Submit and Manage Genesys Cloud Recording Transcriptions via Python SDK

What You Will Build

A production-ready Python module that submits transcription jobs for Genesys Cloud recordings, validates media constraints, processes diarization and confidence scores, tracks latency, generates audit logs, and syncs results via webhooks. This uses the Genesys Cloud Media API and the official genesyscloud Python SDK. The tutorial covers Python 3.9+.

Prerequisites

• OAuth2 Client Credentials flow with scopes: media:recording:read, media:recording:transcribe, webhook:manage
• genesyscloud SDK >= 2.40.0
• Python 3.9+
• External dependencies: pip install httpx pydantic python-dotenv

Authentication Setup

The Genesys Cloud Python SDK handles token caching and automatic refresh when initialized with client credentials. You must configure the environment variables for your OAuth client.

import os
from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2

def init_genesys_client() -> PureCloudPlatformClientV2:
    """Initialize the Genesys Cloud platform client with automatic token refresh."""
    client = PureCloudPlatformClientV2()
    client.set_access_token(os.getenv("GENESYS_ACCESS_TOKEN"))
    return client

For client credentials flow, generate the initial token using the SDK or a separate OAuth script. The SDK maintains the token in memory and refreshes it automatically before expiration.

Implementation

Step 1: Fetch Recording Metadata and Validate Constraints

Before submitting a transcription job, you must verify the recording meets format requirements and duration limits. Genesys Cloud enforces a maximum duration of 240 minutes for transcription jobs. You must also check for corrupted audio markers by comparing declared duration against file size and verifying the recording status.

import httpx
from typing import Dict, Any
from datetime import datetime

MAX_DURATION_MINUTES = 240
SUPPORTED_FORMATS = {"wav", "mp3"}

def validate_recording(client: PureCloudPlatformClientV2, recording_id: str) -> Dict[str, Any]:
    """Fetch recording metadata and validate against transcription constraints."""
    media_api = client.media_api
    response = media_api.get_media_recordings_recording_id(recording_id)
    recording = response.to_dict()

    if recording["status"] != "ready":
        raise ValueError(f"Recording status is {recording['status']}. Must be ready.")

    duration_ms = recording.get("durationMs", 0)
    duration_min = duration_ms / 60000

    if duration_min > MAX_DURATION_MINUTES:
        raise ValueError(f"Duration {duration_min:.2f} min exceeds {MAX_DURATION_MINUTES} min limit.")

    file_format = recording.get("fileFormat", "").lower()
    if file_format not in SUPPORTED_FORMATS:
        raise ValueError(f"Unsupported format: {file_format}. Supported: {SUPPORTED_FORMATS}")

    file_size = recording.get("fileSize", 0)
    if duration_ms > 0 and file_size < (duration_ms * 0.001):
        raise ValueError("Potential corrupted audio: file size is too small for declared duration.")

    return recording

Step 2: Construct Transcription Payload and Submit Atomic POST

The transcription request requires a recordingRef object, a languageCode, and a process directive. You will map a language-matrix to a primary code and fallback list, then submit the payload via an atomic HTTP POST. This ensures format verification and triggers automatic storage upon success.

import json
import time
from httpx import HTTPError

def build_transcription_payload(
    recording_id: str,
    primary_language: str,
    language_matrix: list[str],
    enable_diarization: bool = True,
    confidence_threshold: float = 0.75
) -> Dict[str, Any]:
    """Construct the transcription request body with schema validation."""
    if not primary_language or len(primary_language) < 2:
        raise ValueError("Invalid primary language code.")

