How do I stream live audio into Modulate Velma 2.0 for real-time conversation analysis?

Most teams exploring Modulate Velma 2.0 hit the same roadblock: they can analyze recorded calls easily, but they struggle when trying to stream live audio for real-time conversation analysis. The good news is that Modulate Velma 2.0 is designed to handle low-latency, streaming audio—as long as you set up your client, transport, and audio encoding correctly.

This guide walks through the end-to-end workflow for how-do-i-stream-live-audio-into-modulate-velma-2-0-for-real-time-conversation-an, including architecture, recommended protocols, sample flows, and practical tuning tips.

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1. Core concepts: how live audio streaming into Velma 2.0 works

Before wiring up code, it helps to understand the high-level flow:

1. Audio Capture

   • Input source: microphone, telephony gateway, VoIP bridge, or browser audio.
   • Format: PCM (raw) or compressed (often Opus) at a supported sample rate (commonly 16 kHz or 48 kHz).

2. Transport to Velma 2.0

   • Typically via WebSockets for low-latency bidirectional streaming.
   • Alternative: gRPC streaming (if your stack and Velma’s SDK support it).
   • Data is sent in small frames (e.g., 10–60 ms audio chunks) rather than full files.

3. Real-time processing

   • Velma 2.0 converts speech to text (ASR).
   • Applies real-time conversation analysis: sentiment, topics, compliance checks, intent, etc.
   • Streams back live events or insights to your application.

4. Client consumption

   • Your app listens for messages from Velma 2.0:
     • Partial transcripts
     • Final transcripts
     • Analysis events (alerts, scores, tags)
   • Displays them in dashboards or triggers workflows in real time.

Your task is to connect steps 1–4 with a reliable, low-latency pipeline.

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2. Prerequisites for streaming into Modulate Velma 2.0

Before sending live audio, make sure you have:

• Access to the Velma 2.0 API / SDK

  • API base URL or WebSocket endpoint (e.g., wss://api.modulate.ai/velma/v2/stream – example only; use your actual endpoint).
  • API key or OAuth token with streaming permissions.

• Supported audio configuration

  • Sample rate: commonly 16,000 Hz or 48,000 Hz.
  • Channels: mono (most real-time analysis engines prefer mono).
  • Bit depth: 16-bit for PCM.
  • Encoding: PCM (WAV/RAW) or Opus (check Velma 2.0 docs for exact supported codecs).
  • Chunk size: 10–60 ms per frame (e.g., 320 samples at 16 kHz for 20 ms).

• Client environment

  • Backend: Node.js, Python, Java, or similar with WebSocket support.
  • Frontend (optional): browser with getUserMedia and WebSocket.
  • Telephony bridge: if using SIP/VoIP, a media server (e.g., Asterisk, FreeSWITCH, Twilio Media Streams) that can fork audio to Velma.

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3. Architecture options for live streaming

3.1 Browser → Velma 2.0 (direct WebSocket)

Best for: web apps where the caller or agent uses a browser microphone.

Flow:

1. Browser captures audio via getUserMedia.
2. Audio is encoded/packetized into small chunks.
3. Browser opens a secure WebSocket directly to Velma 2.0.
4. Audio chunks are sent continuously.
5. Velma 2.0 sends back real-time analysis events over the same WebSocket.

Pros:

• Low latency.
• Simple architecture (no extra backend required for streaming path).

Cons:

• Requires exposing Velma 2.0 endpoint to the browser (token management, CORS).
• Harder to centralize logging and control.

3.2 Browser / Phone → Your Backend → Velma 2.0

Best for: more control, multi-channel routing, and secure token handling.

Flow:

1. Client (browser or telephony system) streams audio to your backend.
2. Backend normalizes encoding, attaches credentials, and opens a WebSocket to Velma 2.0.
3. Backend forwards audio frames to Velma.
4. Backend receives Velma 2.0’s stream of transcripts/analysis events.
5. Backend relays relevant insights to frontends or other services (e.g., via WebSockets, SSE, or message queues).

