
A lightweight text-to-speech model (4 billion parameters) capable of generating realistic and expressive speech in 9 languages, with support for various dialects. Available via API and can be tested directly in the studio
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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
1 Flash TTS is a specialized text-to-speech model designed for speed and expressive depth. It stands out in the audio generation category by supporting over 70 languages, providing a robust solution for developers and creators targeting international audiences.
Unlike traditional TTS systems that can sound monotonous, this model utilizes natural-language audio tags, allowing users to specify emotions, rhythm, and style through simple text commands. This level of control is intended to produce more human-like and engaging audio.
Furthermore, the model's ability to handle multiple speakers simultaneously opens up possibilities for complex narrative content and interactive dialogues. As part of the Flash series, it is engineered for efficiency, making it a candidate for applications where response time is critical.
While the tool is listed as free, users should evaluate the consistency of the output across different languages and verify any specific usage caps on the official platform before full-scale implementation.

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Gemini 3.1 Flash TTS is listed as free on AIForest. Users are encouraged to check the official documentation for current usage limits, potential API costs for high-volume requests, and whether any paid tiers are available for advanced commercial integration.
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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.
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.
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.
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