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README.md

@@ -11,6 +11,9 @@ tags:
 - whisper
 - local-ai
 pretty_name: Sovereign ASR Bench (RTX 5090)
+configs:
+- config_name: default
+  data_files: board.csv
 ---
 
 # Sovereign ASR Bench — RTX 5090
@@ -19,7 +22,7 @@ Local, self-hosted **automatic speech recognition** benchmarks on one RTX 5090 3
 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).
+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
 
@@ -29,10 +32,11 @@ words — the LibriSpeech standard).
 | 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 |
 
+![WER-other vs RTFx vs VRAM](./assets/asr-head-to-head.png)
+
 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`.
+the most noise-robust. Full writeup + mechanism in [`asr-head-to-head.md`](./asr-head-to-head.md).
 
 ## Method