Today Google announces significant improvements to the Gemini 2.5 Flash and Gemini 2.5 Pro Text-to-Speech models, designed to give you more control over style, pacing, and voices in complex scenarios.
What changes in Gemini 2.5 TTS
Google releases two new models in preview: Gemini 2.5 Flash TTS (optimized for low latency) and Gemini 2.5 Pro TTS (optimized for quality). These replace the TTS models they published in May and are already available to try in Google AI Studio, the Playground, and via the Gemini API.
Key points:
- Richer expressiveness and better adherence to style instructions.
- Context-aware pacing control.
- More consistent multi-speaker dialogues and multilingual capabilities.
Technical improvements and why they matter
If you work on audiobooks, e-learning, product tutorials, or podcasts, you know a voice has to do more than read text: it has to interpret. These updates address three critical layers.
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Expressiveness and style: the model now follows style prompts more faithfully. Do you want a voice 'cheerful and upbeat' or 'somber and serious'? The model adapts tone, intonation, and nuance so the performance fits the role.
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Contextual pacing: pacing stops being a fixed value. The system adjusts speed based on content: pause for emphasis, speed up in action sequences, or slow down for complex explanations. It also improves the ability to follow explicit instructions about pace.
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Dialogue and multi-speaker consistency: in conversations, the model maintains character identities more coherently and makes natural transitions between speakers — useful for podcasts, simulated interviews, and narrative games. These improvements apply across the 24 supported languages, preserving tone and timbre per character.
Relevant details for developers
As technical content, here are practical notes:
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Latency vs quality: use
Gemini 2.5 Flash TTSwhen your app needs quick responses (for example, real-time assistants). PrioritizeGemini 2.5 Pro TTSfor recordings where fidelity and vocal color matter more. -
Prompt engineering: the key is still crafting precise style prompts. State tone, pacing, and emotion in the initial instruction; the model now responds with greater fidelity. You can combine style instructions with time markers or pause symbols to guide pacing.
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Multi-speaker: to keep voices consistent, define attributes per character (age, timbre, emotion) and reuse them each time the speaker changes. This prevents the voice from 'floating' between turns.
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Localization and technical pronunciation: Google mentions improvements in technical pronunciations and intonation control; helpful for specific terminology in e-learning and product videos.
Use cases and practical examples
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Audiobooks and narrative: the narrator can start nervous and speed up into relief. Example prompt to test pacing:
Style: You are a storyteller for a mystery novel. Start nervous, accelerate into excitement and reliefand then a paragraph of text. -
Podcasts and multi-character content: create natural conversations where each character keeps their vocal identity.
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Audio creation platforms: partners like Wondercraft already use Gemini TTS in modes like Convo Mode and Director Mode to give fine control over delivery, pronunciation, and nonverbal editing.
How to start today
The models are available via the Gemini API in Google AI Studio. Google suggests exploring the developer docs, the prompting guide, and the Gemini API Cookbook for examples and best practices. You can also try the experience in the Playground and experiment with 'vibe coding' to iterate voices quickly.
Here’s a practical idea: start with Gemini 2.5 Flash TTS if you’re iterating on quick UX tests, then move to Gemini 2.5 Pro TTS for your production master once voices and pacing are dialed in.
Thinking about TTS today is not just converting text to audio; it’s designing performances. These updates make control finer and more reproducible, and that changes how you build listening experiences.
Source
https://blog.google/technology/developers/gemini-2-5-text-to-speech