We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies:
 (i) learning the speaker embedding while keeping the WaveNet core fixed,
 (ii) fine-tuning the entire architecture with stochastic gradient descent, and
 (iii) predicting the speaker embedding with a trained neural network encoder.
 The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.
Sample efficient algorithms to adapt a text-to-speech model to a new voice style with the state-of-the-art performance.