AI-Powered Audiobook Pipeline: A Portfolio Project
(The beginning — here’s the initial AI generation)
As part of my professional portfolio, I developed an AI-powered audiobook pipeline to demonstrate how modern AI tools can transform raw text into engaging audio content. This project showcases my ability to integrate Python, OpenAI, and text-to-speech APIs within a structured pipeline — a skill set that directly applies to today’s AI-driven software development landscape.
Why This Project Matters
Employers value engineers who can leverage AI not just conceptually, but practically. This project demonstrates:
- AI Literacy: Applying GPT to refine and transform raw text into narration-ready scripts.
- Pipeline Design: Automating workflows from text input to audio output.
- Real-World Value: Producing a polished audiobook that can be scaled for translations, global distribution, and metadata automation.
Technology Stack
- Python 3.11+ – scripting and pipeline logic.
- OpenAI API (GPT-5) – refining narration text.
- Text-to-Speech API – generating realistic AI voices.
- VS Code – development environment.
- FFmpeg – audio post-processing.
Pipeline Overview
- Input Stage: Load raw short story text.
- Refinement Stage: Use GPT to clean and prepare narration.
- Synthesis Stage: Generate speech via TTS API.
- Post-Processing Stage: Merge, normalize, and export audio files.
- Output Stage: Deliver audiobook-ready MP3 with metadata.
Sample Code (Python)
import openai
import requests
# --- 1. Load text ---
with open("story.txt", "r") as f:
raw_text = f.read()
# --- 2. Refine text with GPT ---
openai.api_key = "YOUR_API_KEY"
response = openai.ChatCompletion.create(
model="gpt-5",
messages=[
{"role": "system", "content": "Refine this story for audiobook narration."},
{"role": "user", "content": raw_text}
]
)
narration_text = response["choices"][0]["message"]["content"]
# --- 3. Convert to speech with TTS API ---
tts_api_url = "https://api.elevenlabs.io/v1/text-to-speech/voice"
headers = {"xi-api-key": "YOUR_TTS_KEY"}
payload = {"text": narration_text, "voice_settings": {"stability": 0.5}}
audio_response = requests.post(tts_api_url, headers=headers, json=payload)
with open("audiobook.mp3", "wb") as out:
out.write(audio_response.content)
Outcomes
- Produced a full audiobook from raw text.
- Demonstrated API orchestration between GPT and TTS services.
- Built a foundation for scaling into translations, distribution, and automation.
Takeaway for Employers
This project demonstrates my:
- Applied AI Engineering – practical AI integration for creative and technical outputs.
- Automation Skills – building scalable pipelines.
- Portfolio Readiness – delivering tangible, AI-driven artifacts that show initiative and technical adaptability.
