| --- |
| 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`](./asr-head-to-head.md). |
|
|
| ## 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. |
|
|
