Retrieving the Full Voice Conversation Transcript via Genesys Cloud Speech Analytics

What You Will Build

• This tutorial demonstrates how to programmatically retrieve the full text transcript of a voice conversation using the Genesys Cloud Speech and Text Analytics API.
• The solution utilizes the GET /api/v2/analytics/conversations/details/query endpoint combined with the Speech Analytics SDK to extract transcribed segments.
• The implementation is provided in Python using the official genesyscloud SDK.

Prerequisites

• OAuth Client Type: A Genesys Cloud OAuth Client ID and Secret with the public or confidential grant type.
• Required Scopes: The token must include the analytics:conversation:view scope to access conversation details and transcripts.
• SDK Version: genesyscloud Python SDK version 140.0.0 or later.
• Runtime: Python 3.8 or higher.
• Dependencies: Install the SDK via pip:

  pip install genesyscloud
  

Authentication Setup

Genesys Cloud APIs require OAuth 2.0 authentication. The most robust method for server-side integrations is the Client Credentials flow. This flow exchanges your Client ID and Secret for an access token.

The following code initializes the PureCloud platform client. This handles the token acquisition, caching, and automatic refresh logic internally.

from genesyscloud.platform_client import PlatformClient

def get_platform_client(client_id: str, client_secret: str, base_url: str = "https://api.mypurecloud.com"):
    """
    Initializes and returns a configured Genesys Cloud Platform Client.
    """
    # Create the platform client instance
    platform_client = PlatformClient(
        client_id=client_id,
        client_secret=client_secret,
        base_url=base_url
    )
    
    # Configure the client to use the default OAuth flow
    # The SDK automatically manages token caching and refreshing
    platform_client.init_oauth()
    
    return platform_client

Critical Note: If you receive a 401 Unauthorized error, verify that your OAuth Client has the analytics:conversation:view scope assigned in the Genesys Cloud Admin Console under Admin > Integration > OAuth Clients.

Implementation

Retrieving a transcript is not a single API call. The Genesys Cloud architecture separates the conversation index (metadata) from the conversation details (transcripts, recordings, interactions). To get the transcript, you must:

1. Query the Conversation Details API to find the specific conversation ID or retrieve a batch of conversations.
2. Extract the transcripts array from the response.
3. Process the transcript segments to reconstruct the full text.

Step 1: Constructing the Conversation Details Query

The GET /api/v2/analytics/conversations/details/query endpoint accepts a complex JSON body. This body defines the time range, the conversation type (voice), and the specific data you want to return.

To retrieve transcripts, you must ensure the transcripts field is included in the fields parameter. If you omit this, the API will return metadata but no text.

import json
from datetime import datetime, timezone

def build_conversation_query(start_time: str, end_time: str, limit: int = 10):
    """
    Builds the JSON payload for the Conversation Details Query.
    
    Args:
        start_time: ISO 8601 start time (e.g., "2023-10-01T00:00:00Z")
        end_time: ISO 8601 end time (e.g., "2023-10-02T00:00:00Z")
        limit: Maximum number of conversations to return per page
    
    Returns:
        dict: The query payload
    """
    query_payload = {
        "interval": f"{start_time}/{end_time}",
        "groupBy": ["conversation"],
        "filter": [
            {
                "type": "equals",
                "path": "type",
                "value": "voice"
            }
        ],
        "aggregations": [],
        "fields": [
            "id",
            "type",
            "startTime",
            "endTime",
            "transcripts",  # Critical: Must include this to get text
            "participants"
        ],
        "size": limit
    }
    return query_payload

Step 2: Executing the Query and Handling Pagination

The Conversation Details API supports pagination via the nextPageToken. If you are querying a large volume of conversations, you must loop until no nextPageToken is returned.

For this tutorial, we will implement a simple fetch that retrieves the first page. In production, wrap the api.get_analytics_conversations_details_query call in a while loop checking for response.next_page_token.

from genesyscloud.platform_client import PlatformClient
from genesyscloud.analytics.api.conversations_api import ConversationsApi

def fetch_conversation_transcripts(platform_client: PlatformClient, start_time: str, end_time: str):
    """
    Fetches conversation details including transcripts.
    
    Args:
        platform_client: Authenticated PlatformClient instance
        start_time: ISO 8601 start time
        end_time: ISO 8601 end time
    
    Returns:
        list: A list of conversation objects containing transcripts
    """
    conversations_api = ConversationsApi(platform_client)
    
    # Build the query body
    query_body = build_conversation_query(start_time, end_time, limit=5)
    
    try:
        # Execute the query
        # Note: The SDK method is get_analytics_conversations_details_query
        response = conversations_api.get_analytics_conversations_details_query(
            body=query_body
        )
        
        # Check if the response contains entities
        if response.entities:
            return response.entities
        else:
            print("No conversations found in the specified time range.")
            return []
            
    except Exception as e:
        # Handle API errors (400, 401, 429, 500)
        print(f"Error fetching conversations: {e}")
        raise e

Step 3: Processing the Transcript Data Structure

The transcripts field in the response is an array of objects. Each object represents a segment of the conversation. Voice transcripts are typically split into segments based on speaker turns or silence gaps.

Each transcript segment contains:

• text: The actual transcribed text.
• speaker: The role of the speaker (e.g., agent, customer, system).
• start: The timestamp of the segment start.
• duration: The length of the segment in milliseconds.

You must iterate through these segments to reconstruct the full narrative.

def extract_full_transcript(conversation_entity: dict) -> str:
    """
    Extracts and concatenates the transcript text from a conversation entity.
    
    Args:
        conversation_entity: A single conversation object from the API response
    
    Returns:
        str: The full transcript with speaker labels
    """
    transcripts = conversation_entity.get("transcripts", [])
    
    if not transcripts:
        return "No transcript available for this conversation."
    
    full_text_lines = []
    
    for segment in transcripts:
        speaker = segment.get("speaker", "Unknown")
        text = segment.get("text", "").strip()
        
        # Skip empty segments
        if not text:
            continue
            
        # Format: [Speaker]: Text
        full_text_lines.append(f"[{speaker.upper()}]: {text}")
        
    # Join lines with newlines for readability
    return "\n".join(full_text_lines)

Complete Working Example

The following script combines authentication, querying, and processing into a single runnable module. Replace YOUR_CLIENT_ID and YOUR_CLIENT_SECRET with your actual credentials.

import os
import sys
from datetime import datetime, timezone, timedelta
from genesyscloud.platform_client import PlatformClient
from genesyscloud.analytics.api.conversations_api import ConversationsApi

def get_platform_client(client_id: str, client_secret: str):
    """Initializes the Genesys Cloud Platform Client."""
    platform_client = PlatformClient(
        client_id=client_id,
        client_secret=client_secret,
        base_url="https://api.mypurecloud.com"
    )
    platform_client.init_oauth()
    return platform_client

def build_conversation_query(start_time: str, end_time: str):
    """Builds the JSON payload for the Conversation Details Query."""
    return {
        "interval": f"{start_time}/{end_time}",
        "groupBy": ["conversation"],
        "filter": [
            {
                "type": "equals",
                "path": "type",
                "value": "voice"
            }
        ],
        "aggregations": [],
        "fields": [
            "id",
            "type",
            "startTime",
            "endTime",
            "transcripts",
            "participants"
        ],
        "size": 5
    }

def extract_full_transcript(conversation_entity: dict) -> str:
    """Extracts and concatenates the transcript text from a conversation entity."""
    transcripts = conversation_entity.get("transcripts", [])
    
    if not transcripts:
        return "No transcript available for this conversation."
    
    full_text_lines = []
    
    for segment in transcripts:
        speaker = segment.get("speaker", "Unknown")
        text = segment.get("text", "").strip()
        
        if not text:
            continue
            
        full_text_lines.append(f"[{speaker.upper()}]: {text}")
        
    return "\n".join(full_text_lines)

def main():
    # 1. Setup Authentication
    client_id = os.getenv("GENESYS_CLIENT_ID", "YOUR_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET", "YOUR_CLIENT_SECRET")
    
    if client_id == "YOUR_CLIENT_ID":
        print("Error: Please set GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET environment variables.")
        sys.exit(1)
        
    platform_client = get_platform_client(client_id, client_secret)
    
    # 2. Define Time Range (Last 24 Hours)
    end_time = datetime.now(timezone.utc)
    start_time = end_time - timedelta(days=1)
    
    start_iso = start_time.strftime("%Y-%m-%dT%H:%M:%SZ")
    end_iso = end_time.strftime("%Y-%m-%dT%H:%M:%SZ")
    
    print(f"Querying conversations from {start_iso} to {end_iso}...")
    
    # 3. Execute Query
    conversations_api = ConversationsApi(platform_client)
    query_body = build_conversation_query(start_iso, end_iso)
    
    try:
        response = conversations_api.get_analytics_conversations_details_query(body=query_body)
        
        if not response.entities:
            print("No voice conversations found in the last 24 hours.")
            return
            
        print(f"Found {len(response.entities)} conversation(s).\n")
        
        # 4. Process and Print Transcripts
        for conv in response.entities:
            conv_id = conv.get("id", "Unknown ID")
            start = conv.get("startTime", "Unknown Time")
            
            print(f"--- Conversation ID: {conv_id} (Started: {start}) ---")
            transcript_text = extract_full_transcript(conv)
            print(transcript_text)
            print("-" * 50)
            
    except Exception as e:
        print(f"Failed to fetch conversations: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized or 403 Forbidden

Cause:
The OAuth token does not have the required analytics:conversation:view scope, or the Client ID/Secret is incorrect.

Fix:

1. Log in to the Genesys Cloud Admin Console.
2. Navigate to Admin > Integration > OAuth Clients.
3. Select your client.
4. Under the Scopes tab, ensure analytics:conversation:view is checked.
5. Save the changes. The SDK will automatically use the new scope on the next token refresh.

Error: Transcripts field is missing or empty

Cause:

1. The conversation type is not voice. The query filter restricts results to voice, but if you remove the filter, you may get chat or email conversations which do not have speech transcripts.
2. Speech Analytics is not enabled for the organization or the specific queue.
3. The conversation is too recent. Speech Analytics processing is asynchronous. It can take several minutes to an hour for transcripts to appear after the conversation ends.

Fix:

• Verify the conversation ended at least 15 minutes ago.
• Check the Speech Analytics settings in Admin to ensure transcription is enabled.
• Confirm the fields array in your query payload explicitly includes "transcripts".

Error: 429 Too Many Requests

Cause:
You are hitting the API rate limit. The Conversation Details API has a strict rate limit.

Fix:
Implement exponential backoff. The Genesys Cloud Python SDK includes built-in retry logic, but you can also manually handle it.

import time
import random

def api_call_with_retry(api_func, *args, max_retries=3, **kwargs):
    for attempt in range(max_retries):
        try:
            return api_func(*args, **kwargs)
        except Exception as e:
            if "429" in str(e):
                wait_time = (2 ** attempt) + random.uniform(0, 1)
                print(f"Rate limited. Retrying in {wait_time:.2f} seconds...")
                time.sleep(wait_time)
            else:
                raise e
    raise Exception("Max retries exceeded")

Official References

• Conversation Details Query API Documentation
• Genesys Cloud Python SDK Reference
• Speech Analytics Overview