Retrieving the Full Conversation Transcript via the Speech and Text Analytics API

What You Will Build

You will build a Python script that queries the Genesys Cloud Speech and Text Analytics API to retrieve detailed conversation interactions, specifically extracting the full voice-to-text transcript for a given conversation ID. This tutorial uses the Genesys Cloud REST API v2 via the requests library in Python. The code handles OAuth2 authentication, paginated result retrieval, and JSON parsing to isolate the transcript lines.

Prerequisites

• OAuth Client: A Genesys Cloud API client application with the following scopes:
  • analytics:conversation:view
  • analytics:report:view (required for some analytics query endpoints)
  • speechanalytics:conversation:view (critical for transcript access)
• Environment: Python 3.8+
• Dependencies:
  • requests (for HTTP calls)
  • python-dotenv (for secure credential management)
  • pyyaml (optional, for config parsing, though we will use environment variables)

Install the dependencies:

pip install requests python-dotenv

Authentication Setup

Genesys Cloud uses OAuth 2.0 for API authentication. For server-to-server applications (such as a script running on your backend), you will use the Client Credentials flow. This flow requires your Client ID and Client Secret.

Step 1: Obtain an Access Token

Create a file named .env in your project root to store credentials securely. Do not commit this file to version control.

GENESYS_CLOUD_REGION=us-east-1
GENESYS_CLOUD_CLIENT_ID=your_client_id_here
GENESYS_CLOUD_CLIENT_SECRET=your_client_secret_here

Create a Python module auth.py to handle token retrieval and caching. In production, you should implement token caching to avoid requesting a new token for every API call, as tokens are valid for one hour.

# auth.py
import os
import requests
from dotenv import load_dotenv

load_dotenv()

# Configuration
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")

# Construct the base URL based on the region
if REGION == "us-east-1":
    BASE_URL = "https://api.mypurecloud.com"
elif REGION == "us-east-2":
    BASE_URL = "https://api.mypurecloud.us-east-2.com"
elif REGION == "eu-west-1":
    BASE_URL = "https://api.mypurecloud.ie"
else:
    BASE_URL = f"https://api.{REGION}.mypurecloud.com"

def get_access_token() -> str:
    """
    Retrieves an OAuth2 access token using Client Credentials flow.
    Returns the token string. Raises an exception on failure.
    """
    if not CLIENT_ID or not CLIENT_SECRET:
        raise ValueError("GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET must be set in environment variables.")

    url = f"{BASE_URL}/oauth/token"
    data = {
        "grant_type": "client_credentials",
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET
    }

    try:
        response = requests.post(url, data=data, timeout=10)
        response.raise_for_status()  # Raises HTTPError for bad responses (4xx, 5xx)
        token_json = response.json()
        return token_json.get("access_token")
    except requests.exceptions.RequestException as e:
        raise ConnectionError(f"Failed to obtain access token: {e}")

Implementation

The core logic involves querying the Analytics API for conversation details. While the Speech Analytics API has specific endpoints for insights, the full transcript text is most reliably retrieved via the analytics/conversations/details/query endpoint when the transcript expansion is included, or by using the speechanalytics/conversations/{conversationId} endpoint if you have a specific ID.

For this tutorial, we will use the Speech Analytics endpoint GET /api/v2/speechanalytics/conversations/{conversationId} because it returns structured transcript lines with speaker identification, timestamps, and confidence scores, which is superior for programmatic processing compared to the raw analytics query.

Step 1: Define the Transcript Retrieval Function

We need a function that accepts a conversation_id and returns the transcript lines. This function must handle:

1. Fetching the access token.
2. Constructing the API URL.
3. Handling HTTP errors (401, 403, 404, 429).
4. Parsing the JSON response to extract the transcript array.

Create transcript_extractor.py:

# transcript_extractor.py
import os
import requests
import time
from typing import List, Dict, Any, Optional
from auth import get_access_token, BASE_URL

# Required OAuth Scope: speechanalytics:conversation:view
SCOPE_REQUIRED = "speechanalytics:conversation:view"

def get_transcript_lines(conversation_id: str) -> List[Dict[str, Any]]:
    """
    Retrieves the full transcript lines for a specific conversation ID.
    
    Args:
        conversation_id (str): The ID of the conversation to retrieve.
        
