Retrieving Full Conversation Transcripts via Genesys Cloud Speech Analytics API

What You Will Build

This tutorial demonstrates how to retrieve the complete, timed text transcript for a specific voice conversation using the Genesys Cloud Speech and Text Analytics API. You will build a Python script that authenticates via OAuth, queries for conversation details using a conversation ID, and extracts the speaker-labeled transcript segments. The implementation covers authentication, API query construction, result parsing, and error handling for rate limits and missing data.

Prerequisites

• OAuth Client: A Genesys Cloud OAuth client with the following scopes:
  • conversation:read (to retrieve conversation metadata)
  • analytics:conversations:view (to access transcript data via analytics endpoints)
• SDK Version: Genesys Cloud Python SDK (genesys-cloud-sdk) version 130.0.0 or later.
• Language/Runtime: Python 3.8+
• Dependencies:
  • genesys-cloud-sdk
  • requests (if not using SDK for raw HTTP debugging)

Install the SDK via pip:

pip install genesys-cloud-sdk

Authentication Setup

Genesys Cloud uses OAuth 2.0 for API authentication. The most common flow for server-side integrations is the Client Credentials flow. You must cache the access token and handle expiration (tokens typically last 3600 seconds).

The following code snippet initializes the PureCloudPlatformClientV2 client. This client handles token management internally if you configure the refresh callback, but for this tutorial, we will use a simple static configuration for clarity. In production, implement a token cache with automatic refresh.

import os
from purecloudplatformclientv2.rest import ApiException
from purecloudplatformclientv2 import PureCloudPlatformClientV2

def get_platform_client():
    """
    Initializes and returns the Genesys Cloud Platform Client.
    """
    # Environment variables must be set:
    # GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, GENESYS_ENVIRONMENT (e.g., mycompany.mypurecloud.com)
    
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    environment = os.getenv("GENESYS_ENVIRONMENT", "mycompany.mypurecloud.com")

    if not all([client_id, client_secret, environment]):
        raise ValueError("Missing required environment variables for Genesys Cloud authentication.")

    # Initialize the platform client
    client = PureCloudPlatformClientV2()

    # Configure the client for Client Credentials flow
    client.set_oauth_client_credentials(client_id, client_secret, environment)

    return client

Implementation

Step 1: Query Conversation Details

The Speech Analytics API does not provide a direct endpoint like /api/v2/voice/conversations/{id}/transcript. Instead, the transcript data is embedded within the detailed conversation analytics response. You must use the POST /api/v2/analytics/conversations/details/query endpoint.

This endpoint accepts a JSON body specifying the date range and the specific conversation ID(s) you wish to retrieve.

Required OAuth Scope: analytics:conversations:view

from purecloudplatformclientv2 import AnalyticsApi
from purecloudplatformclientv2.models import ConversationDetailsQuery

def get_conversation_details(client: PureCloudPlatformClientV2, conversation_id: str, start_date: str, end_date: str):
    """
    Retrieves detailed conversation analytics including transcript.
    
    Args:
        client: The initialized PureCloudPlatformClientV2 instance.
        conversation_id: The UUID of the conversation.
        start_date: ISO 8601 datetime string (e.g., "2023-10-01T00:00:00Z").
        end_date: ISO 8601 datetime string (e.g., "2023-10-31T23:59:59Z").
    
    Returns:
        The ConversationDetailsResponse object.
    """
    analytics_api = AnalyticsApi(client)
    
    # Construct the query body
    # The 'filter' object is critical. We filter by conversationId.
    query_body = ConversationDetailsQuery(
        date_range=f"{start_date}/{end_date}",
        filter={
            "conversationId": {
                "eq": conversation_id
            }
        },
        # Select only the necessary fields to reduce payload size.
        # 'transcript' is the key field we need.
        select=["id", "transcript", "participants"]
    )

    try:
        # Execute the query
        response = analytics_api.post_analytics_conversations_details_query(body=query_body)
        return response
    except ApiException as e:
        print(f"Exception when calling AnalyticsApi->post_analytics_conversations_details_query: {e}")
        raise

Step 2: Extracting the Transcript

The response from post_analytics_conversations_details_query contains a conversation_details list. Each item in this list represents a conversation. The transcript is located under the transcript property of each detail object.

