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All Projects β†’ rishikksh20 β†’ melgan

rishikksh20 / melgan

Licence: BSD-3-Clause license
MelGAN implementation with Multi-Band and Full Band supports...

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text-to-speechspeechspeech-synthesisvocodermelganmelgan-stftmulti-band-melganfull-band-melgan

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Multi-band MelGAN and Full band MelGAN

Unofficial PyTorch implementation of Multi-Band MelGAN paper. This implementation uses Seungwon Park's MelGAN repo as a base and PQMF filters implementation from this repo.
MelGAN :
Multi-band MelGAN:

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.
    • For Multi-Band training use config/mb_melgan config file in -c
  • tensorboard --logdir logs/

Pretrained model

Check out here.

Inference

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

Results

Open In Colab

References

License

BSD 3-Clause License.

Useful resources

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