C# Speech-to-Text Call Recorder: Build a Real-Time Transcription App

How to Create a C# Call Recorder with Speech-to-Text Transcription

This guide shows a practical, end-to-end approach to build a C# application that records calls (VoIP or system audio) and transcribes them to text using a speech-to-text API. It covers architecture, required libraries, recording options, implementation steps, and tips for accuracy and compliance.

Overview

• Goal: Capture call audio, save or stream it, send to a speech-to-text service, and store/display transcriptions.
• Assumptions: Windows desktop/server environment, .NET 7+ (or .NET 6), familiarity with C# and async programming.
• Components: Audio capture (system or VoIP), optional audio preprocessing, speech-to-text API (e.g., OpenAI, Azure Speech, Google Speech-to-Text), storage (files or database), simple UI/CLI.

Architecture

1. Audio Capture:

   • Option A: System audio loopback (records any audio playing through speakers).
   • Option B: Capture from VoIP app using virtual audio devices (e.g., VB-Audio Virtual Cable) or app-specific APIs.
   • Option C: Capture from microphone and speaker separately and merge channels.

2. Processing:

   • Optional voice activity detection (VAD), noise reduction, and audio format conversion (16-bit PCM, 16 kHz+).

3. Transcription:

   • Send audio segments (streamed or batch) to a speech-to-text API and receive transcripts.

4. Storage/UI:

   • Save audio files and transcripts; optionally provide real-time transcription display.

Required Libraries & Tools

• .NET ⁄₇ SDK
• NAudio (audio capture/processing) — NuGet
• A speech-to-text client SDK or HTTP client (for OpenAI/other APIs)
• Optional: WebSocket or streaming client for real-time APIs
• Optional: FFmpeg (for format conversion) or use NAudio for PCM conversions
• Optional: Virtual audio cable (VB-Audio) for capturing both sides of a call

Step-by-step Implementation

1) Setup project

• Create a new console or WPF project:

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  dotnet new console -n CallRecorder cd CallRecorder dotnet add package NAudio 

• Add any speech-to-text SDK package or prepare HttpClient for API calls.

2) Capture audio with NAudio (loopback)

• Use WasapiLoopbackCapture to record system output. Example pattern:

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  var capture = new WasapiLoopbackCapture(); var writer = new WaveFileWriter(outputPath, capture.WaveFormat); capture.DataAvailable += (s, e) => writer.Write(e.Buffer, 0, e.BytesRecorded); capture.RecordingStopped += (s, e) => { writer.Dispose(); capture.Dispose(); }; capture.StartRecording(); // call capture.StopRecording() when done 

• For microphone input, use WasapiCapture or WaveInEvent.

3) Save or stream audio

• Option: Save chunks to disk (e.g., every 10–30 seconds) to avoid huge uploads and enable partial transcription.
• Example chunking approach:
  • Create a MemoryStream buffer; on timed interval (or on silence detection) write to a new WAV file and clear buffer.

4) Preprocess audio (optional but recommended)

• Convert to mono 16-bit PCM at 16 kHz or 16–48 kHz depending on the STT API.
• Use NAudio’s WaveFormatConversionStream or resample with MediaFoundationResampler:

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  var resampler = new MediaFoundationResampler(sourceWaveProvider, new WaveFormat(16000, 16, 1)); WaveFileWriter.CreateWaveFile(outputPath, resampler); 

5) Choose a Speech-to-Text API

• Options: OpenAI Whisper/Realtime or Whisper API, Azure Speech-to-Text, Google Cloud Speech-to-Text, DeepSpeech, or Vosk (on-prem).
• For this guide we’ll outline a generic HTTP upload flow suitable for OpenAI-like or other REST APIs.

6) Send audio to STT service

• Batch upload example (HTTP multipart):

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  using var http = new HttpClient(); using var content = new MultipartFormDataContent(); var audioBytes = File.ReadAllBytes(wavPath); content.Add(new ByteArrayContent(audioBytes), “file”, “chunk.wav”); content.Add(new StringContent(“en”), “language”); var resp = await http.PostAsync(”https://api.speech.example/v1/transcribe”, content); var json = await resp.Content.ReadAsStringAsync(); 

• For streaming/real-time APIs, use WebSocket or gRPC clients per provider docs.

7) Handle transcription results

• Parse returned JSON for timestamps and speaker labels (if provided).
• Append partial transcripts to UI or save final transcripts alongside audio files.
• Example storage layout:
  • recordings/
    • call_2026-02-04_10-15.wav
    • call_2026-02-0410-15.json (transcript + metadata)

8) Optional: Speaker diarization and timestamps

• Some APIs provide speaker diarization. If not, you can:
  • Record separate channels for local vs remote and label accordingly.
  • Use third-party diarization tools (pyannote.audio) offline, but that requires cross-language integration.

Sample minimal program (conceptual)

• Main tasks: start recording, chunk files every N seconds, send each chunk for transcription, append text to transcript file.
• Pseudocode summary:

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  Start loopback capture Every 15s or on silence:save chunk.wav   resample to 16k mono   upload to STT API   append returned text to transcript.txt On stop:   finalize transcript 

Tips for accuracy

• Use high sample rates (16–48 kHz) and 16-bit PCM.
• Prefer single-speaker or separate channels for higher diarization accuracy.
• Apply noise reduction and VAD to remove silence/noise before transcription.
• Test and tune API parameters (language model, profanity filter, punctuation, timestamps).

Compliance & Privacy (brief)

• Obtain consent from call participants before recording.
• Store recordings and transcripts securely (encrypted at rest).
• Implement access controls and audit logs.

Next steps / Enhancements

• Implement live streaming transcription for real-time display.
• Add UI for playback with synced transcript highlighting.
• Integrate speaker identification and sentiment analysis.
• Support multiple STT providers with a common interface.

If you want, I can:

• Provide a full runnable code sample (console or WPF) that records loopback audio, chunks it, converts format, and uploads to a specific STT API (specify provider).