tapir/docs/research/asr-kb-whisper-vs-pianissimo-2026-10-01.md

# kb-whisper vs pianissimo on one real hour of Swedish speech (tapir#33, 2026-10-01)

One recording, one speaker: a 63.7-minute Teams session of the maintainer dictating project
notes (domain-heavy, English loanwords and product names mixed into Swedish). **No transcript
text and no domain terms are recorded here**: this repo is public and the content is private.
Only aggregate numbers.

Backends, both through the LiteLLM gateway and Tapir's own adapter, unchanged:
`iguana/kb-whisper` (whisper.cpp, KB's Swedish fine-tune) and `iguana/pianissimo` (KlangAI
Swedish Parakeet/FastConformer-TDT, MLX 8-bit, served by a small FastAPI shell on iguana:8102).

## Result: a near tie on accuracy, a large difference in speed

| | kb-whisper | pianissimo |
|---|---|---|
| Wall time for 63.7 min | 4m08s (~15x realtime) | **40 s (~95x realtime)** |
| Segments | 1,302 (timed pieces, wrapped at ~60 chars) | 434 (sentences) |
| Words (normalised) | 6,606 | 6,510 |
| Punctuation | light, every segment ends in a full stop | heavier (138 vs 82 commas) |
| Repetition loops | none | none |

Word-level agreement between them: **92.4%** as first computed, **93.1%** after the segment
rejoin fix below. 380 aligned disagreement spans, median one word each.

**Blind A/B by the speaker** (the only ground truth available), on the 30 spans a model
(Claude Opus 5.5) had picked as changing meaning, shuffled and unlabelled: **pianissimo 17,
kb-whisper 13**, no "neither", no "can't tell".

| Category of the 30 | pianissimo | kb-whisper |
|---|---|---|
| Technical term / English loanword | 8 | 4 |
| Number / date / unit | 4 | 1 |
| Name / proper noun | 3 | 3 |
| Function word / small word | 1 | 2 |
| Other (everyday words) | 1 | 3 |

The reading: pianissimo is better on terminology and numbers, kb-whisper on ordinary Swedish
words; names are even. Their errors differ in kind: kb-whisper's tend to be garbled words, a
couple were wrong numbers; pianissimo's tend to be fluent substitutions that read as plausible
(a wrong institution, an invented person, a dropped sign), which a reader or an LLM is less
likely to catch.

Opus's own lean agreed with the speaker's blind answer on **30 of 30**. That is not a measure
of Opus over all 380 spans: it chose spans it could decide, and every one of the 30 carried a
lean. Its full classification of the 380 (counts only): function/small word 81, filler or
false start 65, technical term 58, compound split/join 46, other 39, name 28, casing or
punctuation residue 26, inflection 23, number/date/unit 10, content omission or insertion
(>= 3 words) 4.

## A bug this comparison found in Tapir (fixed)

kb-whisper's output is wrapped at ~60 characters and **a wrap can fall inside a word**; the
continuation segment has no leading space, the response's `text` field joins segments with
newlines, and a segment can be a lone `.`. Tapir stored that joined text, so chat received
broken words. Measured on real gateway output: 7% of segments (96/1,332) in a synthetic hour,
42/1,302 lines in the real one. berget's segments are whole sentences with no leading space, so
the two need different joins. `transcribe.joinSegments` now rebuilds the text from the segments.
Checked against the real transcript with pianissimo's vocabulary as an independent yardstick:
36 spans changed, 31 now entirely known words, words unknown to pianissimo 224 -> 177.
Five rejoined spans still contain an unknown word (a name or an unlucky glue): small residual
risk. Earlier tests used short sentences and could not see this.

## What this does not show

- **One recording, one speaker, one domain.** No claim about other voices, accents, crosstalk,
  or room noise. 17 vs 13 is not a significant difference.
- **The other ~350 spans** were not judged by the speaker. They are mostly spelling, compounds,
  fillers and small words; Opus's counts there are a model's judgement, not ground truth.
- Errors **both** models share in text they agree on are invisible to this method.
- Speed was measured once each, on an otherwise idle iguana; pianissimo's 40 s includes the
  gateway hop and a 122 MB WAV upload.
- Whether pianissimo survives the infra#273 Metal-OOM contention the way whisper does not was
  not tested; both run on iguana.

## What it suggests for Tapir (not decided, filed as an issue)

Pianissimo is a **second local backend on the same host, through the same adapter**. That
makes it a natural middle tier ahead of the cloud fallback (kb-whisper -> pianissimo ->
berget), so a wedged whisper no longer sends private audio off the homelab. At 40 s an hour it
is also cheap enough to run **both** on every upload and give chat both texts, or flag where
they disagree; the maintainer's blind answers show they err in different places.