How to Build a Voice AI Agent Using Deepgram and OpenAI: A Step-by-Step Guide
Blog post from Deepgram
A tutorial explains how to build a Python-based customer-support voice AI agent using Deepgram’s speech-to-text, text-to-speech, and audio intelligence APIs alongside OpenAI’s GPT-3.5 Turbo. The workflow transcribes a customer’s audio inquiry, extracts topics, summaries, sentiment, and intent, sends the transcription to an LLM guided by a customer-service system prompt, and converts the generated reply into spoken audio. It covers obtaining and securely storing API keys, setting up a Python environment, organizing helper functions for transcription, analysis, language-model responses, and speech generation, and testing the system with a synthesized billing inquiry. The demo produces a transcript-derived topic and summary, an empathetic support response, and an output audio file, while suggested extensions include real-time streaming, fine-tuned models, retrieval-augmented generation connected to CRM or ticketing systems, and conversation analytics for management insights.
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