In Audacity, you start by creating a silent mono 32-bit per sample waveform of length N samples (say 100). You have to choose a length parameter N here, and I'll give you a heuristic for this later on*. You create an impulse response by first filtering a Dirac delta function by the filter you wish to apply to your audio. I suspect, based on your requirements, that you need an 'impulse response' instead (also called a Filter Kernel in the context of FIR filters, or a Convolution Kernel). I thought I was familiar with signal processing, but I haven't heard of a transfer function. I would greatly appreciate any help you could provide me, thanks! Furthermore, as it is the first time I am working with python I have very limited knowledge on how to implement this process in a python script. However I have no clue how I would go about converting the curve into a Transfer-function. If my limited understanding of signal processing is correct I would need to convert the continous curve into a discrete transfer function and then apply this transfer function to the input signal.

I constructed the required filter curve eq in audacity: The needed filter curve constructed in Audacity

which would not fit my needs as I have a specific eq manipulation in mind. Researching the topic I found some ways to process a signal via transfer functions in python, however I only found functions like a low-pass filter etc. I am trying to write a script where I would feed a voice recording into it, internally apply an eq and have the modified signal returned.

Audacity equalizer