
Base model	`openai/whisper-small.en` (244M params)
Method	LoRA (r=16, alpha=32) — 5.6M trainable / 247M total (2.3%)
Data	1,800 train / 200 val clips from FLEURS en_us
Epochs	3
Batch size	2 x 8 gradient accumulation = 16 effective
Learning rate	1e-4 with warmup (50 steps)
Device	Apple M4 (MPS), 29 min total
Peak RAM	~5.2 GB

Results

Epoch	WER	Eval Loss
1	7.2%	0.214
2	5.9%	0.192
3	5.76%	0.192

A/B test on 15 TTS-synthesized English phrases:

Baseline small.en: 7/15 (47%) exact match
Fine-tuned: 12/15 (80%) exact match
Latency: identical (~1.4s/utterance on CPU int8)

Usage

python

from transformers import WhisperForConditionalGeneration, WhisperProcessor

model = WhisperForConditionalGeneration.from_pretrained("ronxldwilson/whisper-small-en-finetuned")
processor = WhisperProcessor.from_pretrained("ronxldwilson/whisper-small-en-finetuned")

For the CTranslate2 int8 version (production, faster-whisper): see ronxldwilson/whisper-small-en-finetuned-ct2-int8