AI-Powered Speech-to-Text SDK for .NET Applications
Transform Speech into Actionable Text with LM-Kit's Edge AI Transcription Engine
Accurate, Efficient, and On-Device Speech Recognition
LM-Kit’s Speech-to-Text engine transforms audio content into structured, actionable data on-device, with zero dependency on the cloud. Whether you’re analyzing phone calls, podcasts, meetings, or interviews, our AI transcription system supports audio indexing, semantic search, and real-time transcription in a single unified pipeline. It seamlessly integrates with LM-Kit’s powerful RAG engine for multimodal workflows, searching across both voice and text.
Why LM-Kit Speech-to-Text?
Organizations frequently encounter valuable insights locked within audio content. Manual transcription is slow, expensive, and error-prone. LM-Kit’s AI-powered Speech-to-Text automates this process, significantly improving efficiency, accuracy, and productivity, enabling quick decision-making and enhanced workflow integration.
Key Features
On-Device AI Transcription
Run powerful transcription models directly on-device to ensure privacy, reduce latency, and stay in full control of your data.
Batch Audio File Support
Transcribe entire audio files in one shot, ideal for meetings, calls, podcasts, interviews, and multimedia content.
100+ Languages Automatically Detected
Detect and transcribe speech in over 100 languages without manual configuration. Ideal for global content and multilingual scenarios.
Structured Output with Developer-Friendly API
Receive JSON-based structured outputs with timestamps and optional metadata, or use our high-level API for rapid application integration.
Semantic Indexing and Cross-Modal Retrieval
Enable powerful semantic search on transcribed audio by pairing with LM-Kit’s RAG engine, making audio content discoverable and context-aware.
Universal WAV Compatibility
Support for any .wav ile, any sample rate, any number of channels (mono, stereo, multi). No conversion needed.
Flexible Model Catalog
Choose from a curated and ever-expanding set of transcription models—lightweight options for constrained devices, or high-accuracy models for demanding environments.
Swap models through configuration only -> your code stays the same.
Built for Developer Velocity
LM-Kit simplifies integration with a single, unified API. No boilerplate. No rework when switching models. Whether you’re experimenting or deploying at scale, the developer experience remains fast and consistent.
Explore Usage Examples
Speech to Text Demo
The LM-Kit.NET Speech-to-Text demo is a console application that transcribes WAV audio files into structured text using models like OpenAI Whisper. It features model selection, confidence scoring, language detection, and on-device processing. With a simple API, it enables developers to integrate fast, private, and accurate transcription into their applications effortlessly.
Language Detection From Audio (Code snippet)
using LMKit.Media.Audio;
using LMKit.Model;
using LMKit.Speech;
namespace YourNamespace
{
class Program
{
static void Main(string[] args)
{
// Instantiate the Whisper model by ID.
// See the full model catalog at:
// https://docs.lm-kit.com/lm-kit-net/guides/getting-started/model-catalog.html
var model = LM.LoadFromModelID("whisper-large-turbo3");
// Open the WAV file from disk for analysis
var wavFile = new WaveFile(@"d:\discussion.wav");
// Create the speech-to-text engine for streaming transcription and language detection
var engine = new SpeechToText(model);
// Detect the primary language spoken in the audio file; returns an ISO language code
var language = engine.DetectLanguage(wavFile);
// Output the detected language to the console
Console.WriteLine($"Detected language: {language}");
}
}
}
Audio to Text (Code snippet)
using LMKit.Media.Audio;
using LMKit.Model;
using LMKit.Speech;
namespace YourNamespace
{
class Program
{
static void Main(string[] args)
{
// Instantiate the Whisper model by ID.
// See the full model catalog at:
// https://docs.lm-kit.com/lm-kit-net/guides/getting-started/model-catalog.html
var model = LM.LoadFromModelID("whisper-large-turbo3");
// Open the WAV file from disk for transcription
var wavFile = new WaveFile(@"d:\discussion.wav");
// Create the speech-to-text engine for streaming, multi-turn transcription
var engine = new SpeechToText(model);
// Print each segment of transcription as it’s received (e.g., real-time display)
engine.OnNewSegment += (sender, e) =>
Console.WriteLine(e.Segment);
// Transcribe the entire WAV file; returns the full transcription information
var transcription = engine.Transcribe(wavFile);
// TODO: handle transcription results (e.g., save to file or process further)
}
}
}
Common Use Cases
Business Documentation
Convert meeting recordings into clean transcripts and summaries.
Healthcare Workflows
Capture and structure voice notes and medical consultations with multilingual support.
Education Platforms
Transcribe multilingual lectures and courses for accessibility and search.
Media & Entertainment
Index interviews and spoken content for editing, archiving, and discovery.
Customer Service Intelligence
Analyze support calls across regions and languages for sentiment and operational insights.
Legal & Compliance Documentation
Transcribe depositions, hearings, and legal consultations with accuracy and multilingual support, facilitating case preparation, audit trails, and regulatory compliance.
Start Building Today
Explore our docs, try the demo, or integrate instantly with LM-Kit.NET’s SDK.