Real-Time Media Stream Transcription in Genesys Cloud Using Python

What You Will Build

• You will build a Python service that subscribes to Genesys Cloud media streams, validates transcription constraints, processes audio chunks via WebSocket, synchronizes with external voice processors, and generates audit logs.
• This implementation uses the Genesys Cloud REST API for conversation validation and the Media WebSocket API for real-time streaming.
• The code is written in Python using requests, websocket-client, and pydantic.

Prerequisites

• OAuth 2.0 Client Credentials flow with scopes: conversation:read, user:view, analytics:conversations:view
• Genesys Cloud Python SDK v1.0.0+ (pip install genesys-cloud-python-sdk)
• Python 3.9+ runtime
• External dependencies: requests, websocket-client, pydantic, numpy, httpx

Authentication Setup

Genesys Cloud requires OAuth 2.0 bearer tokens for all REST API calls. The Media WebSocket endpoint inherits authentication from the initial handshake or uses a pre-authenticated session. The following code retrieves and caches the token with automatic refresh logic.

import requests
import time
from typing import Optional
import logging

logger = logging.getLogger(__name__)

class GenesysAuthManager:
    def __init__(self, client_id: str, client_secret: str, region: str = "us-east-1"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.region = region
        self.base_url = f"https://api.{region}.mypurecloud.com"
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_token(self) -> str:
        if self.token and time.time() < self.token_expiry:
            return self.token

        payload = {
            "grant_type": "client_credentials",
            "scope": "conversation:read user:view analytics:conversations:view"
        }
        
        try:
            response = requests.post(
                f"{self.base_url}/oauth/token",
                auth=(self.client_id, self.client_secret),
                data=payload,
                timeout=10
            )
            response.raise_for_status()
            data = response.json()
            self.token = data["access_token"]
            self.token_expiry = time.time() + data["expires_in"] - 60
            logger.info("OAuth token refreshed successfully.")
            return self.token
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 429:
                logger.warning("Rate limit (429) hit during token refresh. Retrying in 5 seconds.")
                time.sleep(5)
                return self.get_token()
            logger.error(f"OAuth token retrieval failed: {e}")
            raise
        except requests.exceptions.RequestException as e:
            logger.error(f"Network error during OAuth flow: {e}")
            raise

Implementation

Step 1: Validate Conversation Constraints and Stream Configuration

Before opening a WebSocket stream, you must verify that the conversation supports real-time media extraction and that your buffer configuration aligns with Genesys Cloud limits. The Conversation API returns metadata that dictates chunking behavior.

import requests
from pydantic import BaseModel, Field, validator
from typing import Dict, Any

class StreamConstraints(BaseModel):
    maximum_stream_buffer_seconds: float = Field(..., gt=0, le=30.0)
    conversation_constraints: Dict[str, Any] = Field(default_factory=dict)
    media_ref: str = Field(..., description="Internal media reference identifier")
    conversation_matrix: Dict[str, Any] = Field(default_factory=dict)
    stream_directive: str = Field(..., pattern="^(subscribe|publish|bidirectional)$")

    @validator("maximum_stream_buffer_seconds")
    def validate_buffer_limit(cls, v):
        if v > 25.0:
            raise ValueError("Genesys Cloud recommends buffer limits under 25 seconds for real-time transcription.")
        return v

def validate_conversation_stream(conversation_id: str, auth: GenesysAuthManager) -> StreamConstraints:
    url = f"https://api.{auth.region}.mypurecloud.com/api/v2/conversations/{conversation_id}"
    headers = {"Authorization": f"Bearer {auth.get_token()}", "Content-Type": "application/json"}
    
    response = requests.get(url, headers=headers, timeout=10)
    response.raise_for_status()
    conv_data = response.json()

    constraints = StreamConstraints(
        maximum_stream_buffer_seconds=20.0,
        conversation_constraints={
            "media_type": conv_data.get("type", "voice"),
            "direction": conv_data.get("direction", "inbound"),
            "participants": len(conv_data.get("participants", []))
        },
        media_ref=conv_data.get("id", ""),
        conversation_matrix={"routing": conv_data.get("routing", {}), "state": conv_data.get("state", "")},
        stream_directive="subscribe"
    )
    
    logger.info(f"Conversation {conversation_id} validated. Buffer limit: {constraints.maximum_stream_buffer_seconds}s")
    return constraints

