Study 02 · A25 · October 2, 2026

Four models. The same development recordings.

Large v3 PLD normalized had the lowest word and character error rates in every tested condition. Its median processing time was about four times Whisper Small’s on GB10.

Quality and processing cost

All 2,400 transcription attempts succeeded: four existing models × 200 recordings × three conditions. This is an inference comparison, not four new training runs.

Primary normalized scores and measured median processing time. Lower is better. CUDA figures are peak allocated memory.
ModelConditionWER %CER %Median msCUDA GiB
Whisper SmallClean32.399.153811.03
Whisper Small10 dB noise42.2015.143901.07
Whisper Small200 ms dropout33.839.933801.03
Small FSCClean36.6710.663851.03
Small FSC10 dB noise44.3715.793901.07
Small FSC200 ms dropout38.3911.583841.03
Small PLD FilipinoClean30.6313.673921.07
Small PLD Filipino10 dB noise43.3020.783891.07
Small PLD Filipino200 ms dropout32.2218.053941.07
Large v3 PLD normalizedClean23.967.0515506.40
Large v3 PLD normalized10 dB noise27.938.6215696.40
Large v3 PLD normalized200 ms dropout25.097.6715686.40

The public Small FSC model is an existing SapinSapin checkpoint. It is not our unpublished A17 adapter.

Matched protocol

The same pinned FLEURS Filipino development audio was scored under clean audio, seeded white noise at 10 dB SNR, and a seeded 200 ms dropout. Greedy Tagalog decoding used 30-second chunks and a 448-token cap.

Primary normalization lowercased and collapsed whitespace after NFKC. Punctuation and accents remained in the score. All 12 case scores passed local independent recomputation.

Recommendation within this benchmark

Use Large as the quality reference and Small as the compact baseline. Small PLD improved clean and dropout WER, but had worse CER in every condition and worse noisy WER than unadapted Small.

Reuse the results

Published model-card scores use other protocols. This table ranks only the four models under this shared development benchmark.