.Ensure being compatible with various frameworks, including.NET 6.0,. Internet Platform 4.6.2, and.NET Specification 2.0 as well as above.Lessen dependences to stop model problems as well as the requirement for binding redirects.Translating Sound Information.Among the primary performances of the SDK is actually audio transcription. Designers can easily record audio files asynchronously or in real-time. Below is an example of how to record an audio data:.using AssemblyAI.using AssemblyAI.Transcripts.var customer = brand new AssemblyAIClient(" YOUR_API_KEY").var records = wait for client.Transcripts.TranscribeAsync( new TranscriptParams.AudioUrl="https://storage.googleapis.com/aai-docs-samples/nbc.mp3". ).transcript.EnsureStatusCompleted().Console.WriteLine( transcript.Text).For nearby documents, comparable code can be utilized to accomplish transcription.wait for utilizing var stream = brand-new FileStream("./ nbc.mp3", FileMode.Open).var transcript = wait for client.Transcripts.TranscribeAsync(.stream,.brand-new TranscriptOptionalParams.LanguageCode = TranscriptLanguageCode.EnUs.).transcript.EnsureStatusCompleted().Console.WriteLine( transcript.Text).Real-Time Audio Transcription.The SDK likewise reinforces real-time sound transcription using Streaming Speech-to-Text. This attribute is actually specifically useful for treatments requiring immediate processing of audio information.using AssemblyAI.Realtime.wait for using var transcriber = brand new RealtimeTranscriber( new RealtimeTranscriberOptions.ApiKey="YOUR_API_KEY",.SampleRate = 16_000. ).transcriber.PartialTranscriptReceived.Subscribe( transcript =>Console.WriteLine($" Limited: transcript.Text "). ).transcriber.FinalTranscriptReceived.Subscribe( transcript =>Console.WriteLine($" Final: transcript.Text "). ).wait for transcriber.ConnectAsync().// Pseudocode for acquiring sound from a microphone for instance.GetAudio( async (portion) => await transcriber.SendAudioAsync( piece)).await transcriber.CloseAsync().Making Use Of LeMUR for LLM Apps.The SDK combines along with LeMUR to permit creators to build big foreign language model (LLM) functions on vocal information. Listed below is an example:.var lemurTaskParams = new LemurTaskParams.Urge="Give a short conclusion of the transcript.",.TranscriptIds = [transcript.Id],.FinalModel = LemurModel.AnthropicClaude3 _ 5_Sonnet..var action = await client.Lemur.TaskAsync( lemurTaskParams).Console.WriteLine( response.Response).Sound Intellect Models.Additionally, the SDK possesses built-in support for audio intellect versions, enabling sentiment evaluation and also other innovative components.var transcript = await client.Transcripts.TranscribeAsync( brand new TranscriptParams.AudioUrl="https://storage.googleapis.com/aai-docs-samples/nbc.mp3",.SentimentAnalysis = accurate. ).foreach (var result in transcript.SentimentAnalysisResults!).Console.WriteLine( result.Text).Console.WriteLine( result.Sentiment)// POSITIVE, NEUTRAL, or even downside.Console.WriteLine( result.Confidence).Console.WriteLine($" Timestamp: result.Start - result.End ").For additional information, see the official AssemblyAI blog.Image resource: Shutterstock.