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Embedding Gemma Transcribe

▲ 436 points • 100 comments • by amrrs • 3w ago • HN discussion ↗

Pangram verdict · v3.3

We believe that this text is a mix of AI and human-written content.

32 %

AI likelihood · overall

Mixed
65% human-written 35% AI-generated
SEGMENTS · HUMAN 2 of 4
SEGMENTS · AI 0 of 4
WORD COUNT 324
PEAK AI % 64% · §2
Analyzed
Oct 13
backend: pangram/v3.3
Segments scanned
4 windows
avg 81 words each
Distribution
65 / 35%
human / AI fraction
Verdict
Mixed
Pangram v3.3

Article text · 324 words · 4 segments analyzed

Human AI-generated
§1 Human · 25%

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 Siliconmodels 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.

§2 Mixed · 64%

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.

§3 Human · 14%

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? ModelRuns onMacSpeed Embedding Gemma TranscribeNeural Enginebase M3 MacBook Air, 8 GB 288×1 hour in 12.6 s IBM Granite Speech 5.0 TurboCTCGPU (MLX)base M3 MacBook Air, 8 GB ~150×1 hour in 25.7 s NVIDIA Parakeet TDT 0.6B v3 (FluidAudio)Neural EngineM4 Pro156× NVIDIA Parakeet TDT 0.6B v3 (FluidAudio)Neural EngineM5 Pro335× Phonon-2, a Parakeet v3 fine-tune (FluidAudio)Neural EngineM5 Pro480× × real time: seconds of audio per second of processing. Rows 1–2: our tests on the same base M3 MacBook Air.

§4 Mixed · 53%

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.