Sovereign ASR on one RTX 5090: a 0.6B transducer beats 5-year-old Whisper-large on noisy speech — and runs 4–9× faster
Rig: one RTX 5090 32GB · parakeet.cpp (ggml, sm_120 CUDA) vs whisper.cpp · LibriSpeech test-clean / test-other, 200 utts/split (400 total) · load-once · one shared text normalizer · temp 0
Setup: Each model loads its weights once and transcribes the whole 400-utterance subset, so RTFx
(real-time factor = audio-seconds processed per processing-second) measures transcription, not model
load — a per-file CLI loop reloads the model every call and silently measures load time, the single most
common way to publish a bogus ASR speed number. Every hypothesis and reference passes through one
normalizer (lowercase, strip punctuation, fold contractions) before micro-averaged WER (total errors /
total reference words — the LibriSpeech standard, not the mean of per-utterance WERs). Parakeet-TDT-0.6b-v2
(f16) runs via parakeet.cpp's native bench --manifest (TDT decoder, load reported separately); Whisper
large-v3 / large-v3-turbo run via whisper.cpp's multi-file mode (one load over the whole invocation). The
always-on local LLM was drained for the run; VRAM is each model's own steady-state footprint.
The board
| model | params | WER clean | WER other | RTFx | VRAM |
|---|---|---|---|---|---|
| parakeet-tdt-0.6b-v2 | 0.6B | 1.49% | 4.73% | 451× | 2.0 GB |
| whisper-large-v3 | 1.55B | 1.47% | 5.96% | 53× | 4.6 GB |
| whisper-large-v3-turbo | 809M | 1.42% | 6.26% | 117× | 2.5 GB |
The finding
On clean read speech, it's a three-way tie — all three within 0.07pts (1.42–1.49%). Clean LibriSpeech is saturated; nobody wins, and chasing that number is chasing noise.
On the harder test-other split, the 0.6B Parakeet wins decisively: 4.73% vs 5.96% (v3) vs 6.26% (turbo). The smallest model is the most noise-robust. And efficiency isn't close — Parakeet is 3.9× faster than turbo, 8.5× faster than v3, on the least VRAM of the three.
The pointed version: whisper-large-v3-turbo is Whisper's own speed variant, and Parakeet beats it on every axis — faster and more accurate on noise. The only place anything edges Parakeet is full v3 on clean speech, by 0.02pts, at 8.5× the compute and 2.2× the VRAM.
Mechanism
Whisper (2022) is an attention encoder-decoder; Parakeet is a TDT transducer (token-and-duration), frame-synchronous decoding built for streaming throughput — that architecture is where the 451× RTFx comes from. The interesting part is the accuracy: at 0.6B, Parakeet is not under-capacity for English read speech — it's right-sized, so it gives up nothing on noise while running an order of magnitude faster. Turbo, by contrast, buys its speed by cutting Whisper's decoder stack (32 → 4 layers) and pays for it on the hard split (6.26%, the worst here). Same "small + specialized beats big + general" result the rig keeps hitting elsewhere.
Honest caveats
- 200 utts/split is a directional subset, not the full test-other (2,939 utts). The 1.2pt noise gap is real on this sample, but a full run would tighten the interval — treat the ranking as solid and the exact margin as provisional.
- English-only. Parakeet-TDT is monolingual; Whisper-large is multilingual + can translate. This board is an English-transcription comparison, nothing more.
- One shared normalizer, with number-words-vs-digits not reconciled — counted as errors equally for every model. A stated limitation, applied identically, so it doesn't bias the comparison.
- Parakeet at f16 (the fair quality point against Whisper's ggml weights); quant sweep is future work.
Worth it?
Yes — Parakeet-TDT-0.6b is the sovereign ASR default on the 5090: faster, leaner, tougher on noise. Keep Whisper-large only when you need non-English or translation.
Method note: the load-once runner (scripts/asr_bench.py) and the scorer (scripts/asr_report.py,
dep-free micro-WER) are in this repo; the chart is scripts/chart_asr.py.
Sources
- parakeet.cpp (ggml runtime): https://github.com/mudler/parakeet.cpp
- Prebuilt Parakeet GGUFs: https://huggingface.co/mudler/parakeet-cpp-gguf
- LibriSpeech: https://www.openslr.org/12
- 2026 open-ASR landscape: https://huggingface.co/blog/CohereLabs/cohere-transcribe-03-2026-release