The transcript is an array of segments. Each segment contains:

• start_time: Offset in seconds from the start of the conversation.
• end_time: Offset in seconds.
• speaker: The participant ID who spoke.
• text: The transcribed text.

It is important to note that the transcript may be empty if:

1. The conversation did not have speech analytics enabled.
2. The transcription is still processing (for very recent conversations).
3. The conversation was not a voice call (e.g., chat or email).

def extract_transcript(conversation_detail):
    """
    Extracts and formats the transcript from a ConversationDetail object.
    
    Args:
        conversation_detail: A single item from the conversation_details list.
        
    Returns:
        A list of dictionaries containing 'speaker_id', 'text', and 'timestamp'.
    """
    transcript_segments = []
    
    # Check if transcript property exists and is not None
    if not hasattr(conversation_detail, 'transcript') or conversation_detail.transcript is None:
        return []
    
    if not conversation_detail.transcript.segments:
        return []

    for segment in conversation_detail.transcript.segments:
        transcript_segments.append({
            "speaker_id": segment.speaker.id if segment.speaker else "Unknown",
            "speaker_name": segment.speaker.name if segment.speaker else "Unknown",
            "text": segment.text,
            "start_time": segment.start_time,
            "end_time": segment.end_time
        })
        
    return transcript_segments

Step 3: Handling Rate Limits and Retries

Genesys Cloud APIs enforce rate limits (HTTP 429). When retrieving analytics data, especially if querying many conversations, you may hit these limits. The SDK does not automatically retry all 429s in all versions, so implementing a simple exponential backoff is recommended for robustness.

import time
import random

def retry_on_rate_limit(func, *args, max_retries=3, base_delay=1):
    """
    Decorator-like function to retry API calls on 429 Too Many Requests.
    """
    for attempt in range(max_retries):
        try:
            return func(*args)
        except ApiException as e:
            if e.status == 429:
                # Extract Retry-After header if present
                retry_after = e.headers.get('Retry-After')
                if retry_after:
                    delay = int(retry_after)
                else:
                    # Exponential backoff with jitter
                    delay = base_delay * (2 ** attempt) + random.uniform(0, 1)
                
                print(f"Rate limited. Retrying in {delay:.2f} seconds...")
                time.sleep(delay)
            else:
                # Re-raise if it is not a 429 error
                raise
    raise Exception("Max retries exceeded due to rate limiting.")

Complete Working Example

The following script combines all steps into a runnable module. It retrieves the transcript for a specific conversation ID.

import os
import sys
import json
from datetime import datetime, timezone, timedelta

from purecloudplatformclientv2 import PureCloudPlatformClientV2, AnalyticsApi, ApiException
from purecloudplatformclientv2.models import ConversationDetailsQuery

def get_platform_client():
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    environment = os.getenv("GENESYS_ENVIRONMENT", "mycompany.mypurecloud.com")

    if not all([client_id, client_secret, environment]):
        raise ValueError("Missing required environment variables.")

    client = PureCloudPlatformClientV2()
    client.set_oauth_client_credentials(client_id, client_secret, environment)
    return client

def get_conversation_transcript(conversation_id: str):
    """
    Main function to retrieve and print the transcript for a given conversation ID.
    """
    client = get_platform_client()
    
    # Define date range: Last 30 days
    end_date = datetime.now(timezone.utc)
    start_date = end_date - timedelta(days=30)
    
    date_range_str = f"{start_date.strftime('%Y-%m-%dT%H:%M:%SZ')}/{end_date.strftime('%Y-%m-%dT%H:%M:%SZ')}"
    
    try:
        analytics_api = AnalyticsApi(client)
        
        query_body = ConversationDetailsQuery(
            date_range=date_range_str,
            filter={
                "conversationId": {
                    "eq": conversation_id
                }
            },
            select=["id", "transcript", "participants"]
        )

