How to Perform Audio Call QA Analysis Using the Sonnet Model and Deepgram API

Quality Assurance (QA) analysis for audio calls is crucial for businesses that rely on customer interactions. By analyzing customer support or sales calls, companies can improve customer experience, ensure compliance, and enhance agent performance. With AI-powered tools like Deepgram for speech-to-text conversion and Sonnet for intelligent analysis, automating QA analysis has become easier than ever.

Why Use Deepgram and Sonnet?

Deepgram is an AI-powered speech-to-text platform known for its accuracy and real-time transcription capabilities. Sonnet, on the other hand, is a powerful AI model capable of analyzing text data and extracting meaningful insights. When combined, they offer a seamless way to process and analyze call recordings for QA purposes.

Steps to Perform QA Analysis on Audio Calls

1. Convert Audio Calls to Text Using Deepgram

import requests

API_KEY = "your_deepgram_api_key"
AUDIO_FILE_PATH = "path_to_your_audio_file.wav"

with open(AUDIO_FILE_PATH, "rb") as audio:
    response = requests.post(
        "https://api.deepgram.com/v1/listen",
        headers={
            "Authorization": f"Token {API_KEY}",
            "Content-Type": "audio/wav",
        },
        data=audio,
    )
    transcript = response.json()["results"]["channels"][0]["alternatives"][0]["transcript"]
    print("Transcript:", transcript)

2. Analyze the Transcription Using Sonnet Model
Once we have the call transcript, we can analyze it for QA purposes using the Sonnet model. The Sonnet model can help with:

-- Sentiment analysis (detecting customer and agent emotions)
-- Keyword spotting (identifying compliance keywords)
-- Issue detection (highlighting complaints or repeated concerns)
-- Agent performance evaluation (checking script adherence)

import boto3

def analyze_text_with_sonnet(text):
    client = boto3.client("bedrock-runtime")
    response = client.invoke_model(
        modelId="sonnet-3.5",
        contentType="application/json",
        body={"prompt": f"Analyze the sentiment and key insights from this conversation: {text}"}
    )
    return response["output"]

transcription_text = "The customer was unhappy with the service and asked for a refund."
qa_results = analyze_text_with_sonnet(transcription_text)
print("QA Analysis:", qa_results)

3. Automating QA Analysis
-- n8n or Zapier for automation
-- Metabase for visualizing trends in call analytics

Conclusion

Using Deepgram and the Sonnet model together can significantly improve the speed and accuracy of audio call QA analysis. With automated transcription and AI-powered analysis, businesses can gain better insights into customer interactions, ensure compliance, and enhance customer service quality.

By implementing this workflow, you can save time, reduce manual QA efforts, and make data-driven decisions to improve customer satisfaction.