Deep Voice 3

This is a tensorflow implementation of DEEP VOICE 3: 2000-SPEAKER NEURAL TEXT-TO-SPEECH. For now, we are just focusing on single speaker synthesis.

Requirement

• Tensorflow >= 1.2
• Python >= 3.0

Dataset

The LJ Speech Dataset

Pre-process

Download and unzip the LJ Speech Dataset. Run:

python prepro.py

Note: Make sure that we have unzipped the dataset into the same foler of prepro.py.

After this, we would get three new folders:

├── dones          [New]
├── mags           [New]
├── mels           [New]
├── metadata.csv
├── README
└── wavs

Training

Training data is loaded from ./LJSpeech-1.0/metadata.csv, ./LJSpeech-1.0/mels, ./LJSpeech-1.0/dones, ./LJSpeech-1.0/mags as default. If we want to change the loading path, we could change the config in class Hyperparams.

To train the model, we use this command:

python train.py

Pre-trained Model

Currently, we can not get good result. However, we still provide our pre-trained model in case someone is interested in it.

Pre-trained Model.

Its attention figure is as follows:

All the attention figures generated at training are included in the pre-trained model zipped file.

File Description

• hyperparams.py: hyper parameters
• prepro.py: creates inputs and targets, i.e., mel spectrogram, magnitude, and dones.
• data_load.py
• utils.py: several custom operational functions.
• modules.py: building blocks for the networks.
• networks.py: encoder, decoder, and converter
• train.py: train
• synthesize.py: inference
• test_sents.txt: some test sentences in the paper.

Reference

Most of the code is borrowed from Kyubyong/deepvoice3.