Fine-tuned openai/whisper-small for transcribing non-native (accented) English. On a speaker-disjoint held-out test set it reduces word error rate (WER) from 21.30% to 15.53% (a 5.77-point absolute, ~27% relative improvement) over the base model.

Results

Model	Test WER
`openai/whisper-small` (baseline)	21.30%
`whisper-small-accented-en` (this model)	15.53%

WER measured with jiwer against gold transcripts on a speaker-disjoint held-out split (no test speaker appears in training).

Intended use

Transcription of read/prepared English speech from non-native speakers. It is a research/portfolio model and is not deployed in any production system.

How to use

python

from transformers import pipeline

asr = pipeline(
    "automatic-speech-recognition",
    model="katherineahn/whisper-small-accented-en",
)
print(asr("audio.wav")["text"])

Training data

A subset of facebook/voxpopuli, config en_accented: European Parliament recordings from non-native English speakers spanning 15 first-language backgrounds. Filtered to examples with gold transcripts, then split speaker-disjoint (364 speakers, 54 held out) and capped at 3,000 training / 400 test clips. A small number of clips that failed to decode were skipped.

Training procedure

Fine-tuned with the Hugging Face Seq2SeqTrainer on a single Tesla T4 GPU.

Base model: openai/whisper-small (~242M parameters)
Steps: 600 (~14.5 minutes)
Training loss: ~0.96 -> ~0.25

Limitations

whisper-small is a small model; larger Whisper variants will transcribe more accurately at the cost of speed and memory.
Training data is read parliamentary speech, so accuracy may drop on spontaneous, conversational, or noisy audio.
Evaluated on a 400-clip held-out set; treat the reported WER as an estimate.

Citation

Base model: Radford et al., Robust Speech Recognition via Large-Scale Weak Supervision (Whisper). Dataset: Wang et al., VoxPopuli.