An API powered by Google's AI technology allows you to accurately convert speech into text. You can accurately caption your content, provide a better user experience with products using voice commands, and gain insight from customer interactions to improve your service. Google's deep learning neural network algorithms are the most advanced in automatic speech recognition (ASR). Speech-to-Text allows for experimentation, creation, management, and customization of custom resources. You can deploy speech recognition wherever you need it, whether it's in the cloud using the API or on-premises using Speech-to-Text O-Prem. You can customize speech recognition to translate domain-specific terms or rare words. Automated conversion of spoken numbers into addresses, years and currencies. Our user interface makes it easy to experiment with your speech audio.
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LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents.
Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development.
Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.
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Cartesia Sonic-3.6
Sonic is an advanced text-to-speech model designed specifically for real-time voice agents, featuring a natural delivery system with a response time of less than 90 milliseconds and supporting over 40 languages seamlessly. Its primary aim is to facilitate effortless voice interactions, characterized by a tone that adapts to various contexts, a steady pacing, and speech that aligns with the natural flow of conversation. Sonic automatically interprets the emotional nuances within transcripts, adjusting its delivery accordingly, and allows for the direct insertion of non-verbal cues like laughter into the spoken text. Faithful to the original transcripts, the model generates clear audio across different languages and voice options while effortlessly managing alphanumeric data, including order and phone numbers, email addresses, and IDs, without requiring any prior processing. Its context-aware pronunciation ensures that heteronyms are articulated correctly based on surrounding terms, and customizable pronunciation dictionaries empower teams to dictate how specific proper nouns and industry-related terminology should be pronounced. This comprehensive approach not only enhances the quality of interactions but also tailors the user experience to meet diverse communication needs.
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GPT-Realtime-1.5
GPT-Realtime-1.5 is an advanced real-time voice model from OpenAI designed to power interactive audio-based applications such as voice agents and customer support systems. It supports multimodal inputs, including text, audio, and images, and produces both text and audio outputs for dynamic conversations. The model is optimized for speed, delivering fast and responsive interactions that feel natural in live environments. With a 32,000-token context window, it can manage long conversations while maintaining continuity and context. It is particularly suited for applications that require real-time communication, such as call centers and virtual assistants. The model includes support for function calling, enabling seamless integration with external tools and APIs. It is accessible through multiple endpoints, including realtime, chat completions, and responses APIs. Pricing is based on token usage, with separate rates for text, audio, and image processing. The model is designed for scalability, supporting high request volumes depending on usage tiers. Overall, it enables developers to build fast, reliable, and scalable voice-driven applications.
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