Day 3: Transcribing Voice with Whisper AI #whisper #aivoice

SEO Title: Day 3: Transcribing Voice with Whisper AI #whisper #aivoice
Focus Keyphrase: AI lie detector
Meta Description: Learn how to use OpenAI Whisper to transcribe audio in your AI lie detector app. Convert microphone input to text and prepare for semantic analysis.

Table of Contents

Convert Voice to Text with Whisper

Today we’ll connect our saved audio to OpenAI Whisper to get clean, accurate transcriptions. Transcribing voice is a vital part of the AI lie detector workflow, allowing us to later compare tone with spoken content.

Step 1: Choose a Whisper Engine

You have two great options for transcription:

✅ OpenAI API (easy, reliable, cloud-based) → OpenAI Whisper API
⚡ Faster-Whisper (local, fast, lightweight) → GitHub

We’ll use OpenAI Whisper here for simplicity. You can switch later if you prefer offline processing.

Step 2: Install the OpenAI SDK

pip install openai

Make sure you’ve set your API key as an environment variable:

export OPENAI_API_KEY=sk-xxxxxxx

Step 3: Add Transcription Logic

Update app.py in the backend:


import openai
import os

@app.route('/api/voice', methods=['POST'])
def receive_audio():
    audio_file = request.files['audio']
    save_path = "latest_input.webm"
    audio_file.save(save_path)

    print("Transcribing...")

    audio = open(save_path, "rb")
    transcript = openai.Audio.transcribe("whisper-1", audio)

    print("Transcript:", transcript["text"])
    return {"transcript": transcript["text"]}

Step 4: Test It Out

Run your Flask server:

python app.py

Open your browser, hit **Start Recording**, speak a sentence, then hit **Stop**. Check your terminal or browser’s response — the transcribed text should be returned!

Why This Matters for AI Lie Detection

Now that your AI lie detector app can understand what was said, you’re ready to analyze the truthfulness of that content using language models (next step).

Coming Tomorrow

In Day 4: Detecting Contradictions Using GPT, we’ll feed this transcript into a language model and compare it against baseline truth patterns to detect potential dishonesty or inconsistency.

Tags: #AIUX #LieDetection #SpeechToText #WhisperAI

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