Whisper Base Mla 24lang

Honest sizing note (read first)

Conversion cost GROWS as the model shrinks — measured across the 24-language family: small ≈+0.6 → base ≈+1.1 → tiny ≈+1.9 (approximate per-language medians). At the base tier you pay ≈+1.1 WER (median) for the 62.5% cache cut, and absolute quality is base-tier (much higher WER than small — that is the base model, not MLA). If quality is the priority prefer whisper-small-mla-24lang; this tier is for memory-constrained deployment where the cache cut matters most.

Results (CommonVoice-17 test — Malay from FLEURS, n≤1500/lang (ko 339, no 370, ms 749, vi 1274; rest 1500); cost = paired vs an identically-trained control; CER for th/zh/ja)

Thin-CommonVoice languages (Korean, Norwegian, Danish, Greek, Finnish, Czech, Vietnamese) are the weakest — a DATA limit, not an MLA one. At this base tier several are near-floor (WER: nn-NO 69.4, ko 60.6, da 56.9, el 55.3) — reported for transparency, marginal-to-unusable; at that error level the paired "conversion cost" is dominated by metric noise, not MLA. So restrict the size-cost curve to the usable-WER languages. Per-language cost also depends on the recovery mix — a language's cost here differs from the same language in whisper-base-mla-cv11 (the 11-language model); expected (mix composition moves the low-rank fit), not irreproducibility. Malay was trained on FLEURS (no CV Malay exists; FLEURS is CC-BY-4.0; its eval-only license respected — audio used for training, never redistributed).

Matched control now published — verify the conversion cost yourself: burakaydinofficial/whisper-base-24lang (trained identically, minus the MHA→MLA conversion). Evaluate both with scripts/validate.py.

Limitations

• 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.
• Runtime: requires trust_remote_code + transformers==4.46.x (no whisper.cpp/CT2/faster-whisper).
• Language coverage: covers these 24 languages; erodes Whisper's others.
• Decoding: greedy-decode evals (beam-5 adds ~1-2 WER on both arms; conversion cost unchanged — measured).
• Domain: consumer-mic read-speech.
• Training: 15k steps, warmup+cosine, encoder frozen both arms, dev-selected, bf16 (weights released as fp16).