📅 2025-08-05 — Session: Developed and Automated Audio Diarization Script
🕒 00:10–23:30
🏷️ Labels: Python, Audio Processing, Linkedin, SEO, Data Science
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and automate a Python script for audio diarization, specifically targeting audio from YouTube, and to optimize LinkedIn profiles for data-related roles.
Key Activities
- Developed a Python script to automate the process of downloading audio from YouTube, converting it to WAV format, and performing speaker diarization using the pyannote library.
- Refined the script to handle errors and improve functionality, including integration with daily workflows.
- Automated the execution of the diarization script by generating a list of YouTube URLs from a DataFrame and executing the script via command line or subprocess in Python.
- Resolved an ArgumentParser error when running scripts in Jupyter Notebooks by adapting the argument parsing.
- Explored methods to read JSONL files with Pandas effectively.
Achievements
- Successfully developed and automated a robust audio diarization script.
- Resolved technical issues related to script execution and error handling.
Pending Tasks
- Further optimization of LinkedIn profiles for data roles, focusing on SEO strategies and skill alignment.
LinkedIn Optimization
- Outlined strategies for optimizing LinkedIn profiles for data roles, including SEO strategies and key elements for top-ranked profiles.
- Identified common traits in SERP profiles and structured a skill bag for Data Scientist/Data Engineer roles.
Achievements
- Developed a comprehensive plan for LinkedIn profile optimization, enhancing visibility and alignment with recruiter searches.