Conformer-VC

Conformer-VC is inspired by Non-autoregressive sequence-to-sequence voice conversion that is parallel voice conversion methods powered by conformer.

The differences between original paper are

• NOT using reduction-factor.
• Mel-spectrograms are not normalized by speaker statistics.
• Extract durations by DTW, not pretrained autoregressive model.
• Use HiFi-GAN instead of ParallelWaveGAN

Requirements

• pytorch
• numpy
• pyworld
• accelerate
• soundfile
• librosa
• cython
• omegaconf
• tqdm
• resemblyzer
• matplotlib
• scipy

If you get an error about the package, please install it.

Usage

1. Preprocess

If you wanna train your dataset, please rewrite configs/preprocess.yaml and preprocess.py properly.
Note that num of source files and num of tgt files must be same and file ids must be same.

$ cd dtw && python setup.py build_ext --inplace && cd ..
$ python prerprocess.py

2. Training

single gpu training

$ ln -s ./dataaset/feats DATA
$ python train.py

or multi gpus

$ ln -s ./dataaset/feats DATA
$ accelerate config

answer question of your machine.

$ accelerate launch train.py

3. Validation

$ python validate.py --model_dir {MODEL_DIR} --hifi_gan {HIFI_GAN_DIR} --data_dir DATA

if this script run correctly, outputs directory is generated and synthesized wav is in it.