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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Gemini 3.5 Transcribe
Gemini 3.5 Transcribe represents Google’s most advanced speech-to-text technology to date, tailored for sophisticated voice interactions and immediate transcription. Rather than merely translating speech into text, it converts raw audio into polished, precise, and well-structured text, effectively managing background noise, intricate terminology, various accents, dialects, and natural speech rhythms. Its intelligent transcription capabilities automatically account for self-corrections, eliminate filler words like “ums” and “ahs,” and present the final output in an easily readable format. This model offers continuous bidirectional streaming with sub-second response times, making it ideal for interactive voice applications, alongside the ability to process pre-recorded audio for meetings, call logs, and other recordings while ensuring speaker attribution and word-level timestamps. Additionally, its custom vocabulary feature enables the recognition of specialized terms, unique spellings, postal codes, order IDs, and other industry-specific language, enhancing its versatility for various use cases. As a result, Gemini 3.5 Transcribe stands out as a powerful tool for anyone seeking high-quality transcription services.
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Voxtral Small Transcribe 3
Voxtral Small Transcribe 3 is a versatile multilingual speech-to-text model tailored for converting recorded audio into text. Offered via an audio transcription API by Mistral, it incorporates features like timestamp documentation, speaker diarization, and context biasing, enabling developers to input key phrases that enhance the recognition of specific terms and names relevant to particular domains. This model is capable of automatically identifying supported languages or can utilize a specified language code, accommodating over 50 languages in total. It is particularly suitable for application developers focused on creating workflows for transcribing recorded audio, such as meetings, interviews, call transcriptions, customer service systems, and voice-agent processes. Additionally, Mistral outlines options for self-deployment of the Voxtral 3 series, which includes hosting on private clouds or on-premises solutions, making it adaptable to various organizational needs. Furthermore, the flexibility of the model ensures that it remains a valuable tool for enhancing communication efficiency across different sectors.
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