        # Execute query with retry logic
        def execute_query():
            return analytics_api.post_analytics_conversations_details_query(body=query_body)

        response = retry_on_rate_limit(execute_query)

        if not response.conversation_details:
            print(f"No conversation found with ID: {conversation_id} in the specified date range.")
            return

        conversation_detail = response.conversation_details[0]
        
        # Extract transcript
        transcript = extract_transcript(conversation_detail)
        
        if not transcript:
            print(f"Conversation {conversation_id} found, but no transcript data available.")
            print("Possible reasons: Speech analytics not enabled, transcription pending, or non-voice channel.")
            return

        # Output the transcript
        print(f"Transcript for Conversation ID: {conversation_detail.id}")
        print("-" * 50)
        
        for segment in transcript:
            speaker = segment['speaker_name'] if segment['speaker_name'] != "Unknown" else f"ID: {segment['speaker_id']}"
            print(f"[{segment['start_time']:.2f}s - {segment['end_time']:.2f}s] {speaker}:")
            print(f"  {segment['text']}")
            print()

    except ApiException as e:
        print(f"API Error: {e.status} - {e.reason}")
        if e.body:
            print(f"Response Body: {e.body}")
        raise
    except Exception as e:
        print(f"Unexpected Error: {e}")
        raise

def extract_transcript(conversation_detail):
    transcript_segments = []
    
    if not hasattr(conversation_detail, 'transcript') or conversation_detail.transcript is None:
        return []
    
    if not conversation_detail.transcript.segments:
        return []

    for segment in conversation_detail.transcript.segments:
        transcript_segments.append({
            "speaker_id": segment.speaker.id if segment.speaker else "Unknown",
            "speaker_name": segment.speaker.name if segment.speaker else "Unknown",
            "text": segment.text,
            "start_time": segment.start_time,
            "end_time": segment.end_time
        })
        
    return transcript_segments

def retry_on_rate_limit(func, *args, max_retries=3, base_delay=1):
    import time
    import random
    for attempt in range(max_retries):
        try:
            return func(*args)
        except ApiException as e:
            if e.status == 429:
                retry_after = e.headers.get('Retry-After')
                if retry_after:
                    delay = int(retry_after)
                else:
                    delay = base_delay * (2 ** attempt) + random.uniform(0, 1)
                
                print(f"Rate limited. Retrying in {delay:.2f} seconds...")
                time.sleep(delay)
            else:
                raise
    raise Exception("Max retries exceeded due to rate limiting.")

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python get_transcript.py <conversation_id>")
        sys.exit(1)
        
    conv_id = sys.argv[1]
    get_conversation_transcript(conv_id)

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token is expired, invalid, or the client credentials are incorrect.
• Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET. Ensure the client is active in the Genesys Cloud Admin console. Check that the token has not exceeded its 3600-second lifetime.

Error: 403 Forbidden

• Cause: The OAuth client lacks the required scopes.
• Fix: Ensure the client has analytics:conversations:view and conversation:read scopes assigned in the Admin console under Admin > Security > OAuth Clients.

Error: Empty Transcript Array

• Cause: The conversation exists, but no text was returned.
• Fix:
  1. Verify the conversation is a voice call. Chat and email transcripts are retrieved via different endpoints.
  2. Check if Speech Analytics is enabled for the user or queue involved in the conversation.
  3. For very recent conversations (last 15-30 minutes), transcription may still be processing. Implement a polling mechanism with exponential backoff if you need real-time data.

Error: 400 Bad Request - Invalid Date Range

• Cause: The date_range format is incorrect or the range is too large.
• Fix: Ensure the date range is in ISO 8601 format (YYYY-MM-DDTHH:mm:ssZ). The analytics API often restricts queries to a maximum of 30 days. Split queries into smaller chunks if necessary.

Error: 429 Too Many Requests

• Cause: You have exceeded the API rate limit for your tenant or client.
• Fix: Implement the retry logic shown in Step 3. Check the Retry-After header in the response for the exact wait time. Reduce the frequency of your polling if this occurs frequently.

Official References

• Genesys Cloud Speech and Text Analytics API Documentation
• Post Analytics Conversations Details Query Endpoint
• Python SDK Reference
• OAuth 2.0 Client Credentials Flow