Gnani AI Launches Timbre v3.0 Text-to-Speech Model With Expressive AI Voices Across Indian Languages

Gnani AI Introduces Timbre v3.0 With Expressive Voice Controls

Gnani AI launched Timbre v3.0 on October 14, a text-to-speech model that gives businesses and content creators precise control over how AI-generated voices deliver speech1

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. The Bengaluru-based company's multilingual speech model introduces audio tags that allow developers to direct voices to whisper, laugh, shout, pause, or adjust pace at specific moments within the content. Timbre v3.0 currently powers more than one lakh hours of voice calls, demonstrating its real-world deployment across enterprise applications1

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Audio Tags Enable Nuanced Expression Across Indian Languages

The model supports 10 languages, including Hindi, Bengali, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Tamil, Telugu, and Indian-accented English1

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. Developers can insert audio tags directly into text to control how AI voices deliver specific lines. These tags include cues for laughing, giggling, sighing, whispering, and shouting, as well as instructions to pause, slow down, or speak with excitement1

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. The model can also generate non-verbal sounds such as coughs, sniffles, yawns, and exhales, expanding creative possibilities for games, animation, podcasts, advertisements, social media videos, and e-learning applications1

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. For text without explicit audio tags, Timbre v3.0 automatically adjusts its delivery based on the meaning, tone, and punctuation of each sentence1

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Real-Time Generation Powers Live Customer Conversations

Timbre v3.0 generates its first audio in under 200 milliseconds, making it suitable for real-time voice conversations1

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. The model supports streaming playback, allowing audio to begin before the model finishes generating the full response1

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. This real-time generation capability enables deployment across customer service voice agents, IVR systems, and live phone conversations. The model also supports long-form speech synthesis for extended narration and responses, providing flexibility to use the same voice across phone systems, applications, and digital content3

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Text Normalization Addresses Enterprise Use Cases

Gnani AI emphasizes the model's text normalization capabilities that convert written numbers, dates, currencies, symbols, and abbreviations into natural speech synthesis1

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. This process helps distinguish between amounts that need to be read as sums and account digits that need to be spoken individually. "A payment reminder needs to read an amount clearly. An insurance message needs to give the listener time to absorb a date," said Bharath Shankar, Chief Product and Engineering Officer at Gnani AI2

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. For example, voice agents can clearly communicate payment amounts, account numbers, due dates, and interest rates in banking, financial services, and insurance applications1

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. Developers can adjust speaking speeds to give listeners more time to understand important information or move faster through routine updates1

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Consistent Voice Character Across Multiple Languages

The unified multilingual speech model maintains a voice's character across languages, allowing businesses to use a consistent voice for customer interactions across different regions1

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. Timbre v3.0 offers multiple prebuilt male and female voices that retain their characteristics when switching between Indian languages2

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. This helps businesses maintain a recognizable brand voice across regions while serving customers in their preferred language. Enterprise applications include payment reminders, collections, know-your-customer processes, customer onboarding, insurance renewals, claims, appointment reminders, and customer support across financial services, healthcare, and telecom sectors1

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. Beyond business calls, the model supports audio articles, audiobooks, in-app guidance, and accessibility tools that read digital content aloud in a user's preferred language1

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