EmbeddingGemma 2
EmbeddingGemma 2 is an open, lightweight multimodal embedding model designed to map text, code, images, video, and audio into a shared embedding space for search, retrieval, classification, routing, and RAG applications. Built on the Gemma 4 architecture and released under the Apache 2.0 license, it has 740 million parameters and is optimized for on-device inference. Its modular design can use as little as 270M parameters for text-only workloads, with optional vision and audio encoders for full multimodal support. Matryoshka Representation Learning lets developers reduce output vectors from 768 dimensions to 512, 256, or 128, lowering storage and memory requirements for local vector databases. The model supports an 8K-token context window and can process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations on local hardware.
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Gemma 3n
Gemma 3n is our state-of-the-art open multimodal model, engineered for on-device performance and efficiency. Made for responsive, low-footprint local inference, Gemma 3n empowers a new wave of intelligent, on-the-go applications. It analyzes and responds to combined images and text, with video and audio coming soon. Build intelligent, interactive features that put user privacy first and work reliably offline. Mobile-first architecture, with a significantly reduced memory footprint. Co-designed by Google's mobile hardware teams and industry leaders. 4B active memory footprint with the ability to create submodels for quality-latency tradeoffs. Gemma 3n is our first open model built on this groundbreaking, shared architecture, allowing developers to begin experimenting with this technology today in an early preview.
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LiteScribe
LiteScribe is an AI transcription platform that turns any meeting, interview, or recording into accurate text in 100+ languages, then adds AI summaries, action items, topic tags and sentiment. It measures 94.1% word accuracy on real-world English audio, rising to about 95% in High Accuracy mode. Speaker diarization, translation, PII redaction and profanity filtering are built in. Ask AI questions across your whole transcript library using 14+ models or your own API key, and store reusable prompts in a Prompt Vault. Capture from direct upload, social links, cloud storage, a Chrome recorder, and native desktop and mobile apps. Meeting Mode records Zoom, Teams, Meet and Webex calls without putting a bot into the meeting. Export to DOCX, PDF, PPTX, XLSX and SRT. The Windows, macOS and Linux desktop app also runs fully offline with on-device transcription and an on-device LLM, so sensitive audio never leaves the machine.
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Paraspeech
Paraspeech is a speech-to-text app for Mac and iOS that turns spoken thoughts into clean text with a simple hold, speak, and release workflow. On Mac, users press and hold a hotkey in the app where they want to write, speak naturally, and release; Paraspeech processes the recording and attempts to insert the result directly into the active editable field, with clipboard fallback for unsupported fields. Apple Silicon Macs can use supported local speech modes, allowing audio to be transcribed on-device and work offline after setup, while eligible cloud-backed paths are also available depending on the selected backend. Fast local models cover English, Japanese, Mandarin Chinese, and 25-language dictation, while Multilingual Large expands coverage to more than 100 languages where available. AI Rewriting can turn rambling speech into cleaner, formatted text through Cloud Cleanup or, where supported, an on-device rewrite model.
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