

You are left with just under 10,000 examples from approximately 40 unique speakers, which should be sufficient.
Note that some speakers with few examples may actually have more audio available if the examples are long. However, 
determining the total amount of audio for each speaker requires scanning through the entire dataset, which is a 
time-consuming process that involves loading and decoding each audio file. As such, we have chosen to skip this step here.
Speaker embeddings
To enable the TTS model to differentiate between multiple speakers, you'll need to create a speaker embedding for each example. 
The speaker embedding is an additional input into the model that captures a particular speaker's voice characteristics.
To generate these speaker embeddings, use the pre-trained spkrec-xvect-voxceleb 
model from SpeechBrain. 
Create a function create_speaker_embedding() that takes an input audio waveform and outputs a 512-element vector 
containing the corresponding speaker embedding.