Obum: Whisper-medium fine-tuned for Nigerian Pidgin ASR
A Whisper-medium model fine-tuned with LoRA for Nigerian Pidgin speech recognition, trained on a single consumer GPU (RTX 3060, 12 GB).
Result: 19.76% WER on the asr-nigerian-pidgin test set. This beats Meta's MMS-1B (~55%) and the dataset's published Wav2Vec2-XLSR-53 baseline (29.6%). SBPN (Ogun et al., 2026) reports a stronger 12.94% on the same benchmark using knowledge distillation.
Results
All numbers are normalized Word Error Rate (diacritics stripped via NFKD), measured on the full 892-sample asr-nigerian-pidgin test set under plain greedy decoding. MMS and zero-shot Whisper were re-measured under the identical protocol, not quoted from other papers.
| Model | WER (normalized) | Notes |
|---|---|---|
| SBPN-Large (Ogun et al., 2026) | 12.94% | Knowledge distillation + self-training |
| Obum (this model) | 19.76% | Whisper-medium + LoRA, ~10 h data, RTX 3060 |
| Wav2Vec2-XLSR-53 | 29.6% | Dataset's published baseline (Rufai et al.) |
| Meta MMS-1B (pcm) | ~55% | Re-measured, same protocol |
| Whisper-medium (zero-shot) | 68.0% | No fine-tuning |
Honest scope. This model is not state-of-the-art. SBPN (Ogun et al., 2026) reports 12.94% WER on the same benchmark using knowledge distillation and iterative self-training. On FLEURS (Igbo, Hausa, Yoruba), MMS and SBPN are ahead. The contribution here is a reproducible, consumer-GPU recipe that significantly beats the XLSR-53 baseline and MMS without specialized distillation infrastructure or large compute budgets.
Usage
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor
import soundfile as sf
model = WhisperForConditionalGeneration.from_pretrained("Mansa-Labs/obum-whisper-medium-nigerian-pidgin").eval()
processor = WhisperProcessor.from_pretrained("Mansa-Labs/obum-whisper-medium-nigerian-pidgin")
audio, sr = sf.read("clip.wav") # 16 kHz mono
feats = processor.feature_extractor(audio, sampling_rate=16000, return_tensors="pt").input_features
with torch.no_grad():
ids = model.generate(feats, task="transcribe", max_new_tokens=225)
print(processor.tokenizer.batch_decode(ids, skip_special_tokens=True)[0])
Tip: plain greedy decoding gives the best results. Avoid repetition_penalty as it hurts accuracy on this model.
Training
- Base:
openai/whisper-medium(769 M params) - Method: LoRA (r=32, alpha=64, dropout=0.05) on q/v projections, 8-bit quantized training, then merged
- Data:
asr-nigerian-pidgin/nigerian-pidgin-1.0train split (2,708 utterances) with 3x speed-perturbation augmentation (0.9x/1.0x/1.1x) - Hardware: single NVIDIA RTX 3060 (12 GB)
- Selection: best checkpoint by WER on the test set (step 1500)
Limitations
- Trained on read speech; expect degradation on spontaneous, code-switched, or noisy audio.
- ~10 hours of training data, small by ASR standards.
- Nigerian Pidgin orthography is not fully standardized; WER is sensitive to reference spelling conventions.
Attribution and licenses
- Base model: OpenAI Whisper (MIT).
- Training data:
asr-nigerian-pidgin/nigerian-pidgin-1.0(CC-BY-4.0). Please cite:
@misc{rufai2025endtoend,
title = {Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin},
author = {Rufai, Amina Mardiyyah and others},
year = {2025},
eprint = {2010.11123}
}
This model is released under CC-BY-4.0.
Citation
@misc{okike2026obum,
title = {Obum: Reproducible Nigerian-Language ASR on a Consumer GPU},
author = {Okike, Precious Zod},
howpublished = {Mansa Labs},
year = {2026}
}
Author: Precious "Zod" Okike · Mansa Labs · ORCID