A LoRA adapter that fine-tunes NCAIR1/NigerianAccentedEnglish for Nigerian Pidgin automatic speech recognition. The base model handles Nigerian-accented English; this adapter teaches it the structural particles of Pidgin that the base model mistranscribes as English equivalents.

Results

Evaluated on the 892-example test split of asr-nigerian-pidgin/nigerian-pidgin-1.0:

Model : WER

This adapter is checkpoint-500, selected by a checkpoint sweep — it outperformed both checkpoint-400 (32.19%) and checkpoint-510 (31.72%). The regression at step 510 indicates the final training steps overfit; the best saved checkpoint is step 500.

Usage

python

from transformers import WhisperForConditionalGeneration, WhisperProcessor
from peft import PeftModel

BASE = "NCAIR1/NigerianAccentedEnglish"
processor = WhisperProcessor.from_pretrained(BASE)
base = WhisperForConditionalGeneration.from_pretrained(BASE, use_safetensors=False)
model = PeftModel.from_pretrained(base, "%s").merge_and_unload()

Limitations

Trained and evaluated on a single Pidgin corpus; performance on noisier real-world audio (market noise, phone-mic recordings, heavy code-switching) is untested.
The checkpoint sweep spanned only steps 400-510, so step 500 is the best saved checkpoint, not necessarily the global optimum of the training run.

Base model

Built on NCAIR1/NigerianAccentedEnglish by Awarri Technologies, in partnership with Nigeria's minister of information.

melvinemmanuel001/whisper-naija-pidgin-lora

Whisper Nigerian Pidgin ASR — LoRA Adapter

Results

Usage

Limitations

Base model

melvinemmanuel001/
whisper-naija-pidgin-lora