UnivNet

UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation.

Training

python train.py --config config_c32.json

Citation

@misc{seo2021controlling,
      title={Controlling Neural Networks with Rule Representations}, 
      author={Sungyong Seo and Sercan O. Arik and Jinsung Yoon and Xiang Zhang and Kihyuk Sohn and Tomas Pfister},
      year={2021},
      eprint={2106.07804},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Note

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

References:

• Hi-Fi-GAN repo
• LVCNet repo

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 🧰 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]

correct training and inference execution for both univnet and hifigan still needs to be updated in the README