at ranks 8 and 32, **Full Fine-Tuning** at LR 1e-5 and 5e-5) applied to the best zero-shot model.

**Best result: WER 19.27% (Full Fine-Tuning, LR 5e-5) — a 64-point improvement over zero-shot.**

## 🔗 Resources

- 📦 Models on HuggingFace Hub: *(coming soon)*
- 💻 Source code on GitHub: *(coming soon)*
- 📈 Experiment tracking on W&B: [FonBench project](https://wandb.ai/inesassiahounkponou-esgis/FonBench)
- 📄 Dataset: [alaleye/fon](https://huggingface.co/datasets/alaleye/fon) (CC-BY-4.0)

## 👤 Author
**Inès Assia HOUNKPONOU** — Master IA, ESGIS Bénin, 2026
Supervisor: **Prof. Fréjus LALEYE**

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference