Retrieving Full Voice Conversation Transcripts via Genesys Cloud Speech Analytics

What You Will Build

• You will build a script that retrieves the complete text transcript for a specific voice conversation, including speaker identification and timestamps.
• This tutorial uses the Genesys Cloud PureCloud Platform API, specifically the Analytics Conversations Details endpoint.
• The implementation is provided in Python using the official genesyscloud SDK and httpx for raw API verification.

Prerequisites

To execute this tutorial, you must have the following resources configured:

• Genesys Cloud Organization: An active Genesys Cloud organization with Speech and Text Analytics enabled.
• OAuth 2.0 Service Account: A Genesys Cloud Service Account (API Key) with the following scopes:
  • analytics:conversation:read
  • speech:analytics:read (Required if accessing specific speech insights, though transcript often lives under analytics)
  • conversation:read
• Python Environment: Python 3.8 or higher.
• Dependencies:
  • genesyscloud (Official Python SDK)
  • httpx (For asynchronous HTTP requests in raw examples)
  • python-dotenv (For secure credential management)

Install the required packages:

pip install genesyscloud httpx python-dotenv

Authentication Setup

Genesys Cloud uses OAuth 2.0 for authentication. The most robust method for server-to-server integrations is the Client Credentials flow. You should never hardcode credentials. Use environment variables or a secrets manager.

Create a .env file in your project root:

GENESYS_CLOUD_REGION=us-east-1
GENESYS_CLOUD_CLIENT_ID=your_client_id
GENESYS_CLOUD_CLIENT_SECRET=your_client_secret

The official Python SDK handles token acquisition and refreshing automatically. Below is the initialization pattern.

import os
from dotenv import load_dotenv
from purecloudplatformclientv2 import Configuration, ApiClient

# Load environment variables
load_dotenv()

def get_platform_client() -> ApiClient:
    """
    Initializes and returns a configured PureCloud API Client.
    Handles OAuth token acquisition via Client Credentials flow.
    """
    region = os.getenv("GENESYS_CLOUD_REGION", "us-east-1")
    client_id = os.getenv("GENESYS_CLOUD_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLOUD_CLIENT_SECRET")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET must be set.")

    # Configure the SDK
    configuration = Configuration(
        host=f"https://api.{region}.mypurecloud.com",
        client_id=client_id,
        client_secret=client_secret
    )

    # The ApiClient manages the OAuth token lifecycle
    api_client = ApiClient(configuration=configuration)
    return api_client

Implementation

Retrieving a transcript is not a single “get transcript” call. The Genesys Cloud Analytics API separates the metadata of a conversation from its detailed content. To get the transcript, you must:

1. Query the Analytics API for the conversation details using the Conversation ID.
2. Parse the response to locate the text or speech channels.
3. Extract the transcript segments from the channel data.

Step 1: Query Conversation Details by Conversation ID

The primary endpoint for retrieving conversation data is POST /api/v2/analytics/conversations/details/query. Unlike a simple GET, this endpoint uses a request body to define the scope of the query. This allows you to fetch multiple conversations or apply filters, but for a single transcript, you pass the specific conversationId.

Required Scope: analytics:conversation:read

import json
from purecloudplatformclientv2 import AnalyticsApi, ConversationDetailsQueryRequest

def get_conversation_details(api_client: ApiClient, conversation_id: str) -> dict:
    """
    Retrieves detailed conversation data including transcript segments.
    
    Args:
        api_client: The initialized PureCloud API Client.
        conversation_id: The UUID of the conversation to retrieve.
        
