burakaydinofficial/whisper-large-v3-mla-cv11

Whisper-Large-v3-MLA (11 languages) — compression cost ≈ statistical zero

openai/whisper-large-v3 with decoder self-attention converted MHA→MLA (Whisper-MLA, arXiv:2603.00563) and recovery-fine-tuned on the CC0 Whispered corpus — the same model that annotated that corpus, now with a 62.5% smaller decode KV-cache. Conversion cost vs an identically-trained unconverted control: not statistically significant on 9 of 11 languages (significant only on ar +0.56 WER and zh +0.52 CER; median ≈ +0.1). n=1500/lang, paired per-speaker bootstrap.

from transformers import AutoModelForSpeechSeq2Seq
model = AutoModelForSpeechSeq2Seq.from_pretrained("burakaydinofficial/whisper-large-v3-mla-cv11", trust_remote_code=True)  # transformers==4.46.x

Results (CommonVoice-17 test, greedy, Whisper normalization + Arabic folding)

Lang	WER/CER %	conversion cost
en	6.6 / 3.2	+0.20 ns
de	5.3 / 1.9	+0.09 ns
es	4.2 / 1.4	+0.02 ns
fr	9.6 / 3.6	+0.08 ns
ru	4.2 / 1.1	−0.03 ns
tr	8.4 / 1.9	−0.08 ns
cy	35.8 / 12.0	+0.67 ns
ar	16.6 / 6.0	+0.56 ✱
th	14.0 / 4.4	−0.02 CER ns
zh	18.3 / 9.7	+0.52 CER ✱
ka	72.3 / 34.3	+0.22 ns — floor

Size-scaling (measured across the released family): conversion cost falls with model size — tiny ≈+1.9 → base ≈+1.0 → small ≈+0.4 → medium ≈+0.4 → large ≈ 0 (approximate per-size medians; the exact per-language costs are in each model's table). We preregistered predictions twice and revised twice (decay → "saturation" → decay-to-zero); the monotone decline is the honest result.

Matched control now published — verify the conversion cost yourself: burakaydinofficial/whisper-large-v3-cv11 (trained identically, minus the MHA→MLA conversion). Evaluate both with scripts/validate.py.

Limitations

• Costs are recovery-mix-specific: do NOT compare a language's conversion cost across the cv11 and 24-language lines — different recovery sets move each language's low-rank fit (French in the small model: +0.32 cv11 vs +1.22 24-lang). Not irreproducibility.
• What the 62.5% is (cache scope): it is the decode self-attention KV-cache — the part that grows with output length and concurrency. The (larger, encoder-length ~1500-frame) cross-attention/encoder memory is NOT compressed, so single-stream total decode-memory savings are modest; the 62.5% cut compounds at output-length × batch concurrency, which is where it pays off.
• Requires trust_remote_code=True and transformers==4.46.x (custom MLA attention modules); not loadable in whisper.cpp / faster-whisper / CTranslate2.
• Language coverage is the 11 recovery languages — unseen scripts degrade (measured); ka reported but weak at every size.
• Consumer-mic read-speech domain (SNR-ladder at small: cost flat to 0 dB); greedy-decode evals — beam-5 adds ~1-2 WER on both arms without changing the conversion cost (measured on the small tier).
• Training: 15k steps, warmup+cosine, encoder frozen both arms, dev-selected, bf16; released weights fp16 (~3.1 GB).

FLEURS (out-of-domain) — this model, WER% (CER% for th/zh)

ABSOLUTE FLEURS numbers on a different read-speech corpus. Do NOT compare these per-language to the CommonVoice table above — the corpora differ in transcript normalization/orthography and speaker pools (e.g. Welsh reads easier on FLEURS, Georgian worse), so cross-corpus swings are corpus artifacts, not model behavior. The clean out-of-domain claim is the PAIRED MLA-vs-control cost (verified to be small on the small tier). Per-language absolute: en 8.0 · de 7.2 · es 5.8 · fr 8.5 · ru 9.5 · tr 10.8 · cy 33.5 · ar 13.2 · th 17.9 · zh-CN 10.0 · ka 87.7. (Full JSON: the release repo's eval artifacts.)