    return {
        "recordingRef": {
            "id": recording_id,
            "type": "recording"
        },
        "languageCode": primary_language,
        "process": "start",
        "speakerDiarization": enable_diarization,
        "confidenceThreshold": confidence_threshold,
        "customData": {
            "languageMatrix": language_matrix,
            "submittedAt": datetime.utcnow().isoformat(),
            "auditId": f"tx_{int(time.time())}"
        }
    }

def submit_transcription_job(
    client: PureCloudPlatformClientV2,
    payload: Dict[str, Any],
    base_url: str
) -> Dict[str, Any]:
    """Submit transcription job via atomic HTTP POST with retry logic for 429."""
    token = client.get_access_token()
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }
    url = f"{base_url}/api/v2/media/recording/transcribe"

    max_retries = 3
    for attempt in range(max_retries):
        try:
            with httpx.Client(timeout=30.0) as http_client:
                response = http_client.post(url, headers=headers, json=payload)
                response.raise_for_status()
                return response.json()
        except HTTPError as e:
            if response.status_code == 429 and attempt < max_retries - 1:
                retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                print(f"Rate limited. Retrying in {retry_after}s...")
                time.sleep(retry_after)
            else:
                raise RuntimeError(f"HTTP {response.status_code}: {response.text}") from e

Step 3: Process Results, Diarization, and Confidence Evaluation

After submission, the API returns a transcriptionId. You must poll the result endpoint, extract speaker segments, evaluate confidence scores against your threshold, and trigger an automatic store webhook for external search synchronization.

def evaluate_transcription_result(result: Dict[str, Any], confidence_threshold: float) -> Dict[str, Any]:
    """Process diarization segments and filter by confidence score."""
    segments = result.get("segments", [])
    filtered_segments = []
    total_confidence = 0.0

    for seg in segments:
        conf = seg.get("confidence", 0.0)
        speaker = seg.get("speaker", "unknown")
        text = seg.get("text", "")

        if conf >= confidence_threshold:
            filtered_segments.append({
                "speaker": speaker,
                "text": text,
                "confidence": conf,
                "startMs": seg.get("startMs"),
                "endMs": seg.get("endMs")
            })
            total_confidence += conf

    avg_confidence = total_confidence / len(filtered_segments) if filtered_segments else 0.0
    return {
        "acceptedSegments": filtered_segments,
        "rejectedSegmentsCount": len(segments) - len(filtered_segments),
        "averageConfidence": round(avg_confidence, 4)
    }

def trigger_store_webhook(webhook_url: str, transcription_id: str, processed_data: Dict[str, Any]) -> None:
    """Send completed transcription to external search via webhook."""
    payload = {
        "transcriptionId": transcription_id,
        "status": "completed",
        "processedAt": datetime.utcnow().isoformat(),
        "data": processed_data
    }
    with httpx.Client(timeout=15.0) as client:
        resp = client.post(webhook_url, json=payload, headers={"Content-Type": "application/json"})
        if resp.status_code not in (200, 202):
            print(f"Webhook sync failed: {resp.status_code} {resp.text}")

Step 4: Latency Tracking, Audit Logging, and Process Validation

You must track submission-to-completion latency, log governance data, and expose a unified transcriber class for automated management. The class below orchestrates validation, submission, polling, and auditing.

class RecordingTranscriber:
    def __init__(self, client: PureCloudPlatformClientV2, base_url: str, webhook_url: str):
        self.client = client
        self.base_url = base_url
        self.webhook_url = webhook_url
        self.audit_log = []

    def transcribe(self, recording_id: str, primary_lang: str, lang_matrix: list[str], threshold: float = 0.75) -> Dict[str, Any]:
        start_time = time.time()
        audit_entry = {
            "recordingId": recording_id,
            "action": "transcription_started",
            "timestamp": datetime.utcnow().isoformat(),
            "status": "pending"
        }

        try:
            # Step 1: Validate
            validate_recording(self.client, recording_id)
            audit_entry["status"] = "validated"

            # Step 2: Submit
            payload = build_transcription_payload(recording_id, primary_lang, lang_matrix, confidence_threshold=threshold)
            job = submit_transcription_job(self.client, payload, self.base_url)
            transcription_id = job.get("transcriptionId")
            audit_entry["transcriptionId"] = transcription_id
            audit_entry["status"] = "submitted"

            # Step 3: Poll and Process
            result = self._poll_transcription(transcription_id)
            processed = evaluate_transcription_result(result, threshold)
            latency = time.time() - start_time
            audit_entry["latencyMs"] = round(latency * 1000, 2)
            audit_entry["status"] = "completed"
            audit_entry["success"] = True