    Returns:
        A dictionary containing the conversation details response.
    """
    analytics_api = AnalyticsApi(api_client)
    
    # Construct the query request
    # We filter by the specific conversation ID to ensure we only get that record
    query_body = ConversationDetailsQueryRequest(
        conversation_ids=[conversation_id]
    )
    
    try:
        # Execute the query
        response = analytics_api.post_analytics_conversations_details_query(body=query_body)
        
        # The response is a wrapper object. We need to inspect the 'conversations' list.
        if not response.conversations:
            print(f"No conversation found with ID: {conversation_id}")
            return {}
            
        return response.conversations[0]
        
    except Exception as e:
        # Handle specific HTTP errors
        status_code = getattr(e, 'status', None)
        if status_code == 401:
            raise Exception("Authentication failed. Check your OAuth token.")
        elif status_code == 403:
            raise Exception("Forbidden. Check if your API key has 'analytics:conversation:read' scope.")
        elif status_code == 404:
            raise Exception(f"Conversation {conversation_id} not found.")
        else:
            raise Exception(f"API Error: {str(e)}")

Step 2: Extract Transcript Segments from Channels

The response from the Analytics API is complex. A voice conversation contains multiple “channels” (e.g., the audio stream, the transcription stream). The transcript itself resides in the channel that has a type of transcription or is embedded within the voice channel if post-call transcription has completed.

In modern Genesys Cloud implementations, the transcript segments are typically found in a channel with type equal to transcription or sometimes nested within the voice channel under a transcript field depending on the specific analytics configuration. However, the most reliable method for voice-to-text is looking for the channel where type is transcription and transcriptType is voice.

Here is how to parse the nested structure safely.

from typing import List, Dict, Any

def extract_transcript_segments(conversation_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Parses the raw conversation data to find and extract transcript segments.
    
    Args:
        conversation_data: The dictionary returned from get_conversation_details.
        
    Returns:
        A list of dictionaries, each representing a transcript segment with:
        - speaker: The identifier of the speaker (e.g., 'agent', 'customer', or user ID)
        - text: The transcribed text
        - start_time: The timestamp of the segment
        - end_time: The timestamp of the segment
    """
    transcripts = []
    
    # The conversation object has a 'channels' list
    channels = conversation_data.get('channels', [])
    
    for channel in channels:
        # We are looking for transcription channels
        # Note: In some configurations, 'type' might be 'voice' and contain a 'transcript' sub-object.
        # In others, there is a distinct 'transcription' channel.
        
        channel_type = channel.get('type')
        
        # Case 1: Dedicated Transcription Channel
        if channel_type == 'transcription':
            transcript_type = channel.get('transcriptType')
            if transcript_type == 'voice':
                # Segments are usually in the 'segments' list
                segments = channel.get('segments', [])
                for segment in segments:
                    transcripts.append({
                        'speaker': segment.get('speaker', {}).get('id', 'Unknown'),
                        'speaker_name': segment.get('speaker', {}).get('name', 'Unknown'),
                        'text': segment.get('text', ''),
                        'start_time': segment.get('startTime'),
                        'end_time': segment.get('endTime'),
                        'confidence': segment.get('confidence')
                    })
                    
        # Case 2: Voice Channel with embedded transcript (Legacy or specific config)
        elif channel_type == 'voice':
            # Check if there is a transcript object embedded
            transcript_obj = channel.get('transcript')
            if transcript_obj:
                segments = transcript_obj.get('segments', [])
                for segment in segments:
                    transcripts.append({
                        'speaker': segment.get('speaker', {}).get('id', 'Unknown'),
                        'speaker_name': segment.get('speaker', {}).get('name', 'Unknown'),
                        'text': segment.get('text', ''),
                        'start_time': segment.get('startTime'),
                        'end_time': segment.get('endTime'),
                        'confidence': segment.get('confidence')
                    })
                    
    # Sort by start time to ensure chronological order
    transcripts.sort(key=lambda x: x['start_time'] if x['start_time'] else '')
    
    return transcripts

Step 3: Handling Asynchronous Transcription Status

Voice transcription is an asynchronous process. If you query a conversation that just ended, the transcript may not be available yet. The API will return the conversation metadata, but the transcription channel may be missing or empty.

You should implement a check for the transcription status.

def check_transcription_status(conversation_data: Dict[str, Any]) -> bool:
    """
    Checks if the voice transcription for the conversation is complete.
    
    Returns:
        True if transcription is complete, False otherwise.
    """
    channels = conversation_data.get('channels', [])
    
    for channel in channels:
        if channel.get('type') == 'transcription' and channel.get('transcriptType') == 'voice':
            # If the channel exists and has segments, it is likely complete
            segments = channel.get('segments', [])
            if len(segments) > 0:
                return True
            # Check for a specific status field if present in your org's config
            if channel.get('status') == 'completed':
                return True
                
    return False

Complete Working Example

This script combines the authentication, querying, parsing, and status checking into a single runnable module. It includes a retry mechanism for pending transcriptions.

import os
import time
import sys
from dotenv import load_dotenv
from purecloudplatformclientv2 import Configuration, ApiClient, AnalyticsApi, ConversationDetailsQueryRequest
from typing import List, Dict, Any, Optional

# Load environment variables
load_dotenv()

def get_platform_client() -> ApiClient:
    region = os.getenv("GENESYS_CLOUD_REGION", "us-east-1")
    client_id = os.getenv("GENESYS_CLOUD_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLOUD_CLIENT_SECRET")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET must be set.")

    configuration = Configuration(
        host=f"https://api.{region}.mypurecloud.com",
        client_id=client_id,
        client_secret=client_secret
    )
    return ApiClient(configuration=configuration)

def get_conversation_details(api_client: ApiClient, conversation_id: str) -> Optional[Dict[str, Any]]:
    analytics_api = AnalyticsApi(api_client)
    
    query_body = ConversationDetailsQueryRequest(
        conversation_ids=[conversation_id]
    )
    
    try:
        response = analytics_api.post_analytics_conversations_details_query(body=query_body)
        
        if not response.conversations:
            return None
            
        # Convert the SDK object to a dictionary for easier parsing
        # In production, use a proper serializer or access properties directly
        # Here we use the SDK's built-in conversion if available, or manual mapping
        # For this example, we assume the SDK object has a .to_dict() method or similar
        # The Genesys Cloud Python SDK objects are dataclasses, so we can use asdict()
        from dataclasses import asdict
        return asdict(response.conversations[0])
        
    except Exception as e:
        status_code = getattr(e, 'status', None)
        if status_code == 401:
            raise Exception("Authentication failed.")
        elif status_code == 403:
            raise Exception("Forbidden. Check scopes.")
        elif status_code == 404:
            return None
        else:
            raise e

def extract_transcript_segments(conversation_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    transcripts = []
    channels = conversation_data.get('channels', [])
    
    for channel in channels:
        channel_type = channel.get('type')
        
        # Look for transcription channel
        if channel_type == 'transcription' and channel.get('transcriptType') == 'voice':
            segments = channel.get('segments', [])
            for segment in segments:
                speaker_info = segment.get('speaker', {})
                transcripts.append({
                    'speaker_id': speaker_info.get('id', 'Unknown'),
                    'speaker_name': speaker_info.get('name', 'Unknown'),
                    'text': segment.get('text', ''),
                    'start_time': segment.get('startTime'),
                    'end_time': segment.get('endTime'),
                    'confidence': segment.get('confidence')
                })
        