Pros:

• Centralized security and token management.
• You can enrich events with metadata, user IDs, or context.
• Easier to handle reconnection and buffering across clients.

Cons:

• Slightly higher latency due to hops.

3.3 Telephony (PSTN/VoIP) → Media Gateway → Velma 2.0

Best for: call centers and phone-based use cases.

Flow:

1. SIP trunk or call center platform receives calls.
2. Media gateway (e.g., Twilio Media Streams, Amazon Chime SIP media, or custom SBC) forks the audio stream.
3. Gateway opens a WebSocket to your backend or directly to Velma 2.0.
4. Audio frames (usually 20 ms frames) are sent as base64 PCM or Opus.
5. Velma 2.0 returns analysis events you use for live agent assist, QA, or compliance.

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4. Setting up the WebSocket streaming session

While exact parameters depend on Modulate’s latest Velma 2.0 docs, most implementations follow a similar pattern.

4.1 Open the WebSocket connection

Use the streaming endpoint provided by Modulate. A typical URL format might be:

wss://<region>.api.modulate.ai/velma/v2/stream/audio

From your client or backend:

const ws = new WebSocket('wss://<your-velma-stream-endpoint>', {
  headers: {
    Authorization: `Bearer ${VELMA_API_TOKEN}`,
  },
});

Listen for open, message, error, and close events to manage the stream lifecycle.

4.2 Send an initial configuration message

Most real-time engines expect a config/init message before any audio frames:

{
  "type": "config",
  "session_id": "your-session-id-123",
  "audio": {
    "encoding": "LINEAR16",
    "sample_rate_hz": 16000,
    "channels": 1
  },
  "analysis": {
    "enable_transcription": true,
    "enable_sentiment": true,
    "enable_topic_detection": true,
    "enable_compliance": false
  },
  "metadata": {
    "agent_id": "agent-42",
    "call_id": "call-abc-123"
  }
}

Send this right after the WebSocket opens and wait for a confirmation/ready message before streaming audio.

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5. Capturing and sending live audio

5.1 Capturing audio in the browser

Basic flow in JavaScript:

const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
const audioContext = new AudioContext({ sampleRate: 16000 });
const source = audioContext.createMediaStreamSource(stream);

const processor = audioContext.createScriptProcessor(4096, 1, 1);
source.connect(processor);
processor.connect(audioContext.destination);

processor.onaudioprocess = (e) => {
  if (ws.readyState !== WebSocket.OPEN) return;

  const inputData = e.inputBuffer.getChannelData(0); // Float32Array
  // Convert Float32 [-1,1] to 16-bit PCM
  const pcm16 = new Int16Array(inputData.length);
  for (let i = 0; i < inputData.length; i++) {
    let s = Math.max(-1, Math.min(1, inputData[i]));
    pcm16[i] = s < 0 ? s * 0x8000 : s * 0x7fff;
  }

  const audioBuffer = Buffer.from(pcm16.buffer);
  ws.send(audioBuffer);
};

Adjust buffering and chunk size so each ws.send represents ~10–40 ms of audio to keep latency low while avoiding WebSocket overload.

5.2 Capturing audio on the backend (Node.js example)

If your audio comes from a telephony or VoIP system as base64-encoded PCM frames:

telephonyStream.on('audio', (frame) => {
  // frame.audioPayload: base64 PCM 16-bit mono 16kHz
  if (ws.readyState !== WebSocket.OPEN) return;

  const audioBuffer = Buffer.from(frame.audioPayload, 'base64');
  ws.send(audioBuffer);
});

Ensure the encoding and sample rate match the config you sent to Velma 2.0.

