Hacker News AI · 2026/10/13 19:27:14
谷歌 EmbeddingGemma 2 改造为本地语音转文字：MacBook Air 13 秒转录 1 小时音频，仅训练轻量读取器
开发者将谷歌多模态嵌入模型 EmbeddingGemma 2 改造为高性能本地语音转文字工具，在基础款 MacBook Air 上实现 1 小时音频仅需约 13 秒完成转录。该方案冻结原始音频编码器，仅训练一个轻量级“读取器”将音频切片映射至文本空间并匹配最近词汇，全程在 Apple Neural Engine 运行且数据不出设备。
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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) | base M3 MacBook Air, 8 GB | ~150×1 hour in 25.7 s |
| NVIDIA Parakeet TDT 0.6B v3 (FluidAudio) | Neural Engine | M4 Pro | 156× |
| NVIDIA Parakeet TDT 0.6B v3 (FluidAudio) | Neural Engine | M5 Pro | 335× |
| Phonon-2, a Parakeet v3 fine-tune (FluidAudio) | Neural Engine | M5 Pro | 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.