        # Fallback for voice channel with embedded transcript
        elif channel_type == 'voice':
            transcript_obj = channel.get('transcript')
            if transcript_obj:
                segments = transcript_obj.get('segments', [])
                for segment in segments:
                    speaker_info = segment.get('speaker', {})
                    transcripts.append({
                        'speaker_id': speaker_info.get('id', 'Unknown'),
                        'speaker_name': speaker_info.get('name', 'Unknown'),
                        'text': segment.get('text', ''),
                        'start_time': segment.get('startTime'),
                        'end_time': segment.get('endTime'),
                        'confidence': segment.get('confidence')
                    })
                    
    transcripts.sort(key=lambda x: x['start_time'] if x['start_time'] else '')
    return transcripts

def is_transcription_ready(conversation_data: Dict[str, Any]) -> bool:
    channels = conversation_data.get('channels', [])
    for channel in channels:
        if channel.get('type') == 'transcription' and channel.get('transcriptType') == 'voice':
            if len(channel.get('segments', [])) > 0:
                return True
    return False

def fetch_full_transcript(conversation_id: str, max_retries: int = 5, wait_seconds: int = 10) -> List[Dict[str, Any]]:
    """
    Main function to fetch the transcript with retry logic for pending transcriptions.
    """
    api_client = get_platform_client()
    
    for attempt in range(1, max_retries + 1):
        print(f"Attempt {attempt}/{max_retries}: Querying conversation {conversation_id}...")
        
        conv_data = get_conversation_details(api_client, conversation_id)
        
        if conv_data is None:
            print("Conversation not found.")
            return []
            
        if is_transcription_ready(conv_data):
            print("Transcription is ready. Extracting segments...")
            return extract_transcript_segments(conv_data)
        else:
            if attempt < max_retries:
                print(f"Transcription not ready yet. Waiting {wait_seconds} seconds...")
                time.sleep(wait_seconds)
            else:
                print("Max retries reached. Transcription may still be processing.")
                
    return []

if __name__ == "__main__":
    # Replace with a real Conversation ID from your Genesys Cloud organization
    TARGET_CONVERSATION_ID = os.getenv("TARGET_CONVERSATION_ID", "replace-with-real-id")
    
    if TARGET_CONVERSATION_ID == "replace-with-real-id":
        print("Error: Set TARGET_CONVERSATION_ID in your .env file.")
        sys.exit(1)
        
    try:
        transcript_segments = fetch_full_transcript(TARGET_CONVERSATION_ID)
        
        if not transcript_segments:
            print("No transcript segments found.")
        else:
            print("\n--- Full Transcript ---")
            for segment in transcript_segments:
                speaker = segment['speaker_name'] or segment['speaker_id']
                text = segment['text']
                start = segment['start_time']
                print(f"[{start}] {speaker}: {text}")
            print("-----------------------")
            
    except Exception as e:
        print(f"Error: {e}")
        sys.exit(1)

Common Errors & Debugging

Error: 403 Forbidden

Cause: The Service Account used for authentication does not have the required OAuth scopes.
Fix: Ensure your API Key has the analytics:conversation:read scope. Go to Admin > Security > OAuth 2.0 Clients, edit your client, and verify the scope is selected.

Error: Conversation Not Found (404)

Cause: The conversationId provided is invalid, or the conversation was deleted.
Fix: Verify the ID is a valid UUID. Ensure the conversation is not older than your organization’s data retention policy (default is often 1 year for analytics, but can be shorter).

Error: Empty Transcript List

Cause: The transcription process has not completed yet, or the conversation did not have voice transcription enabled.
Fix:

1. Check if the conversation is a voice call. Chat/SMS conversations have different channel types (chat, sms).
2. Implement the retry logic shown in the complete example. Transcription can take several minutes after the call ends.
3. Verify that “Automatic Transcription” is enabled in your Genesys Cloud Admin settings under Speech and Text Analytics.

Error: AttributeError on SDK Objects

Cause: Trying to access properties on the SDK object that do not exist or are nested differently than expected.
Fix: The Genesys Cloud Python SDK returns dataclass objects. Use asdict() from the dataclasses module to convert them to standard dictionaries before parsing, as shown in the get_conversation_details function. Alternatively, inspect the object structure in a debugger to find the correct path to the channels list.

Official References

• Genesys Cloud Analytics API Documentation
• Conversation Details Query Endpoint
• Python SDK Reference
• OAuth 2.0 Authentication Guide