    Returns:
        List[Dict]: A list of transcript line objects containing text, speaker, timestamp, etc.
    """
    token = get_access_token()
    
    # Endpoint for Speech Analytics Conversation Details
    url = f"{BASE_URL}/api/v2/speechanalytics/conversations/{conversation_id}"
    
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json"
    }

    try:
        response = requests.get(url, headers=headers, timeout=15)
        
        # Handle Rate Limiting (429)
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 5))
            print(f"Rate limited. Retrying after {retry_after} seconds...")
            time.sleep(retry_after)
            # Note: In a production system, implement a robust retry loop with exponential backoff.
            # For this example, we will simply raise an error after one retry attempt to keep code concise.
            response = requests.get(url, headers=headers, timeout=15)
            response.raise_for_status()

        response.raise_for_status()
        
        data = response.json()
        
        # The transcript is located in the 'transcript' key of the response body
        transcript_lines = data.get("transcript", [])
        
        if not transcript_lines:
            print(f"No transcript found for conversation {conversation_id}.")
            return []
            
        return transcript_lines

    except requests.exceptions.HTTPError as http_err:
        if response.status_code == 401:
            raise PermissionError("Unauthorized: Check your OAuth scopes. Ensure 'speechanalytics:conversation:view' is granted.")
        elif response.status_code == 403:
            raise PermissionError("Forbidden: The client does not have permission to view this conversation's analytics.")
        elif response.status_code == 404:
            raise ValueError(f"Conversation {conversation_id} not found in Speech Analytics.")
        else:
            raise ConnectionError(f"HTTP Error: {http_err}")
            
    except requests.exceptions.RequestException as req_err:
        raise ConnectionError(f"Network error: {req_err}")

Step 2: Processing and Formatting the Transcript

The raw JSON response contains a list of objects. Each object represents a segment of speech. The structure typically looks like this:

{
  "transcript": [
    {
      "speaker": "agent",
      "text": "Hello, how can I help you?",
      "beginTime": "2023-10-27T10:00:01.000Z",
      "endTime": "2023-10-27T10:00:03.000Z",
      "confidence": 0.98,
      "words": [
        { "text": "Hello", "beginTime": "...", "endTime": "...", "confidence": 0.99 },
        { "text": "how", "beginTime": "...", "endTime": "...", "confidence": 0.95 }
      ]
    }
  ]
}

To make this useful, we will create a helper function that formats these lines into a readable string, including timestamps and speaker labels. This is crucial for debugging and logging.

# transcript_extractor.py (append to existing file)

def format_transcript(transcript_lines: List[Dict[str, Any]]) -> str:
    """
    Formats a list of transcript lines into a human-readable string.
    
    Args:
        transcript_lines (List[Dict]): The list of transcript objects from the API.
        
    Returns:
        str: A formatted string of the conversation.
    """
    formatted_output = []
    
    for line in transcript_lines:
        speaker = line.get("speaker", "unknown").upper()
        text = line.get("text", "")
        begin_time = line.get("beginTime", "")
        confidence = line.get("confidence", 0.0)
        
        # Format timestamp for readability (remove milliseconds for cleaner output)
        if begin_time:
            # Simple slicing to remove the last 4 chars (milliseconds + Z) for display
            # This assumes ISO 8601 format
            ts_display = begin_time[:-4]
        else:
            ts_display = "00:00:00"

        # Construct the line
        # Format: [10:00:01] AGENT: Hello (Conf: 0.98)
        line_str = f"[{ts_display}] {speaker}: {text} (Conf: {confidence:.2f})"
        formatted_output.append(line_str)
        
    return "\n".join(formatted_output)

Step 3: Querying by Date Range (Alternative Approach)

If you do not have a specific conversation_id but want to retrieve transcripts for all conversations within a time window, you must use the Analytics Query API. This is more complex because it requires building a query object and handling pagination.

The endpoint is POST /api/v2/analytics/conversations/details/query.

# transcript_extractor.py (append to existing file)

def get_transcripts_by_date_range(start_time: str, end_time: str, max_results: int = 10) -> List[Dict[str, Any]]:
    """
    Retrieves transcripts for conversations within a date range using the Analytics Query API.
    
    Args:
        start_time (str): ISO 8601 start time (e.g., "2023-10-01T00:00:00.000Z")
        end_time (str): ISO 8601 end time (e.g., "2023-10-02T00:00:00.000Z")
        max_results (int): Maximum number of conversations to retrieve.
        
