Whisper-Small-24Lang — 24 languages fine-tune, standard architecture (scientific control)

A plain openai/whisper-small (unmodified architecture) fine-tuned on the 24 languages of the CC0 Whispered corpus. This is the matched scientific control for burakaydinofficial/whisper-small-mla-24lang — trained identically, minus the MHA→MLA conversion — published so the MLA conversion cost is independently reproducible. No custom code: loads directly in transformers, and — being a plain unmodified Whisper — is convertible for faster-whisper / CTranslate2 / whisper.cpp via their standard converters.

from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
model = AutoModelForSpeechSeq2Seq.from_pretrained("burakaydinofficial/whisper-small-24lang")   # no trust_remote_code
processor = AutoProcessor.from_pretrained("burakaydinofficial/whisper-small-24lang")

Reproduce the conversion cost

Evaluate this control and whisper-small-mla-24lang on CommonVoice-17 (scripts/validate.py in the code repo); the per-language difference is the conversion cost reported on the MLA card and in docs/results/.

Results (CommonVoice-17 test, greedy, Whisper normalization + Arabic folding; CER for th/zh/ja)

Lang	this control
en	12.2 WER
de	14.9 WER
es	10.0 WER
fr	19.1 WER
it	15.4 WER
pt	14.8 WER
ru	14.0 WER
nl	13.2 WER
pl	17.3 WER
id	19.5 WER
tr	21.1 WER
hi	23.2 WER
ms	20.1 WER
sv-SE	21.9 WER
th	12.1 CER
zh-CN	15.6 CER
cs	27.3 WER
vi	28.4 WER
fi	28.5 WER
el	30.5 WER
da	32.3 WER
ja	23.3 CER
nn-NO	43.1 WER
ko	44.2 WER

Encoder frozen during fine-tuning; 15,000 steps, warmup+cosine, fp16. Read-speech domain (CommonVoice + FLEURS-validated). "Compression cost" does not apply to this unconverted control.