Voice recognition with fourier transformation with audio input in python
The vanilla version of Fourier Transform (fft) is not the best feature extractor for audio or speech signals. This is primarily due to that FT is a global transformation, meaning that you lose all information along the time axis after the transformation. 

You need to be familiar with the concept of short-time Fourier transform (STFT). Basically STFT tells you what frequency components exist in your signal at each timestamp. The result of STFT (its squared magnitude, to be precise) is called the spectrogram, which is what people usually visualize. An example of spectrogram from the link above:


You may refer to matplotlib.pyplot.specgram or scipy.signal.stft regarding how to plot a spectrogram in Python.

The most commonly used speech feature (as input for neural networks) is the Mel-Frequency Cepstral Coefficients, or MFCC, which carry the similar semantic meaning as the spectrogram. Other commonly used features include PLP, LPCC, etc which you can google for more details. But directly feeding the result of FT or STFT into a neural network is not the best practice.