Step 2: Initialize WebSocket Connection and Subscribe to Media Stream

Genesys Cloud media streaming operates over a persistent WebSocket connection. You must send a structured subscription payload containing the media-ref, conversation-matrix, and stream directive. The connection requires atomic framing to prevent partial message delivery.

import websocket
import json
import threading

def init_media_websocket(conversation_id: str, constraints: StreamConstraints, auth: GenesysAuthManager):
    ws_url = f"wss://media.{auth.region}.mypurecloud.com/media/ws?conversationId={conversation_id}"
    
    ws = websocket.WebSocket()
    
    def on_open(ws_app):
        subscribe_payload = {
            "type": "subscribe",
            "mediaRef": constraints.media_ref,
            "conversationMatrix": constraints.conversation_matrix,
            "streamDirective": constraints.stream_directive,
            "format": "opus",
            "sampleRate": 16000,
            "channels": 1
        }
        ws_app.send(json.dumps(subscribe_payload))
        logger.info("WebSocket opened and subscription sent.")

    def on_error(ws_app, error):
        logger.error(f"WebSocket error: {error}")

    def on_close(ws_app, close_status_code, close_msg):
        logger.warning(f"WebSocket closed: {close_status_code} {close_msg}")

    ws.on_open = on_open
    ws.on_error = on_error
    ws.on_close = on_close
    
    try:
        ws.connect(ws_url)
        logger.info("WebSocket connection established.")
        return ws
    except websocket.WebSocketException as e:
        logger.error(f"Failed to connect to media stream: {e}")
        raise

Step 3: Process Audio Chunks, Handle Codec Conversion, and Validate Stream Integrity

Real-time transcription requires strict frame validation. You must track sequence numbers to detect dropped frames, calculate synchronization drift against wall-clock time, and evaluate codec conversion requirements. The following loop processes incoming WebSocket frames atomically.

import numpy as np
import struct
from datetime import datetime
from typing import List, Tuple

class StreamValidator:
    def __init__(self, max_buffer_seconds: float):
        self.max_buffer_seconds = max_buffer_seconds
        self.last_sequence: int = 0
        self.last_timestamp: float = 0.0
        self.dropped_frames: int = 0
        self.sync_drift_ms: float = 0.0
        self.frame_buffer: List[bytes] = []

    def validate_and_buffer_frame(self, payload: dict) -> Tuple[bool, bytes]:
        seq = payload.get("sequence", 0)
        timestamp = payload.get("timestamp", 0)
        audio_data = payload.get("audioData", "")
        
        if not audio_data:
            return False, b""

        # Dropped frame detection
        if self.last_sequence > 0 and seq != self.last_sequence + 1:
            self.dropped_frames += (seq - self.last_sequence - 1)
            logger.warning(f"Dropped {seq - self.last_sequence - 1} frames. Current seq: {seq}")

        # Synchronization drift calculation
        current_time = time.time()
        if self.last_timestamp > 0:
            drift = (current_time - timestamp) * 1000
            self.sync_drift_ms = drift
            if abs(drift) > self.max_buffer_seconds * 1000:
                logger.error(f"Sync drift exceeded buffer limit: {drift:.2f}ms")
                return False, b""

        self.last_sequence = seq
        self.last_timestamp = timestamp
        self.frame_buffer.append(bytes.fromhex(audio_data))

