voice_audio_multi/tests/parakeet_tdt/multilingual/results

Recorded results

fleurs_weight_types.json

Full output of one run of compare_weight_types.py native f16 bf16 q8_0 — every clip's reference transcript plus what each weight type actually transcribed, so the aggregate numbers below can be re-derived or re-scored with a different WER normalization without re-running anything (and without re-downloading the corpus).

• Corpus: FLEURS test split, 5 clips per language
• 120 clips, 24 languages, 21.8 minutes of audio
• Run date: 2026-07-27

Hardware

Intel i7-9750H (6C/12T, Coffee Lake). An ordinary 6-core desktop-class CPU — nothing exotic and nothing especially old, so the CPU figures should generalize reasonably to similar AVX2 machines.

The GPU in this machine is not representative and CUDA numbers elsewhere in these docs should be read with that in mind: a GTX 1650 with Max-Q Design — Turing, compute capability 7.5, 4 GB, and a 35 W power limit. That is a thermally- and power-constrained mobile part, and its compute capability is below the 8.0 (Ampere) threshold at which ggml enables CUDA graph capture at all. Null results on the CUDA side (flash attention, fused QKV) are specifically this card's null results.

Aggregate

weight type	encode speed	transcript identical to f32	WER vs f32	absolute WER
native (f32)	1.00x	100%	—	0.1338
f16	~1.00x	99.2%	0.0003	0.1336
bf16	1.14x	99.2%	0.0004	0.1334
q8_0	1.79x	91.7%	0.0062	0.1331

Speed figures are encoder graph compute on CPU at 6 threads, measured separately with the drift-cancelling A/B harness — not from this run, which does not control for thermal drift and is not a timing benchmark.

Per-language WER vs the FLEURS human reference

lang	native	f16	bf16	q8_0
bg	0.137	0.137	0.137	0.137
cs	0.122	0.122	0.122	0.122
da	0.257	0.257	0.257	0.263
de	0.029	0.029	0.029	0.029
el	0.419	0.413	0.419	0.413
en	0.050	0.050	0.050	0.050
es	0.045	0.045	0.045	0.045
et	0.164	0.164	0.164	0.184
fi	0.104	0.104	0.104	0.104
fr	0.042	0.042	0.042	0.042
hr	0.128	0.128	0.128	0.128
hu	0.140	0.140	0.140	0.117
it	0.000	0.000	0.000	0.000
lt	0.152	0.152	0.152	0.152
lv	0.326	0.326	0.326	0.326
nl	0.037	0.037	0.037	0.037
pl	0.147	0.147	0.137	0.147
pt	0.077	0.077	0.077	0.077
ro	0.142	0.142	0.142	0.142
ru	0.000	0.000	0.000	0.000
sk	0.078	0.078	0.078	0.078
sl	0.299	0.299	0.299	0.284
sv	0.296	0.296	0.296	0.296
uk	0.021	0.021	0.021	0.021

Five clips per language, so a single clip moves a language's number a lot; these are a check that no language falls off a cliff under quantization, not publishable per-language WER. The absolute values also reflect this harness's deliberately crude normalization (NFKC, lowercase, strip punctuation) — no number/ITN handling, which is why languages whose references spell out numerals score worse. They are comparable across columns, not against other papers.

Where q8_0 diverges

q8_0 differs from native on 10 of 120 clips. Sampling those diffs, most are formatting rather than content:

[de] native  ... wie der T Rex war ihm nicht gewachsen
[de] q8_0    ... wie der T-Rex war ihm nicht gewachsen

[cs] native  ... táhne 80 km/50 mil do vnitrozemí
[cs] q8_0    ... táhne 80 km 50 mil do vnitrozemí

and where real words change, it goes both directions — this one is q8_0 correcting f32, not degrading it:

[hu] native  ... ez a helys turista számára ...
[hu] q8_0    ... ez a hely sok turista számára ...     <- correct reading

which is why the aggregate absolute WER does not move despite an 8.3% transcript churn rate.