Evidence readout
On the same eight LibriSpeech clips, browser Parakeet v3 scored 0/82 word errors versus native WhisperKit's 7/82, matched all four proper nouns, had zero repetition errors, and decoded 3.51x faster; its 33.84x median short-clip real-time factor missed the frozen 50x bar
What the attempt taught
Browser WebGPU ASR can beat a native large-model incumbent on both bounded WER and latency while still failing an absolute throughput target; short-clip fixed cost and long-form throughput are different measurements.
Why it stopped or stayed bounded
Quality, paired native latency, repetition, and offline-warm gates passed, but fixed dispatch overhead on 2-7 second clips held the preregistered median throughput below 50x; the optional first download is 652.7 MiB.
Method and scope
- Experiment family: browser product.
- Record kind: model evaluation.
- Objective: browser asr.
- Methods: evaluation.
- Base models: parakeet-tdt-0.6b-v3.
Disposition
Close this frozen gate without relabeling it; batching, long-form amortization, Safari, or a larger WER set are fresh experiments.
This retained disposition is historical evidence, not authorization to restart the experiment. A new run needs a fresh question, frozen evaluator, explicit resource budget, and scoped tracking issue.
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/attempts.json. The links below are the tracked evidence and explanatory sources preserved with the record.