        # Automatic stream trigger when buffer reaches threshold
        buffer_duration = len(self.frame_buffer) * 0.02  # Assuming 20ms frames
        if buffer_duration >= self.max_buffer_seconds:
            chunk = b"".join(self.frame_buffer[:int(self.max_buffer_seconds / 0.02)])
            self.frame_buffer = self.frame_buffer[int(self.max_buffer_seconds / 0.02):]
            return True, chunk

        return False, b""

    def evaluate_codec_conversion(self, audio_bytes: bytes) -> bytes:
        # Genesys Cloud streams Opus by default. Transcription engines often require PCM 16-bit LE.
        # This placeholder simulates atomic codec evaluation. In production, use pyopus or ffmpeg-python.
        if len(audio_bytes) < 4:
            return b""
        # Verify Opus header signature
        if audio_bytes[:2] == b"\x01\x02":
            logger.info("Opus format verified. Proceeding with passthrough or conversion.")
        return audio_bytes

Step 4: Synchronize with External Processor and Track Latency

Transcription events must align with an external voice processor via webhooks. You must track latency, success rates, and generate audit logs for conversation governance. The following class exposes the media transcriber interface.

import httpx
import logging
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class TranscriptionMetrics:
    total_frames_processed: int = 0
    successful_transcriptions: int = 0
    failed_transcriptions: int = 0
    total_latency_ms: float = 0.0
    audit_log: List[dict] = field(default_factory=list)

class ExternalVoiceProcessor:
    def __init__(self, webhook_url: str):
        self.webhook_url = webhook_url
        self.client = httpx.Client(timeout=5.0)

    def sync_audio_chunk(self, audio_bytes: bytes, conversation_id: str, seq: int) -> dict:
        start_time = time.time()
        payload = {
            "conversationId": conversation_id,
            "sequence": seq,
            "audioPayload": audio_bytes.hex(),
            "timestamp": time.time(),
            "format": "opus"
        }
        
        try:
            response = self.client.post(self.webhook_url, json=payload)
            response.raise_for_status()
            latency = (time.time() - start_time) * 1000
            return {"status": "success", "latency_ms": latency, "data": response.json()}
        except httpx.HTTPStatusError as e:
            logger.error(f"External processor sync failed: {e.response.status_code}")
            return {"status": "error", "latency_ms": (time.time() - start_time) * 1000, "data": {}}
        except httpx.RequestError as e:
            logger.error(f"Network error syncing to external processor: {e}")
            return {"status": "error", "latency_ms": 0, "data": {}}

class MediaTranscriber:
    def __init__(self, conversation_id: str, auth: GenesysAuthManager, webhook_url: str):
        self.conversation_id = conversation_id
        self.auth = auth
        self.validator = StreamValidator(20.0)
        self.processor = ExternalVoiceProcessor(webhook_url)
        self.metrics = TranscriptionMetrics()
        self.ws = None

    def run_stream(self):
        constraints = validate_conversation_stream(self.conversation_id, self.auth)
        self.ws = init_media_websocket(self.conversation_id, constraints, self.auth)
        
        logger.info("Starting real-time transcription stream...")
        try:
            while self.ws.connected:
                raw_msg = self.ws.recv()
                try:
                    frame = json.loads(raw_msg)
                    is_chunk_ready, audio_bytes = self.validator.validate_and_buffer_frame(frame)
                    
                    if is_chunk_ready:
                        converted_audio = self.validator.evaluate_codec_conversion(audio_bytes)
                        result = self.processor.sync_audio_chunk(converted_audio, self.conversation_id, self.validator.last_sequence)
                        
                        self.metrics.total_frames_processed += len(audio_bytes) // 320
                        if result["status"] == "success":
                            self.metrics.successful_transcriptions += 1
                            self.metrics.total_latency_ms += result["latency_ms"]
                        else:
                            self.metrics.failed_transcriptions += 1
                        
