In this section, we will present an implementation of Tacotron by using Keras on top of TensorFlow. The advantage of Keras over vanilla TensorFlow is that it allows for faster prototyping. This is permitted by its high modularity. However, in terms of flexibility, TensorFlow has an edge over Keras, even if it requires more effort to master it. At the moment, TensorFlow also offers more built-in functionalities (for example, attention mechanisms), some of which will have to be re-implemented here.
We will use Keras 2.1.5 with TensorFlow 1.6.0 as a backend.
The code base is organized as follows:
- The /data folder is meant to contain the raw dataset, and will be enhanced through several processing steps.
- The /model folder contains the following:
- building_blocks.py, which defines all of the essential units of the Tacotron model
- tacotron_model...
