/HiFi-GAN

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

Primary LanguagePythonMIT LicenseMIT

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

Unofficial PyTorch implementation of HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis.
HiFi-GAN :


Note

  • For more complete and end to end Voice cloning or Text to Speech (TTS) toolbox šŸ§° please visit Deepsync Technologies.

Prerequisites

Tested on Python 3.6

pip install -r requirements.txt

Prepare Dataset

  • Download dataset for training. This can be any wav files with sample rate 22050Hz. (e.g. LJSpeech was used in paper)
  • preprocess: python preprocess.py -c config/default.yaml -d [data's root path]
  • Edit configuration yaml file

Train & Tensorboard

  • python trainer.py -c [config yaml file] -n [name of the run]
    • cp config/default.yaml config/config.yaml and then edit config.yaml
    • Write down the root path of train/validation files to 2nd/3rd line.
    • Each path should contain pairs of *.wav with corresponding (preprocessed) *.mel file.
    • The data loader parses list of files within the path recursively.
  • tensorboard --logdir logs/

Pretrained model

Check here.

Inference

  • python inference.py -p [checkpoint path] -i [input mel path]