Embedding Gemma Transcribe
B · Useful, limited A one-page product note from an independent builder with concrete speed and error figures from its own tests; a credible idea, but self-reported and without a published evaluation.
Summary based on the full text·Original: about 2 minutes to read
In short
A developer at haku.sh turned Google's EmbeddingGemma 2, a multimodal embedding model never trained for speech-to-text, into an on-device transcriber for Macs. The audio encoder is left frozen. A small trained "reader" maps short slices of audio into the model's text space and picks the closest of about 229,000 English words, and a tiny extra model adds punctuation. It transcribes an hour of audio in 12.6 seconds on a base MacBook Air, at about 5% word error rate.
Key numbers
- 1 hour in 12.6 stranscription time for an hour of audio on a base M3 MacBook Air with 8 GB (288x real time)
- about 5%word error rate, against 2-3% for leading speech models
- under two hours on 8×H100training time for the reader, the only trained component
- ~229,000English words the reader matches each audio slice against
As reported by the source.
English·Web page·opens eg2.haku.sh
What it reports
- Speed: 288x real time on a base M3 MacBook Air (Neural Engine), against about 150x for IBM Granite Speech 5.0 TurboCTC on the same machine.
- Published figures for NVIDIA Parakeet variants on newer Macs range from 156x to 480x; the author calls this a rough guide, not a like-for-like ranking.
- Word error rate about 5%, against 2-3% for leading models; the author expects a better-trained reader to close the gap.
- Training: under two hours on 8×H100 on openly licensed English speech.
- Free macOS app, 715 MB of models on first launch; audio stays on the device.
How they got it
The author's own speed tests on one laptop for the first two rows; other rows copied from FluidAudio's published benchmarks on different hardware and audio. No test set, error breakdown or code is given on the page.
Limits
- No named test set for the word error rate, so the 5% figure cannot be checked.
- Speed comparisons mix the author's tests with third-party figures on different machines.
- English only; per-word matching against a fixed vocabulary will struggle with names and rare terms.
Why it matters for companies
Private, local transcription of meetings and interviews is getting cheap enough to run on standard laptops, which removes one reason to send audio to a cloud service. The technique of reading another modality out of a shared embedding space with a small adapter may also cut the cost of building narrow tools. At 5% error, it suits drafts and search, not records that need to be exact.
Summary based on the full text. Read the original for the full argument and data.Read the original on eg2.haku.sh
- Tags
- Local inference