README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - asr
  - speech-to-text
  - benchmark
  - rtx-5090
  - parakeet
  - whisper
  - local-ai
pretty_name: Sovereign ASR Bench (RTX 5090)
configs:
  - config_name: default
    data_files: board.csv

Sovereign ASR Bench — RTX 5090

Local, self-hosted automatic speech recognition benchmarks on one RTX 5090 32GB. Part of the WITCHEER local-AI rig. Methodology that matters: load-once measurement (so RTFx times transcription, not model load), one shared text normalizer applied to every model output and reference, and micro-averaged WER (total errors / total reference words — the LibriSpeech standard). The board lives as data in board.csv (shown in the viewer).

Board — LibriSpeech (test-clean / test-other), 200 utts/split, load-once

model	params	arch	WER clean	WER other	RTFx	VRAM
parakeet-tdt-0.6b-v2	0.6B	TDT transducer	1.49%	4.73%	451×	2.0 GB
whisper-large-v3	1.55B	attention enc-dec	1.47%	5.96%	53×	4.6 GB
whisper-large-v3-turbo	809M	attention enc-dec	1.42%	6.26%	117×	2.5 GB

Clean read speech is a three-way tie (saturated). On the noisy split the 0.6B Parakeet transducer wins on WER and runs 4–9× faster on the least VRAM — the smallest model is the most noise-robust. Full writeup + mechanism in asr-head-to-head.md.

Method

• Runner (load-once): scripts/asr_bench.py — parakeet.cpp bench --manifest (TDT) + whisper.cpp multi-file, both on sm_120 CUDA.
• Scorer (dep-free): scripts/asr_report.py — micro-WER + RTFx + peak VRAM.

Caveats

200 utts/split is directional (ranking solid, exact margin provisional vs the full 2,939-utt test-other). English-only. Number-words-vs-digits are not reconciled — counted as errors equally for every model.