Whisper-Large-v3-CV11 — 11 languages fine-tune, standard architecture (scientific control)

A plain openai/whisper-large-v3 (unmodified architecture) fine-tuned on the 11 languages of the CC0 Whispered corpus. This is the matched scientific control for burakaydinofficial/whisper-large-v3-mla-cv11 — 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-large-v3-cv11")   # no trust_remote_code
processor = AutoProcessor.from_pretrained("burakaydinofficial/whisper-large-v3-cv11")

Reproduce the conversion cost

Evaluate this control and whisper-large-v3-mla-cv11 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	6.4 WER
de	5.2 WER
es	4.2 WER
fr	9.5 WER
ru	4.3 WER
tr	8.5 WER
cy	35.1 WER
ar	16.0 WER
ka	72.1 WER
th	4.4 CER

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.