    Returns:
        List[Dict]: A list of dictionaries, each containing 'conversationId' and 'transcript'.
    """
    token = get_access_token()
    url = f"{BASE_URL}/api/v2/analytics/conversations/details/query"
    
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json"
    }

    # Build the query body
    # Note: The 'expansions' parameter is critical to include the transcript data
    query_body = {
        "dateFrom": start_time,
        "dateTo": end_time,
        "pageSize": 100,
        "totalRequired": True,
        "expansions": ["transcript"],
        "filters": [
            {
                "type": "boolean",
                "filterType": "and",
                "filters": [
                    {
                        "type": "string",
                        "filterType": "eq",
                        "dimension": "conversationType",
                        "value": "voice"
                    }
                ]
            }
        ]
    }

    all_conversations = []
    next_page_token = None

    try:
        while len(all_conversations) < max_results:
            if next_page_token:
                query_body["pageToken"] = next_page_token
            
            response = requests.post(url, json=query_body, headers=headers, timeout=15)
            response.raise_for_status()
            
            data = response.json()
            
            # Add results to our list
            conversations = data.get("entities", [])
            all_conversations.extend(conversations)
            
            # Check for pagination
            next_page_token = data.get("nextPageToken")
            if not next_page_token:
                break
                
            # Safety break to prevent infinite loops in case of API anomalies
            if len(all_conversations) >= 1000: 
                print("Reached safety limit of 1000 results.")
                break

        return all_conversations[:max_results]

    except requests.exceptions.RequestException as e:
        raise ConnectionError(f"Failed to query analytics: {e}")

Complete Working Example

Combine the modules into a single executable script main.py. This script demonstrates retrieving a single conversation’s transcript and printing it.

# main.py
import os
from dotenv import load_dotenv
from transcript_extractor import get_transcript_lines, format_transcript

load_dotenv()

def main():
    # Example Conversation ID
    # Replace this with a real conversation ID from your Genesys Cloud instance
    # You can find one by looking at the URL in the Admin Console -> Conversations -> History
    conversation_id = os.getenv("SAMPLE_CONVERSATION_ID")
    
    if not conversation_id:
        print("Please set SAMPLE_CONVERSATION_ID in your .env file.")
        return

    print(f"Retrieving transcript for conversation: {conversation_id}\n")
    
    try:
        # Step 1: Fetch raw transcript lines
        transcript_lines = get_transcript_lines(conversation_id)
        
        if not transcript_lines:
            print("No transcript data available.")
            return

        # Step 2: Format and print
        formatted_text = format_transcript(transcript_lines)
        print(formatted_text)
        
        print("\n--- Raw JSON Preview (First Line) ---")
        import json
        print(json.dumps(transcript_lines[0], indent=2))

    except PermissionError as pe:
        print(f"Authentication/Permission Error: {pe}")
    except ValueError as ve:
        print(f"Validation Error: {ve}")
    except ConnectionError as ce:
        print(f"Connection Error: {ce}")
    except Exception as e:
        print(f"Unexpected Error: {e}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

Cause: The access token is invalid, expired, or missing the required scope.
Fix:

1. Ensure your .env file has the correct CLIENT_ID and CLIENT_SECRET.
2. Verify that the OAuth Client in the Genesys Cloud Admin Console has the scope speechanalytics:conversation:view checked under Scopes.
3. If you are caching tokens, ensure you are refreshing them after 55 minutes (tokens expire at 60).

Error: 403 Forbidden

Cause: The client has the correct token but lacks permission to access the specific data.
Fix:

1. Check that the OAuth Client has Role assignments that allow viewing Analytics. By default, many custom clients do not have access to all analytics data.
2. Assign the built-in role Analyst or a custom role with the Conversation Analytics permission to the OAuth Client.
3. Ensure the conversation is not from a queue or user that is excluded from analytics by organization policy.

Error: 404 Not Found

Cause: The conversation_id provided does not exist in the Speech Analytics database.
Fix:

1. Verify the ID is correct.
2. Note that Speech Analytics processes conversations asynchronously. A conversation that just ended may not have a transcript available immediately. Wait 5-10 minutes after the conversation ends before querying.
3. Ensure the conversation was actually a “voice” conversation. Text chats may not populate the voice transcript endpoint in the same way.

Error: Empty Transcript Array

Cause: The conversation was retrieved, but the transcript field is empty.
Fix:

1. The conversation may have been too short for speech analytics to process.
2. The language model may not have been configured correctly for the conversation’s language.
3. Check the speechAnalytics section of the conversation object for status. If it is failed or processing, the transcript is not yet ready.

Error: 429 Too Many Requests

Cause: You have exceeded the API rate limit for your organization.
Fix:

1. Implement exponential backoff in your retry logic.
2. Reduce the frequency of requests.
3. Use the Retry-After header value from the response to determine the wait time.

Official References

• Genesys Cloud Speech Analytics API Documentation
• Genesys Cloud Analytics Conversations Details Query
• OAuth 2.0 Client Credentials Flow