Tacotron 2

Implementation of Tacotron 2 TTS model in PyTorch

Usage

Note: don't forget to clone the repo with git clone --recurse-submodules

Setup

To launch and inference in nvidia-docker container follow these instructions:

0. Install nvidia-docker
1. Run ./docker-build.sh

Training

To launch training follow these instructions:

1. Set preferred configurations in config/config.yaml in particular you might want to set dataset path (it will be concatendated with data path in docker-train.sh)
2. In docker-run.sh change memory, memory-swap, shm-size, cpuset-cpus, gpus, and data volume to desired values
3. Set WANDB_API_KEY environment variable to your wandb key
4. Run ./docker-train.sh waveglow_model_path

Where:

• waveglow_model_path is a path to waveglow .pt model file. It can be downloaded here (Link from https://github.com/NVIDIA/waveglow)

All outputs including models will be saved to outputs dir.

Inference

To launch inference run the following command:

./docker-inference.sh model_path label_encoder_path waveglow_model_path device input_text

Where:

• model_path is a path to .ckpt model file
• label_encoder_path is a path to .pickle label encoder file. It is generated during training by fut_label_encoder.py script
• waveglow_model_path is a path to waveglow .pt model file. It can be downloaded here (Link from https://github.com/NVIDIA/waveglow)
• device is the device to inference on: either 'cpu', 'cuda' or cuda device number
• input_text is an input text for TTS

Predicted output wav and spectrogram will be saved in inferenced folder

Full example:

./docker-inference.sh ./last.ckpt ./le.pickle ../../Tacotron2/waveglow_256channels_universal_v5.pt cuda 'So, so what? I'\''m still a rock star I got my rock moves!'

Pretrained models

All pretrained files for inference (tacotron 2 checkpoint trained on LJSpeech, label encoder and waveglow checkpoint) can be downloaded here.