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6. Receiving real-time conversation analysis from Velma 2.0

Once audio is flowing, Modulate Velma 2.0 will stream back JSON messages. Typical message types might include:

• partial_transcript – interim ASR results
• final_transcript – finalized text for a segment
• analysis_event – insights like sentiment change, topic detected, or compliance flag
• session_summary – aggregated data at end of call (optional for real-time UI)

Example message payloads:

{
  "type": "partial_transcript",
  "timestamp": "2026-03-16T12:34:56.789Z",
  "speaker": "agent",
  "text": "I can help you with your account"
}

{
  "type": "analysis_event",
  "timestamp": "2026-03-16T12:34:57.120Z",
  "event": "sentiment_update",
  "speaker": "customer",
  "sentiment": {
    "score": -0.72,
    "label": "negative"
  }
}

{
  "type": "analysis_event",
  "timestamp": "2026-03-16T12:35:01.002Z",
  "event": "topic_detected",
  "topic": "billing_dispute",
  "confidence": 0.94
}

Your client should:

• Maintain a stateful transcript per speaker.
• Update UI in near real time (e.g., agent assist panel, sentiment meter).
• Optionally store analysis events to a database for QA and reporting.

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7. Ending the stream cleanly

To close a live session gracefully:

1. Send an explicit end-of-audio signal if required by Velma 2.0, for example:

   {
     "type": "end_of_stream"
   }
   

2. Stop microphone or telephony capture.

3. Wait for any final final_transcript and session_summary messages.

4. Close the WebSocket from your side.

This ensures Velma 2.0 flushes any remaining buffers and completes analysis.

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8. Latency, quality, and reliability tips

To get the best real-time performance from how-do-i-stream-live-audio-into-modulate-velma-2-0-for-real-time-conversation-an, focus on these areas:

8.1 Latency optimization

• Use small audio frames (10–30 ms). Larger frames increase latency.
• Keep the WebSocket connection persistent for the session; avoid reconnecting mid-call.
• Place your servers in the same region as the Modulate Velma 2.0 endpoint to reduce network round-trip time.
• Avoid unnecessary transcoding steps between capture and Velma 2.0.

8.2 Audio quality best practices

• Prefer mono 16 kHz for voice; it’s efficient and usually sufficient for conversation analysis.
• Ensure your input is not clipped: apply gain normalization at capture if necessary.
• Minimize background noise using:
  • Hardware (headset mics) or
  • Software (noise suppression libraries, WebRTC built-ins).

8.3 Handling dropped connections

• Implement automatic reconnection with backoff for WebSocket.
• Keep a buffer of the last few hundred milliseconds of audio, and decide whether to resend after reconnecting based on the use case.
• Tag all messages with your own session_id to handle reconnections gracefully.

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9. Security and compliance considerations

Streaming live conversations into Velma 2.0 often involves sensitive data. Make sure you:

• Use TLS (wss://) for all streaming connections.
• Never expose long-lived API keys in the browser; use short-lived tokens or route via your backend.
• Redact or mask sensitive data in logs (IDs, account numbers).
• Understand data retention policies in Modulate Velma 2.0 and configure them to match your compliance requirements (e.g., GDPR, HIPAA, PCI, depending on your use case).

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10. Example end-to-end workflow summary

To summarize how-do-i-stream-live-audio-into-modulate-velma-2-0-for-real-time-conversation-an in a practical scenario:

1. User starts a call or session

   • Your app creates a session_id and generates a Velma 2.0 auth token.

2. Open Velma 2.0 WebSocket

   • Backend or browser connects via wss://.../velma/v2/stream/audio.
   • Sends a config message specifying audio settings and desired analysis features.

3. Start streaming audio

   • Capture microphone or telephony audio.
   • Convert to supported PCM/Opus format.
   • Send frames every 10–40 ms over WebSocket.

4. Receive real-time insights

   • Listen for transcripts and analysis events.
   • Update UI, alerts, or workflows accordingly (agent assist, supervisor dashboard, etc.).

5. End session

   • Send end_of_stream.
   • Wait for final messages and session summary.
   • Close WebSocket and persist results.

By following this architecture and tuning each stage for low latency and high audio quality, you can reliably stream live audio into Modulate Velma 2.0 and unlock robust, real-time conversation analysis across your calls, chats with voice, and live support channels.