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Google DeepMind更新于 10月13日 19:31

Google DeepMind 发布 Gemini 3.5 Transcribe 语音转文本模型

Introducing Gemini 3.5 Transcribe

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Google DeepMind 发布 Gemini 3.5 Transcribe,一款面向实时转写的语音转文本模型,已在 Gemini API、Google AI Studio 和 Gemini Enterprise Agent Platform 向开发者开放。相比此前的 Chirp 3,其最终转写时间据 Artificial Analysis 测量提升 70%,在 FLEURS 基准上流式模式 WER 为 5.50%、非流式模式为 5.04%,并支持多说话人区分、词级时间戳和实时语言切换。该模型同时接入 Gboard、Antigravity、Gemini app 和 Chrome 等产品,LangChain、LiveKit、Vercel 等平台通过 Gemini Live API 提供支持。

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Gemini 3.5 Transcribe 相比 Chirp 3 的延迟与 WER 数据,可用于判断实时转写场景的选型。

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Aug 26, 2026

Our latest speech-to-text model designed for precise and intelligent real-time transcription.

Diego Melendo Casado

Senior Director, Engineering, Gemini Audio

Luke Leonhard

Chief of Staff, Gemini Audio, on behalf of Gemini Audio Team

Your browser does not support the audio element.

Today, we’re introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions. Unlike conventional speech recognition models that struggle with background noise, complex jargon, and disfluency cleanup, Gemini 3.5 Transcribe converts raw audio directly into accurate, polished, formatted text.

Across our products like the Gemini app and on Android, we’ve seen consumers already benefiting from this transcription model with new voice capabilities like Rambler on Android and in the Gemini app on macOS. Now, developers can build similar capabilities with Gemini 3.5 Transcribe in the Gemini API in Google AI Studio and Gemini Enterprise Agent Platform .

We've built 3.5 Transcribe to plug seamlessly into your developer workflows, whether you’re building voice agents, real-time captioning tools, or post-call analytics pipelines. The model is available across two separate APIs:

Gemini 3.5 Transcribe is designed to capture your natural speaking style to better understand your intent and recognize custom vocabulary, so you can execute tasks with your voice.

Gemini 3.5 Transcribe handles live language switches and seamless streaming transcription

Watch Gemini 3.5 Transcribe clean up speech disfluencies with smart transcription capabilities.

3.5 Transcribe delivers transcription with multi-speaker attribution and word-level timestamps.

Gemini 3.5 Transcribe’s performance represents a major advancement from our previous transcription model, Chirp 3, offering new capabilities, improved word error rates, and significantly better latency. As measured by Artificial Analysis, time to final transcription, for example, improves by 70%. On the FLEURS benchmark across a set of top languages and locales, the model delivers precise multilingual performance, improving over Chirp 3, and achieving a 5.50% WER in streaming mode and 5.04% WER in non-streaming use-cases.

In addition to the Gemini API in the Google AI Studio and Gemini Enterprise Agent Platform, 3.5 Transcribe goes further than standard speech-to-text to make working across Google feel more natural and intuitive. By bringing context-aware understanding directly into everyday surfaces like Gboard, Antigravity, the Gemini app, and Chrome, it captures nuances, intent, and inline edits with ease.

Gemini 3.5 Transcribe lets you analyze files, generate images, and search in the Gemini app on macOS using just your voice.

See how Gemini 3.5 Transcribe uses Rambler on Android to automatically remove filler words and clean up speech.

Gemini 3.5 Transcribe leverages screen context on Google Antigravity to ensure accurate transcription accuracy.

By leveraging the Gemini Live API, developer platforms such as Agora , Fishjam , LangChain , LiveKit , Pipecat , Vercel , and Vision Agents enable developers to build and deploy high-performance voice-driven interfaces with ease. These platforms manage complex real-time media streaming infrastructure behind the scenes, allowing developers to focus entirely on crafting the user experience.

Companies like vivo, Intellitek Health, and Lingopal have also shared positive feedback on 3.5 Transcribe, highlighting its impressive latency, accuracy, and expansive language support.

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来源

Google DeepMind