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

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

Reproduce the conversion cost

Evaluate this control and whisper-medium-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)

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.