Master thesis Automatic Multispeaker Voice Cloning
Recent advances in deep learning have shown impressive results in the domain of textto speech. To this end, a deep neural network is usually trained using a corpus of several hours of professionally recorded speech from a single speaker. Giving a new voice to such a model is highly expensive, as it requires recording a new dataset and retraining the model. A recent research introduced a three stage pipeline that allows to clone a voice unseen during training from only a few seconds of reference speech, and without retraining the model. The authors share remarkably natural sounding results, but provide no implementation. We reproduce this framework and open source the first public implementation of it. We adapt the framework with a newer vocoder model, so as to make it run in real time.