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Gemini 3.1 Flash TTS
Audio, Music & SpeechText to Speech
A natural and expressive voice generator that supports over 70 languages via text-to-speech (TTS). Features fine-grained control over style, rhythm, and emotions using natural-language audio tags. The model is also capable of handling multiple speakers simultaneously
Impressions
28,232Directory views
Category
Audio, Music & SpeechText-to-Speech
Listed
June 2026Audio, Music & Speech, Text to Speech
Screenshot
aistudio.google.comPros and cons
Pros
- Supports a wide range of over 70 languages for diverse global applications.
- Offers fine-grained emotional and rhythmic control via natural-language tags.
- Capable of managing multiple speakers within a single audio generation task.
- Flash architecture is optimized for speed and lower latency compared to larger models.
Cons
- The specific syntax for natural-language audio tags may require a learning curve to master.
- Potential usage caps or rate limits on the free tier should be evaluated for high-volume projects.
- Voice consistency and quality may vary across the 70+ supported languages and dialects.
- As a cloud-based model, it requires a stable internet connection for real-time audio generation.
FAQ
How many languages does Gemini 3.1 Flash TTS support?
Gemini 3.1 Flash TTS supports over 70 languages, making it suitable for global localization projects. Users should test specific dialects to ensure the pronunciation and rhythm meet their requirements, as performance can vary between widely used and less common languages in the dataset.
What are natural-language audio tags?
These tags allow users to influence the output using plain language instructions. You can adjust the emotional tone, rhythm, and style of the speech. This provides a more granular level of control compared to standard text-to-speech engines that often lack emotional depth.
Can this tool generate audio for more than one speaker?
Yes, the model is designed to handle multiple speakers simultaneously. This makes it a potential solution for generating dialogue-heavy content, such as podcasts, narrated stories, or interactive voice response systems where distinct vocal identities are required within the same audio stream.