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Build an AI-Powered Voice Journal with Whisper

BeginnerGuided Project

Journaling is hard when you have to type everything. What if you could just talk? Using OpenAI's Whisper model, you'll build an AI-powered voice journal that automatically transcribes your spoken thoughts into organized text entries. This project walks you through speech recognition and audio processing, taking you from raw audio to a working journal app. By the end, you'll have built a complete transcription system and learned how to apply speech-to-text AI to real-world problems.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Machine Learning, NLP, LLM, Artificial Intelligence, Python, AI

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 75 minutes

Platform

  • SkillsNetwork

Last Update

  • January 24, 2026
About this Guided Project
As speech recognition technology becomes more accessible, developers now have the tools to build applications that feel natural and effortless—speaking instead of typing, listening instead of reading. This guided project shows you how to use OpenAI's Whisper model to create a voice-powered journaling application that converts spoken thoughts into searchable, organized text entries. Instead of simple dictation, you'll build an intelligent system that handles audio processing, transcription, metadata management, and persistent storage—giving you hands-on experience with the entire pipeline of speech-to-text AI applications.

What You'll Learn

By the end of this project, you will be able to:
  •  Build a complete voice-to-text application with Whisper: Learn how to load and configure OpenAI's Whisper model, process audio input, and generate accurate transcriptions from speech.
  • Design structured data systems for managing transcriptions: Create a journal class that organizes entries with timestamps, metadata, and audio files, giving you a foundation for building any content management system.
  •  Implement audio processing pipelines with librosa: Understand how to load, resample, and prepare audio data for machine learning models, including handling different file formats and sample rates.
  • Add search and export functionality to text-based applications: Extend your journal with keyword search and file export features, making your data useful and accessible.

Who Should Enroll

  •  Early-career ML engineers who want practical experience applying speech recognition models to real-world use cases beyond simple demos or tutorials.
  • Python developers interested in building AI-powered applications but unsure how to integrate models like Whisper into complete, functional systems.
  • Product builders exploring voice interfaces and conversational AI who need to understand the technical foundations of speech-to-text pipelines.

Why Enroll

This project bridges the gap between running a model once and building something you'd actually use. You'll learn how to integrate Whisper into a complete application, handle real audio data, design persistent storage systems, and build user-facing features—skills that translate directly to building voice assistants, transcription services, or any speech-enabled application. By the end, you won't just understand speech recognition in theory—you'll have a working voice journal you built yourself, plus the knowledge to adapt it into meeting transcribers, voice note apps, or accessibility tools.

What You'll Need

To get the most out of this project, you should have basic Python programming skills and some familiarity with NumPy or data structures. No prior experience with machine learning or audio processing is required—everything is explained step by step. All dependencies are pre-configured in the cloud environment, and the project runs entirely in your browser using Jupyter notebooks. The Whisper tiny model runs efficiently on CPU, so no GPU is needed.

Instructors

Tenzin Migmar

Data Scientist

Hi, I'm Tenzin. I'm a data scientist intern at IBM interested in applying machine learning to solve difficult problems. Prior to joining IBM, I worked as a research assistant on projects exploring perspectivism and personalization within large language models. In my free time, I enjoy recreational programming and learning to cook new recipes.

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Contributors

Jianping Ye

Data Scientist Intern at IBM

I'm Jianping Ye, currently a Data Scientist Intern at IBM and a PhD candidate at the University of Maryland. I specialize in designing AI solutions that bridge the gap between research and real-world application. With hands-on experience in developing and deploying machine learning models, I also enjoy mentoring and teaching others to unlock the full potential of AI in their work.

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