                        self.metrics.audit_log.append({
                            "timestamp": datetime.utcnow().isoformat(),
                            "conversationId": self.conversation_id,
                            "sequence": self.validator.last_sequence,
                            "droppedFrames": self.validator.dropped_frames,
                            "syncDriftMs": self.validator.sync_drift_ms,
                            "status": result["status"]
                        })
                        
                except json.JSONDecodeError:
                    logger.warning("Received non-JSON WebSocket frame. Skipping.")
                except Exception as e:
                    logger.error(f"Stream processing error: {e}")
                    
        except websocket.WebSocketConnectionClosedException:
            logger.info("WebSocket connection closed gracefully.")
        finally:
            self.ws.close()
            self._write_audit_log()
            self._print_metrics()

    def _write_audit_log(self):
        with open("transcription_audit.log", "w") as f:
            for entry in self.metrics.audit_log:
                f.write(json.dumps(entry) + "\n")
        logger.info("Audit log written to transcription_audit.log")

    def _print_metrics(self):
        avg_latency = self.metrics.total_latency_ms / max(1, self.metrics.successful_transcriptions)
        success_rate = (self.metrics.successful_transcriptions / max(1, self.metrics.total_frames_processed)) * 100
        logger.info(f"Stream Complete. Success Rate: {success_rate:.2f}%, Avg Latency: {avg_latency:.2f}ms")

Complete Working Example

The following script combines authentication, validation, WebSocket streaming, external synchronization, and metrics tracking into a single executable module. Replace the placeholder credentials and webhook URL before execution.

import logging
import time
import sys

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")

def main():
    # Configuration
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    REGION = "us-east-1"
    CONVERSATION_ID = "active_conversation_uuid_here"
    WEBHOOK_URL = "https://your-external-processor.com/api/voice/sync"

    auth = GenesysAuthManager(client_id=CLIENT_ID, client_secret=CLIENT_SECRET, region=REGION)
    transcriber = MediaTranscriber(
        conversation_id=CONVERSATION_ID,
        auth=auth,
        webhook_url=WEBHOOK_URL
    )

    try:
        transcriber.run_stream()
    except KeyboardInterrupt:
        logger.info("Stream interrupted by user.")
    except Exception as e:
        logger.error(f"Fatal error in transcription pipeline: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: OAuth token expired, invalid client credentials, or missing required scopes.
• Fix: Verify client_id and client_secret in the Genesys Cloud admin console. Ensure the token endpoint returns a valid access_token. The GenesysAuthManager automatically refreshes tokens before expiry.
• Code Fix: Check the get_token method response. If the scope list is incomplete, append conversation:read and retry.

Error: 429 Too Many Requests

• Cause: Exceeding REST API rate limits during conversation validation or token refresh.
• Fix: Implement exponential backoff. The get_token method includes a 5-second retry for 429 responses. For conversation polling, add a delay between requests.
• Code Fix: Wrap REST calls in a retry decorator with time.sleep(min(2**attempt, 30)).

Error: WebSocket 1006 Abnormal Closure

• Cause: Network interruption, Genesys Cloud media server scaling event, or malformed subscription payload.
• Fix: Validate the media-ref and stream directive against the conversation state. Ensure the WebSocket client handles ping/pong frames.
• Code Fix: Add ws.settimeout(30) and implement a reconnection loop in run_stream that catches WebSocketConnectionClosedException.

Error: Synchronization Drift Exceeds Buffer Limit

• Cause: Clock skew between the Genesys Cloud media server and the local processor, or high network jitter.
• Fix: Adjust maximum_stream_buffer_seconds to accommodate network variance. The StreamValidator rejects frames when drift exceeds the configured threshold.
• Code Fix: Increase max_buffer_seconds in StreamValidator initialization or implement NTP synchronization on the host machine.

Error: Pydantic ValidationError on Conversation Constraints

• Cause: Conversation does not support real-time media extraction or contains invalid routing state.
• Fix: Verify the conversation is in active or monitoring state. Ensure the media type matches voice.
• Code Fix: Catch pydantic.ValidationError in validate_conversation_stream and log the specific field mismatch.

Official References

• Genesys Cloud Conversation API Documentation
• Genesys Cloud Media WebSocket Protocol
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow