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TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis

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TFGAN

This repo is Unofficial implements of TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis using Pytorch.

Requirement

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.
  • tensorboard --logdir logs/

Inference

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

Checkpoint :

  • LJSpeech checkpoint here .

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TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis

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