Aligning models to human values and desires is a formidable task, particularly when we're working with vast datasets that resist meticulous control. Concerns arise when language models spew out content that could be false or harmful. Luckily, advancements in AI research are opening pathways for safer and more reliable interactions …

Large language models, as the name already states, have a huge quantity of parameters and have been trained on large-scale datasets to obtain excellent generalization capabilities.

But if it's so good, why would people want to modify such models?
1. To have a specific behavior - Let's say you have a …

Wav2vec2.0, as the name already suggests, claims to receive a raw audio file in its waveform and convert it to a vector (encode it in the latent space). Therefore, many downstream tasks can be performed on top of this vector representation extracted by the model. Meta claims that using …

In basic terms, reinforcement learning is a technique that allows machine learning models to learn by tries and observations. It is beneficial whenever we need a more clear idea of what the optimal behavior for our model is - for ChatGPT for example. We don't have an optimal answer; instead a …

HuggingFace is an open-source library that started to make transformers-based architectures more accessible to a lot of programmers through their APIs. Therefore, being familiar with their technologies it's a good idea for anyone who wants to be able to implement training and inference pipelines faster and still be able to …

Biometrics systems are focused on obtaining the characteristics of an individual and verifying if it does match with someone in the database. These characteristics can be the fingerprint, the voice, and even our iris. These kinds of systems are everywhere nowadays, in our phones and even door locks, therefore some …

In this blog post, I'll be diving into some concepts of digital signal processing (DSP). Most of them I had to learn before starting to work with models that take audio files as input and therefore, require some preprocessing steps.

The concepts I'll briefly discuss here are:
1. Fourier transforms …