If you want to use WaveNet to create your own AI assistant, you will need three main components: a text-to-speech system, a speech recognition system, and a natural language understanding system. The text-to-speech system is responsible for converting the text output of the assistant into speech, using WaveNet or a similar model. The speech recognition system is responsible for converting the speech input of the user into text, using a model such as DeepSpeech or Wav2Vec. The natural language understanding system is responsible for extracting the intent and entities from the text input, using a model such as BERT or RASA. You can use existing frameworks and libraries, such as TensorFlow, PyTorch, or Spacy, to implement these components, or you can use cloud services, such as Google Cloud, Amazon Web Services, or Microsoft Azure, to access pre-trained models and APIs.