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README.md

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+---
+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)
+---
+
+# 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).
+
+## 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`](./asr-head-to-head.md);
+chart in `asr-head-to-head.png`.
+
+## Method
+
+- **Runner (load-once):** [`scripts/asr_bench.py`](https://github.com/notwitcheer/llm-bench-rig) —
+  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.