# Can a local model adjudicate two ASR transcripts? (tapir#33, 2026-10-01)

Question: when `iguana/kb-whisper` and `iguana/pianissimo` disagree about a word or phrase,
can a **local** model say which reading is right, well enough to merge the two transcripts?
Ground truth: the speaker's own blind answers on 30 disagreements of one real recording
(see `asr-kb-whisper-vs-pianissimo-2026-10-01.md`; no transcript text is recorded here,
this repo is public). Reference: Claude Opus 5.5 agreed with the speaker on all 30.

## Method

- **Items:** 30 disagreement spans that Opus had picked as changing meaning, the speaker's
  verdict as truth (pianissimo right 17, kb-whisper right 13).
- **Prompt:** one fixed English instruction (two transcriptions of Swedish speech differ at
  ONE marked spot; say which reading the speaker most likely said; reply `ANSWER: 1|2|UNSURE`
  plus one sentence). Each item was asked in **both reading orders** and in **two contexts**:
  narrow (+-8 words) and wide (+-100 words). 120 calls per model, temperature 0.
- **Models:** 11, all local (`koala/*`, `iguana/*`): nothing left the homelab. Run one iguana
  model at a time, each unloaded afterwards, to avoid the memory pressure behind infra#273;
  whisper was confirmed healthy afterwards.
- **Pass criteria, fixed before the run:** wide context, strict accuracy >= 80% (an
  unparseable or "unsure" answer counts as wrong), order-swap consistency >= 85%,
  unparseable <= 10%.

## Results (wide context, 60 calls per model)

| Model | Correct | 95% CI | Same pick when order swapped | Unparseable | s/call | Pass |
|---|---|---|---|---|---|---|
| `iguana/gemma4-31b` | 58 = 97% | 89-99% | 93% | 0 | 10.4 | **yes** |
| `iguana/gemma4-26b` | 55 = 92% | 82-96% | 93% | 0 | 2.0 | **yes** |
| `iguana/ornith-35b` | 51 = 85% | 74-92% | 77% | 0 | 2.5 | no (consistency) |
| `koala/phi4-14b` | 50 = 83% | 72-91% | 80% | 0 | 1.0 | no (consistency) |
| `koala/qwen36-35b-a3b` | 49 = 82% | 70-89% | 83% | 0 | 3.3 | no (consistency) |
| `iguana/granite4` | 47 = 78% | 66-87% | 83% | 0 | 4.6 | no |
| `iguana/qwen3-14b` | 44 = 73% | 61-83% | 87% | 0 | 4.4 | no (accuracy) |
| `iguana/gpt-oss-20b` | 40 = 67% | 54-77% | 100% | 17 | 11.2 | no (unparseable) |
| `koala/phi4-mini` | 37 = 62% | 49-73% | 24% | 1 | 0.5 | no |
| `iguana/gemma4-26b-think` | 23 = 38% | 27-51% | 100% | 37 | 15.3 | no (unparseable) |
| `koala/qwen35-9b-fast` | 0 | n/a | n/a | 60 | 7.2 | no (no output) |

Narrow context only (+-8 words) costs 6-12 points for the models that pass: gemma4-31b 92%,
gemma4-26b 83%, ornith-35b 80%. Context is not decoration: Opus also used evidence from
elsewhere in the recording (an amount stated earlier, a term that recurs).

Position bias: the passing models pick "Reading 1" 47-51% of the time. `phi4-mini` picks it 15%
and flips its answer when the order is swapped 76% of the time: it carries almost no signal.

## Reading it

- **Two models pass:** the gemma4 pair. Both clear the bar and neither shows position bias.
  26b is ~5x faster; their intervals overlap, so 30 items cannot separate 92% from 97%.
- **The thinking models were not tested fairly.** `gpt-oss-20b` and `gemma4-26b-think` spend
  the 600-token budget reasoning and often never reach the answer line. When gemma4-26b-think
  did answer, it was right 23 of 23; gpt-oss-20b 93% of the answered. A larger budget would
  make them eligible; at 15 s a call it would not make them practical.

## What this does not show

- **Thirty items, one recording, one speaker, one domain.** The 30 were chosen by Opus as
  decidable, so they overstate how easy the typical disagreement is; ~350 others are mostly
  spelling, compounds and fillers, which no ground truth covers.
- Opus agreeing 30/30 is not a measure of Opus over all 380 disagreements.
- Accuracy was measured at temperature 0 on one prompt; no prompt tuning was tried.
- The speaker's answers are the only truth; they are one person's recollection of what they said.

## Why gemma4-31b is slow, and what was ruled out

On iguana (M2 Ultra, 64 GB) the dense 31B runs at **103 tok/s prefill, 7.9 tok/s decode**; the
26B MoE at 641 and 39.6. Ruled out as the cause: memory pressure (no swap, 45% free, model 100%
on GPU), context length (32k vs 4k made no difference), and the **inference engine** — MLX 4-bit
of the same model measured 76 tok/s prefill and 8.6 decode, the same within noise. (Terse
output saves only ~3.6 s of ~9.3 s because prefill dominates.) Two unrelated engines at the same
speed points at the machine: ~140 GB/s effective memory bandwidth on a part rated for 800, and
**iguana had macOS Low Power Mode on** (`pmset` `lowpowermode 1`). A first MLX benchmark was
wrong because `mlx_lm.server` caches prompts; the figures above are from the corrected run.

**Confirmed 2026-10-02.** The owner turned Low Power Mode off (`lowpowermode 0`, read back over
ssh) and `gemma4:31b` was re-measured with nothing else resident: **217 tok/s prefill, 22.3
tok/s decode** (4 runs, spread 22.2-22.6), against 103 and 7.9 with it on, so 2.1x and 2.8x. The
slowness was the power mode, not the engine or the model. Caveats on the comparison: the new run
used a different, synthetic Swedish prompt (~700 words, the original items held real transcript
text and were deleted) and the model answered in 26-43 tokens, not the ~200 of the first run, so
it is a same-machine before/after, not a controlled A/B. `gemma4:26b` and the MLX figures were
not re-measured. The 31B's 97% judge accuracy is therefore usable at roughly a third of the
latency the sweep implied; the 26B-vs-31B choice in ADR-034 was made on the slow numbers and is
reopened by this.