            # Step 4: Sync and Store
            trigger_store_webhook(self.webhook_url, transcription_id, processed)
            self.audit_log.append(audit_entry)

            return {
                "transcriptionId": transcription_id,
                "latencyMs": audit_entry["latencyMs"],
                "processedData": processed,
                "auditEntry": audit_entry
            }
        except Exception as e:
            audit_entry["status"] = "failed"
            audit_entry["error"] = str(e)
            audit_entry["success"] = False
            self.audit_log.append(audit_entry)
            raise

    def _poll_transcription(self, transcription_id: str, max_wait: int = 300, interval: int = 5) -> Dict[str, Any]:
        """Poll transcription status until complete or timeout."""
        token = self.client.get_access_token()
        url = f"{self.base_url}/api/v2/media/recording/transcribe/{transcription_id}"
        headers = {"Authorization": f"Bearer {token}", "Accept": "application/json"}

        elapsed = 0
        while elapsed < max_wait:
            with httpx.Client(timeout=15.0) as client:
                resp = client.get(url, headers=headers)
                resp.raise_for_status()
                data = resp.json()

                if data.get("status") in ("completed", "failed"):
                    return data
                time.sleep(interval)
                elapsed += interval
        raise TimeoutError("Transcription job did not complete within timeout.")

Complete Working Example

import os
from dotenv import load_dotenv
from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2

load_dotenv()

def main():
    client = init_genesys_client()
    base_url = os.getenv("GENESYS_ENVIRONMENT", "https://api.mypurecloud.com")
    webhook_url = os.getenv("EXTERNAL_SEARCH_WEBHOOK")
    recording_id = os.getenv("TARGET_RECORDING_ID")

    transcriber = RecordingTranscriber(client, base_url, webhook_url)

    try:
        result = transcriber.transcribe(
            recording_id=recording_id,
            primary_lang="en-US",
            lang_matrix=["en-GB", "en-AU"],
            threshold=0.80
        )
        print("Transcription successful.")
        print(f"Latency: {result['latencyMs']}ms")
        print(f"Average Confidence: {result['processedData']['averageConfidence']}")
        print(f"Accepted Segments: {len(result['processedData']['acceptedSegments'])}")
    except Exception as e:
        print(f"Transcription failed: {e}")
    finally:
        # Export audit logs for media governance
        with open("transcription_audit.json", "w") as f:
            import json
            json.dump(transcriber.audit_log, f, indent=2)
        print("Audit log exported.")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 400 Bad Request

• What causes it: Invalid languageCode, missing recordingRef.id, or process directive mismatch.
• How to fix it: Verify the language code matches BCP 47 standards. Ensure process is exactly "start". Validate the recording exists and is in ready status before submission.
• Code showing the fix: The build_transcription_payload function validates the primary language length and structure. The validate_recording function checks status and format before payload generation.

Error: 429 Too Many Requests

• What causes it: Exceeding Genesys Cloud API rate limits for transcription submissions.
• How to fix it: Implement exponential backoff. The submit_transcription_job function reads the Retry-After header and delays subsequent attempts up to three times.
• Code showing the fix: See the retry loop in submit_transcription_job which catches HTTPError and sleeps based on Retry-After.

Error: 404 Not Found

• What causes it: The recordingId does not exist in the tenant or the user lacks media:recording:read scope.
• How to fix it: Verify the recording ID via the Genesys Cloud admin console or GET /api/v2/media/recordings. Ensure the OAuth token includes media:recording:read.
• Code showing the fix: The validate_recording method fetches the recording first. If the ID is invalid, the SDK raises a 404 exception immediately before payload construction.

Error: 500 Internal Server Error or Corrupted Audio

• What causes it: The media file is malformed, or the transcription engine fails to decode the audio stream.
• How to fix it: Check the fileSize against durationMs. If the file size is disproportionately small, the recording is likely corrupted. Re-download or regenerate the recording.
• Code showing the fix: The validation step compares file_size < (duration_ms * 0.001) to detect truncated files. The audit log records status: failed with the specific error payload.

Official References

• Genesys Cloud Media API Documentation
• POST /api/v2/media/recording/transcribe
• Python SDK Reference
• OAuth2 Client Credentials Flow