We turned EmbeddingGemma 2 into a speech-to-text model

Embedding Gemma Transcribe

Google's EmbeddingGemma 2 is an embedding model. We turned it into a speech-to-text model: an hour of audio in about 13 seconds on a base MacBook Air , entirely on device.

Download for Mac (free)

v1.6 · macOS 14+ · Apple Silicon

models download on first launch (715 MB)

Can an embedding model transcribe speech?

EmbeddingGemma 2 is a multimodal embedding model: text, images, audio and video in one shared space. Say a word out loud and its audio lands close to the written word.

So we tried reading words straight out of that space.

How it works

• EmbeddingGemma 2's audio encoder stays exactly as it is.

• A small reader moves each short slice of audio into the model's text space.

• The closest of ~229,000 English words wins, and the slices become a transcript.

• A tiny extra model adds punctuation and capitals.

Only the reader is trained: under two hours on 8×H100, on openly licensed English speech. The base model was never trained for speech-to-text.

How fast?

Model

Runs on

Mac

Speed

Embedding Gemma Transcribe

Neural Engine

base M3 MacBook Air, 8 GB

288×

1 hour in 12.6 s

IBM Granite Speech 5.0 TurboCTC

GPU (MLX)

~150×

1 hour in 25.7 s

NVIDIA Parakeet TDT 0.6B v3 (FluidAudio)

M4 Pro

156×

M5 Pro

335×

Phonon-2, a Parakeet v3 fine-tune (FluidAudio)

480×

× real time: seconds of audio per second of processing. Rows 1–2: our tests on the same base M3 MacBook Air. Rows 3–5: FluidAudio's published benchmarks on larger, newer Macs and different audio. A rough guide, not a like-for-like ranking.

Where it stands

Its word error rate is about 5% right now, against 2–3% for leading speech models. We believe a better-trained reader can close that gap; that's the next iteration.

Use it

Drop in an audio file, or hold Right Option, speak and release to dictate into any app. It runs on the Mac's Neural Engine; your audio never leaves the machine.