Building custom data loader, experiment logging, tips for improving metrics, and GitHub repo if you’d like to follow along — Why Audio Data? NLP for audio data is not getting enough recognition, compared to NLP for text and computer vision tasks. Time to change that! Task Emotion recognition — recognize whether spoken audio exhibits anger, happiness, sadness, disgust, surprise, or neutral emotions.

Watch variables, use conditional breakpoints, call stack, exception breakpoint, etc— when working on larger projects — Quick pulse check — Still using print() statements to debug your code? Hey, no shame in admitting if you do. If it works, it works (especially for smaller scripts)! Having said that, it’s not a sustainable solution when working with larger projects — involving a buttload of third-party packages, util…

Tips and tricks I use for writing clean codes on a day-to-day basis — In this article, I wanted to share a few tips for writing cleaner codes that I have absorbed in the last year — mainly from pair programming. Generally speaking, including them as part of my everyday coding routine has helped me generate supreme quality Python scripts, that are easily maintainable…

Bonus intro to keywords like seasonality, trend, autocorrelation, and much more. — Right off the bat, time-series data is not your average dataset! You might have worked with housing data wherein each row represents features of a particular house (such as total area, number of bedrooms, year in which it was built) or student dataset wherein each row represents such…

How to build a dashboard for generating insights — by Dr. Varshita Sher, Zohaib Khan Introduction Haleon has a world-class portfolio of category-leading global brands, including Sensodyne, Voltaren, Panadol, and Centrum, to name a few. Our brands are trusted by health professionals, customers, and people worldwide to improve the health and well-being of individuals and their communities. It is, therefore…

Thanks, Samuele. This is brilliant. Clearly the cut off assumption plays a big role here in generating the predictor matrix. Do you have any recommendations in this regard - perhaps trying a bunch of cut offs, averaging, etc and the impact on the outcome, if any?

That's cause we don't want to average samples across different channels (and risk losing some signal in data) and ultimately convert the audio into 1-D array (especially when HuBERT can handle multi-dimensional audio arrays).

Optimize model, compress model, search hyperparameters, pip package — all with just a few clicks. — What are hardware-aware models? Often times when data scientists think of an optimized model, they think in terms of one or the other metrics — such as accuracy, precision, recall, etc. In other words, efforts are made to ensure every last bit of accuracy is squeezed out of the training set by tuning the…

Hi Sneha, Please see the following articles wherein I have deployed the ML model on Heroku - End to End Deep Learning Project, Part 1 and Part 2 Hope this helps!