Video Tutorial at https://www.youtube.com/watch?v=MlZ6QfkhRF0

Infer a text file into multiple phrases that can be redone, edited, or removed.

Train Nvidia Tacotron2, Waveglow, and Hifi-Gan models.

"Easy" setup and usage for those new to deepvoice.

It is highly recommend to install anaconda and create a virtual environment to work from.

git clone this repository

https://github.com/youmebangbang/deepvoice-model-utilities.git

cd deepvoice-model-utilities

git clone https://github.com/NVIDIA/tacotron2.git

cd tacotron2

git submodule init

git submodule update

cd ..

git clone https://github.com/NVIDIA/waveglow.git

cd waveglow

git submodule init

git submodule update

cd ..

git clone https://github.com/jik876/hifi-gan.git

Install latest pip version of pytorch (CUDA binaries are inlcuded!): https://pytorch.org/get-started/locally/ Install the conda version if pip gives you errors about libcublas.so

pip install -r requirements.txt --user

*If you cannot build simpleaudio make sure that you have gcc installed: sudo apt-get update , sudo apt-get install build-essentials

Copy the tacotron2 and waveglow replace files into the installed directories

Entries are deliminated with the | character. No quotes around text. Split at least 50 entries from your training.csv into your validation.csv

For Tacotron 2:

[wave file name with extension] | [text of audio]

example: 120.wav|Here is the text of my audio. It is some good text.

For Waveglow:

[wave file name with extension]

example: 120.wav

For Hifi-GAN:

[wave file name without extension] | [text of audio]

example: 120|Here is the text of my audio. It is some good text.

(subject to change based on current knowledge)

For Tacotron 2: Start at 1e-4 until model collapses (NaN loss), then revert to last good checkpoint and use 1e-5 for remainder of training. If model collapses with 1e-5, revert back to the last good checkpoint and continue training.

For Waveglow: Use 1e-4 for entire training.

For Hifi-GAN: Use 2e-4 for entire training.