C# Speech-to-Text Call Recorder: Build Real-Time Transcription for CallsRecording and transcribing phone or VoIP calls in real time is increasingly valuable across customer support, compliance, accessibility, and analytics. This article walks through designing and implementing a robust C# speech-to-text call recorder that captures call audio, streams it to a speech recognition service, and produces near real-time transcripts. We’ll cover architecture, audio capture, streaming, integration with cloud or local ASR (automatic speech recognition) services, handling multi-party calls, performance and accuracy considerations, security and compliance, and sample code to get you started.
Overview and goals
A practical C# speech-to-text call recorder should:
- Capture high-quality audio from PSTN or VoIP calls.
- Stream audio in near real time to an ASR service (cloud or on-prem).
- Produce accurate, time-aligned transcripts and speaker labels when possible.
- Store recordings and transcripts securely for later retrieval and analysis.
- Scale to handle multiple concurrent calls with predictable latency.
- Respect legal and privacy requirements for call recording and data handling.
This guide assumes you have basic C#/.NET experience and some familiarity with audio formats and networking.
Architecture options
High-level architectures vary by telephony source and recognition backend:
- Telephony source:
- PSTN via SIP gateways or telephony providers (Twilio, Plivo, SignalWire).
- VoIP/SIP PBX systems (Asterisk, FreeSWITCH, 3CX).
- Softphone or desktop capture (Windows WASAPI, loopback).
- Recognition backend:
- Cloud ASR APIs (Azure Speech, Google Cloud Speech-to-Text, AWS Transcribe, Whisper API providers).
- Self-hosted/open models (OpenAI Whisper running locally, Vosk, Kaldi).
- Deployment model:
- Edge/on-prem for low latency or compliance.
- Cloud for scale and managed models.
Common pattern: Telephony bridge captures audio → audio frames streamed to a processing service → processing service forwards audio to ASR in streaming mode → ASR returns interim/final transcripts → transcripts stored and optionally returned to client UI.
Audio capture and formats
Quality and format matter for recognition accuracy.
Key considerations:
- Sample rate: 16 kHz or 8 kHz depending on telephony type. Wideband/VoIP often uses 16 kHz; PSTN narrowband often 8 kHz.
- Sample format: 16-bit PCM (signed little-endian) is standard for many ASR systems.
- Channels: For simpler pipelines, use mono (single channel). For speaker separation, capture separate channels for each participant (caller vs. callee).
- Frame size: ASR streaming typically accepts small frames (e.g., 20–100 ms). Sending consistent frame sizes reduces latency jitter.
Capturing audio in C#:
- For low-level capture on Windows, use NAudio (managed) to access WASAPI or WaveIn.
- For telephony integration, many providers deliver audio streams (WebSocket, RTP) or recorded files (WAV). Use RTP libraries (e.g., SIPSorcery) or provider SDKs (Twilio, SignalWire) to obtain audio.
Example: using NAudio to capture microphone/loopback (overview, code below in sample section).
Real-time streaming to ASR
Most modern ASR services support streaming recognition. General flow:
- Open a streaming session (WebSocket or gRPC).
- Send audio in base64 or binary frames at regular intervals.
- Receive interim hypotheses (low-latency partial transcripts) and final results.
- Optionally send metadata (call ID, speaker ID, language, punctuation preferences).
Cloud options:
- Azure Speech: supports real-time WebSocket and SDKs for .NET; provides speaker diarization (with limitations), profanity masking, and custom models.
- Google Cloud Speech-to-Text: gRPC streaming with real-time interim results and speaker diarization.
- AWS Transcribe: streaming via WebSocket; supports vocabulary filtering and real-time transcription.
- Open-source/self-hosted: Vosk has a websocket server; Whisper can be wrapped for streaming but may have higher latency unless optimized.
Latency considerations:
- Keep audio frames small (e.g., 100 ms).
- Use interim results to show live text; wait for final results for storage/analysis.
- Use lower compression or raw PCM to reduce decoding latency (if bandwidth allows).
Speaker diarization and multi-party calls
For multi-party calls, you’ll want speaker separation (who said what). Options:
- Channel-based diarization: record each participant on a separate channel (RTP allows per-SSRC streams). ASR can be fed per-channel audio so transcripts are naturally separated by channel—this is the most reliable approach.
- Model-based diarization: use ASR or specialized diarization models to detect speaker turns and assign speaker IDs. Cloud providers sometimes offer this; open-source toolkits (e.g., pyannote) provide higher-quality diarization but require more resources.
- Voice activity detection (VAD): segment audio before sending to ASR to detect speech vs. silence, reducing wasted processing and improving turn detection.
When possible, prefer channel-based capture for telephony: it’s simpler and more accurate.
Data flow and queuing
A resilient implementation needs buffering and backpressure control:
- Local audio capture → circular buffer or in-memory queue.
- Worker(s) read frames and push to ASR streaming endpoints.
- If ASR is slow, apply backpressure (drop low-priority frames or reduce frame rate) or scale workers.
- Persist raw audio to disk or object storage as a backup (WAV/FLAC) for reprocessing or compliance.
Use a message broker (RabbitMQ, Kafka, Azure Service Bus) for large-scale deployments to decouple capture from processing.
Security, privacy, and compliance
- Notify callers and capture consent where legally required. Recording laws differ by jurisdiction (one-party vs. two-party consent).
- Encrypt audio and transcripts at rest (AES-256) and in transit (TLS).
