Add comprehensive model card (WAXALNet benchmark)

README.md

@@ -2,37 +2,66 @@
 library_name: transformers
 license: apache-2.0
 base_model: openai/whisper-small
+language:
+- kpo
 tags:
-- generated_from_trainer
+- automatic-speech-recognition
+- african-languages
+- waxal
+- waxalnet
+- kpo
+datasets:
+- waxal-benchmarking/waxal
 metrics:
 - wer
-model-index:
-- name: whisper-small-kpo-gbotemi
-  results: []
+- cer
 ---
+# Whisper Small fine-tuned on WAXAL — Ikposo
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+This model is part of **[WAXALNet](https://huggingface.co/waxal-benchmarking)**, a suite of ASR models fine-tuned on the [WAXAL corpus](https://huggingface.co/waxal-benchmarking) across 19 African languages, developed as part of the WAXAL ASR Benchmark study.
 
-# whisper-small-kpo-gbotemi
+## Model Details
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.5751
-- Wer: 0.7239
-- Cer: 0.2642
+| | |
+|---|---|
+| **Language** | Ikposo (`kpo`) |
+| **Language Family** | Niger-Congo (Kwa) |
+| **Architecture** | Whisper Small (244M parameters) |
+| **Base Model** | [openai/whisper-small](https://huggingface.co/openai/whisper-small) |
+| **Training Data** | WAXAL corpus (conversational spontaneous speech) |
+| **Test WER** | 77.5% |
+| **Test CER** | 26.5% |
+| **License** | apache-2.0 |
 
-## Model description
+## Intended Use
 
-More information needed
+This model is intended for automatic speech recognition of **Ikposo** conversational speech. It was evaluated on the WAXAL test set (spontaneous, image-prompted speech) and partially on FLEURS (read speech). It is suitable for research and low-resource ASR applications. It is not recommended for high-stakes production use without further validation.
 
-## Intended uses & limitations
+## Training Data
 
-More information needed
+Fine-tuned on the [WAXAL corpus](https://huggingface.co/waxal-benchmarking), a large-scale dataset of transcribed, image-prompted spontaneous speech across 19 African languages recorded in participants' natural environments. The Ikposo training split contains conversational speech across diverse speakers. Data is released under CC-BY 4.0.
 
-## Training and evaluation data
+## Usage
 
-More information needed
+```python
+from transformers import pipeline
+
+asr = pipeline("automatic-speech-recognition",
+               model="waxal-benchmarking/whisper-small-waxal-kpo")
+result = asr("audio.wav")
+print(result["text"])
+```
+
+## Test Set Performance (WAXAL Benchmark)
+
+Evaluated on the filtered WAXAL test set (duration >= 1.5s, speech rate >= 4 WPS).
+
+| Metric | Score |
+|---|---|
+| **WER** | 77.5% |
+| **CER** | 26.5% |
+
+Full benchmark results across all 19 languages and 6 models are reported in the WAXAL ASR Benchmark paper (citation below).
 
 ## Training procedure
 
@@ -68,3 +97,38 @@ The following hyperparameters were used during training:
 - Pytorch 2.10.0+cu128
 - Datasets 4.0.0
 - Tokenizers 0.22.2
+
+
+## Citation
+
+```bibtex
+@article{waxalnet2026,
+  title  = {The WAXAL ASR Benchmark: Fine-Tuned Edge Models Across 19 African Languages},
+  author = {Olufemi, Victor Tolulope and Babatunde, Oreoluwa and Njema, Ramsey and
+             Gbotemi, Bolarinwa and Yen, Wanchi Lucia and Uzodinma, John and
+             Ajayi, Sunday and Williams, Oluwademilade and Moshood, Kausar and
+             Anyaele, Innocent Elendu and Arefaine, Akebert Tesfahunegn and
+             Hunzwi, Candace and Daniel, Wongel Dawit and Namuganga, Emmilly Immaculate and
+             Kadima, Cleophas and Bahizire, Athanase Biluge and Ranaivoson, Onitsiky and
+             Aaron, Emmanuel and Ladislaus, Nicholaus Dismas and Muhammed, Idris and
+             Simenya, Jonathan Enoch and Koome, Martin and Endaylalu, Matewos Tegete and
+             Adeyemo, Peter Ifeoluwa and Birindwa, Hondi Prisca and Eze-Mbey, Ukachi Agnes and
+             Oduro-Yeboah, Yacoba and Aremu, Toluwani and Adjovi, Pericles and
+             Ngueajio, Mikel K and Mitra, Prasenjit},
+  year   = {2026},
+  note   = {Preprint coming soon}
+}
+```
+
+## Authors
+
+Victor Tolulope Olufemi · Oreoluwa Babatunde · Ramsey Njema · Bolarinwa Gbotemi · Wanchi Lucia Yen · John Uzodinma · Sunday Ajayi · Oluwademilade Williams · Kausar Moshood · Innocent Elendu Anyaele · Akebert Tesfahunegn Arefaine · Candace Hunzwi · Wongel Dawit Daniel · Emmilly Immaculate Namuganga · Cleophas Kadima · Athanase Biluge Bahizire · Onitsiky Ranaivoson · Emmanuel Aaron · Nicholaus Dismas Ladislaus · Idris Muhammed · Jonathan Enoch Simenya · Martin Koome · Matewos Tegete Endaylalu · Peter Ifeoluwa Adeyemo · Hondi Prisca Birindwa · Ukachi Agnes Eze-Mbey · Yacoba Oduro-Yeboah · Toluwani Aremu · Pericles Adjovi · Mikel K Ngueajio · Prasenjit Mitra
+
+## Acknowledgements
+
+We thank the following contributors for their language expertise and native-speaker evaluation support:
+Ajara Oyinloye, Abubakari Sadic Mohammed, Hafiz Adjei, Aliga Norah Lele, Marie-Louise B. Ndamuso, and Odong Diana.
+
+This work was supported by **[Lynguallabs](https://lynguallabs.org/)** (compute, researchers & storage),
+**[Open Token](https://opentoken.global/)** (compute resources), and
+**[CMU Africa](https://www.africa.engineering.cmu.edu/)** (researchers & native speakers).