

It's important to note that the speechbrain/spkrec-xvect-voxceleb model was trained on English speech from the VoxCeleb 
dataset, whereas the training examples in this guide are in Dutch. While we believe that this model will still generate 
reasonable speaker embeddings for our Dutch dataset, this assumption may not hold true in all cases.
For optimal results, we recommend training an X-vector model on the target speech first. This will ensure that the model 
is better able to capture the unique voice characteristics present in the Dutch language.
Processing the dataset
Finally, let's process the data into the format the model expects. Create a prepare_dataset function that takes in a 
single example and uses the SpeechT5Processor object to tokenize the input text and load the target audio into a log-mel spectrogram. 
It should also add the speaker embeddings as an additional input.