- Use role-based access control and audit logs for transcript access.
- Minimize PII collection and redact or obfuscate sensitive fields (credit card numbers, SSNs) using regex or a PII-detection model.
- If using cloud ASR, verify vendor contracts and data residency options. Some providers allow “do not use for training” flags.
Accuracy and model tuning
Ways to improve recognition:
- Use domain-specific language models or custom vocabularies (agent names, product SKUs).
- Provide phrase hints / contextual biasing APIs where supported.
- Preprocess audio: normalize volume, remove DC offset, simple denoising.
- Use multi-pass processing: real-time interim for immediacy, then reprocess with a higher-accuracy batch model (longer context) for final transcripts.
- Train custom acoustic or language models if you control the training data and need domain-level accuracy.
Storage and indexing
- Save raw recordings in a compressed lossless format (FLAC) or WAV for compliance.
- Store transcripts in a structured format (JSON) with timestamps, speaker labels, and confidence scores.
- Index transcripts in a search engine (Elasticsearch, OpenSearch) for fast retrieval and analytics.
- Consider storing metadata: call ID, participants, timestamps, agent ID, sentiment scores.
Example implementation (C# .NET) — simplified
Below is a minimal example showing:
- Capturing audio from a loopback or microphone using NAudio.
- Streaming PCM audio to a hypothetical WebSocket ASR endpoint.
- Receiving and printing transcript messages.
Note: This is illustrative; a production system requires error handling, reconnection, queuing, encryption, and integration with your telephony stack.
// Requires NuGet: NAudio, WebSocketSharp (or System.Net.WebSockets client) using System; using System.Net.WebSockets; using System.Threading; using System.Threading.Tasks; using NAudio.Wave; using System.Text; using System.IO; class RealtimeRecorder { private const int SampleRate = 16000; private const int Channels = 1; private const int BitsPerSample = 16; private WaveInEvent waveIn; private ClientWebSocket ws; public async Task RunAsync(Uri asrWsUri, CancellationToken ct) { ws = new ClientWebSocket(); await ws.ConnectAsync(asrWsUri, ct); waveIn = new WaveInEvent { DeviceNumber = 0, WaveFormat = new WaveFormat(SampleRate, BitsPerSample, Channels), BufferMilliseconds = 100 }; waveIn.DataAvailable += async (s, a) => { // Send raw PCM bytes to ASR via WebSocket // Some ASR endpoints expect base64 or JSON wrapper; adapt as needed. try { var seg = new ArraySegment<byte>(a.Buffer, 0, a.BytesRecorded); await ws.SendAsync(seg, WebSocketMessageType.Binary, true, ct); } catch (Exception ex) { Console.WriteLine("Send failed: " + ex.Message); } }; waveIn.StartRecording(); // Receiving loop var recvBuffer = new byte[8192]; while (ws.State == WebSocketState.Open && !ct.IsCancellationRequested) { var result = await ws.ReceiveAsync(new ArraySegment<byte>(recvBuffer), ct); if (result.MessageType == WebSocketMessageType.Text) { var msg = Encoding.UTF8.GetString(recvBuffer, 0, result.Count); Console.WriteLine("ASR: " + msg); // parse JSON message in real world } else if (result.MessageType == WebSocketMessageType.Close) { await ws.CloseAsync(WebSocketCloseStatus.NormalClosure, "closed", ct); } } waveIn.StopRecording(); waveIn.Dispose(); ws.Dispose(); } } // Usage: // var rec = new RealtimeRecorder(); // await rec.RunAsync(new Uri("wss://your-asr.example/stream"), CancellationToken.None); Handling provider specifics
- Twilio: Twilio’s Media Streams can forward call audio via WebSocket to your app. You’ll receive JSON meta frames plus base64-encoded audio buffers. Decode base64 and forward PCM to your ASR.
- Azure Speech: Use the Azure Speech SDK for C# for simplified streaming. It handles audio chunking and interim/final results and supports custom models.
- Google Cloud: Use the gRPC streaming API (Google.Cloud.Speech.V1) with proper credentials and streaming request types.
- AWS Transcribe: Use the WebSocket-based streaming interface; manage AWS SigV4 signed URLs.
Each provider requires slightly different framing, headers, and auth; read their docs and adapt.
Monitoring, scaling, and testing
- Instrument latency: measure capture → send → ASR → transcript time.
- Monitor dropped frames, reconnections, CPU/memory.
- Load-test with synthetic audio and simulated call volumes.
- Use autoscaling for workers that handle ASR connections; many cloud providers limit concurrent streams per account.
Example production concerns and tips
- Reprocessing: always store raw audio for reprocessing with better models later.
- Cost: streaming ASR costs accumulate; batch reprocessing or selective high-quality reprocessing can save money.
- Error handling: transient network issues are common—reconnect gracefully and resume streams where possible.
- Quality feedback loop: use agent corrections or human review to continuously improve custom vocab and models.
- Latency vs. accuracy tradeoff: choose your balance—interim low-latency with final high-accuracy passes often works best.
Conclusion
Building a C# speech-to-text call recorder involves combining reliable audio capture, low-latency streaming to an ASR backend, robust handling of multi-party calls and storage, and careful attention to security and compliance. Start with a small proof-of-concept using a provider SDK (Azure, Google, AWS, or Twilio) and iterate—add diarization, domain-specific vocabularies, and reprocessing pipelines as you scale. The sample code above gives a starting point; production systems will require more attention to resilience, monitoring, and legal safeguards.
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