README.md

metadata

language: en
license: cc-by-nc-sa-4.0
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
  - automatic-speech-recognition
  - whisper
  - african-accented-english
  - clinical-asr
  - afrispeech
base_model: openai/whisper-medium
datasets:
  - intronhealth/afrispeech-200
metrics:
  - wer
model-index:
  - name: whisper-medium-afrispeech
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: AfriSpeech-200 (16 kHz)
          type: Professor/afrispeech-200-16khz
        metrics:
          - type: wer
            value: 20.13
            name: Test WER

whisper-medium-afrispeech

Fine-tune of openai/whisper-medium on AfriSpeech-200 for African-accented English speech recognition, spanning both general and clinical/medical domains.

• Base model: openai/whisper-medium (769M)
• Training data: Professor/afrispeech-200-16khz — a clean 16 kHz mono build of AfriSpeech-200 (Intron Health): ~200 h, 120 accents, 13 countries, clinical + general.
• Language: English (African accents)

Results (test split, 6,178 clips)

Fine-tuned vs. zero-shot baseline

Zero-shot = openai/whisper-medium evaluated on the same test set with no fine-tuning.

Metric	Zero-shot (baseline)	Fine-tuned (this model)	Improvement
Overall WER	43.22%	20.13%	−23.1 pts (−53%)
Clinical WER	50.55%	27.47%	−23.1 pts (−46%)
General WER	36.09%	12.98%	−23.1 pts (−64%)

Fine-tuning more than halves WER across the board.

WER by domain

Domain	WER	n
General	12.98%	2,670
Clinical	27.47%	3,508
Overall	20.13%	6,178

Clinical speech remains ~2× harder than general — medical terminology (drug names, conditions, dosages) drives most of the error, so domain matters when reporting WER.

WER by accent (accents with n ≥ 25)

Performance varies widely across accents (~7% to ~45%):

Best accents	WER		Hardest accents	WER
okirika	7.4%		khana	44.6%
afrikaans	8.0%		nyandang	44.1%
brass	8.6%
ikwere	8.9%
twi	10.3%

The hardest accents are mostly smaller, under-represented ones — an important coverage/equity consideration. Full per-accent numbers: eval_wer_breakdown.csv.

Usage

import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model = WhisperForConditionalGeneration.from_pretrained("Professor/whisper-medium-afrispeech")
processor = WhisperProcessor.from_pretrained("Professor/whisper-medium-afrispeech")

# audio: a 16 kHz mono waveform (numpy array)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
ids = model.generate(inputs.input_features, language="english", task="transcribe")
print(processor.batch_decode(ids, skip_special_tokens=True)[0])

Training

• Objective: full fine-tune (all parameters), 3 epochs
• Precision / hardware: bf16 + gradient checkpointing on a single NVIDIA A40 (48 GB)
• Batch: 32 effective (16 × 2 grad-accum) · LR: 1e-5 (500 warmup, linear decay) · best model by eval WER
• Data handling: on-the-fly log-Mel feature extraction; clips > 30 s filtered out; labels truncated to Whisper's 448-token decoder cap.

Limitations & biases

• Clinical error rate is still elevated (~27%) — verify medical terms; not for unsupervised clinical use.
• Large accent disparity (~7–45% WER) — weaker on under-represented accents.
• ≤ 30 s audio; English only; inherits Whisper's general biases.

Dataset, license & attribution

Trained on AfriSpeech-200 by Intron Health, released under CC-BY-NC-SA-4.0 (non-commercial, share-alike, attribution). This derivative carries the same license.

@article{olatunji2023afrispeech,
  title={AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR},
  author={Olatunji, Tobi and others},
  journal={Transactions of the Association for Computational Linguistics},
  year={2023}
}