.Make sure compatibility with a number of frameworks, including.NET 6.0,. NET Framework 4.6.2, and.NET Criterion 2.0 as well as above.Reduce dependencies to prevent variation disagreements and also the demand for tiing redirects.Recording Audio Information.Some of the major functionalities of the SDK is actually audio transcription. Creators can transcribe audio files asynchronously or in real-time. Below is an example of exactly how to transcribe an audio report:.making use of AssemblyAI.making use of AssemblyAI.Transcripts.var client = new AssemblyAIClient(" YOUR_API_KEY").var transcript = await client.Transcripts.TranscribeAsync( brand-new TranscriptParams.AudioUrl="https://storage.googleapis.com/aai-docs-samples/nbc.mp3". ).transcript.EnsureStatusCompleted().Console.WriteLine( transcript.Text).For local files, similar code may be made use of to attain transcription.await making use of var flow = brand new FileStream("./ nbc.mp3", FileMode.Open).var records = wait for client.Transcripts.TranscribeAsync(.flow,.brand-new TranscriptOptionalParams.LanguageCode = TranscriptLanguageCode.EnUs.).transcript.EnsureStatusCompleted().Console.WriteLine( transcript.Text).Real-Time Audio Transcription.The SDK likewise sustains real-time sound transcription making use of Streaming Speech-to-Text. This function is actually particularly helpful for uses needing urgent handling of audio data.utilizing AssemblyAI.Realtime.wait for using var scribe = brand new RealtimeTranscriber( brand-new RealtimeTranscriberOptions.ApiKey="YOUR_API_KEY",.SampleRate = 16_000. ).transcriber.PartialTranscriptReceived.Subscribe( transcript =>Console.WriteLine($" Limited: transcript.Text "). ).transcriber.FinalTranscriptReceived.Subscribe( transcript =>Console.WriteLine($" Last: transcript.Text "). ).wait for transcriber.ConnectAsync().// Pseudocode for receiving sound from a microphone as an example.GetAudio( async (part) => wait for transcriber.SendAudioAsync( chunk)).wait for transcriber.CloseAsync().Utilizing LeMUR for LLM Apps.The SDK integrates with LeMUR to make it possible for designers to create big language design (LLM) apps on vocal records. Below is an example:.var lemurTaskParams = brand new LemurTaskParams.Urge="Give a short summary of the transcript.",.TranscriptIds = [transcript.Id],.FinalModel = LemurModel.AnthropicClaude3 _ 5_Sonnet..var response = wait for client.Lemur.TaskAsync( lemurTaskParams).Console.WriteLine( response.Response).Sound Intellect Versions.Also, the SDK possesses integrated assistance for audio intelligence models, permitting feeling analysis as well as various other state-of-the-art attributes.var transcript = await client.Transcripts.TranscribeAsync( brand new TranscriptParams.AudioUrl="https://storage.googleapis.com/aai-docs-samples/nbc.mp3",.SentimentAnalysis = accurate. ).foreach (var lead to transcript.SentimentAnalysisResults!).Console.WriteLine( result.Text).Console.WriteLine( result.Sentiment)// FAVORABLE, NEUTRAL, or even downside.Console.WriteLine( result.Confidence).Console.WriteLine($" Timestamp: result.Start - result.End ").To read more, explore the formal AssemblyAI blog.Image source: Shutterstock.