StarGAN-ZSVC: Unofficial PyTorch Implementation
This repository is an unofficial PyTorch implementation of StarGAN-ZSVC by Matthew Baas and Herman Kamper. This repository provides both model architectures and the code to inference or train them.
One of the StarGAN-ZSVC advantages is that it works on zero-shot settings and can be trained on unparallel audio data (different audio content by different speakers). Also, the model inference time is real-time or faster.
Disclaimer: I implement this repository for educational purpose only. All credits go to the original authors. Also, it may contains different details as described in the paper. If there is a room for improvement, please feel free to contact me.
Set up
git clone [email protected]:Top34051/stargan-zsvc.git
cd stargan-zsvc
conda env create -f environment.yml
conda activate stargan-zsvc
Usage
Voice conversion
Given two audio files, source.wav and target.wav, you can generate a new audio file with the same speaking content as in source.wav spoken by the speaker in target.wav as follow.
First, load my pretrained model weights (best.pt) and put it in checkpoints folder.
Next, we need to embed both speaker identity.
python embed.py --path path_to_source.wav --name src
python embed.py --path path_to_target.wav --name trg
This will generate src.npy and trg.npy, the source and target speaker embeddings.
To perform voice conversion,
python convert.py \
--audio_path path_to_source.wav \
--src_id src \
--trg_id trg
That's it! :tada: You can check out the result at results/output.wav.
Training
To train the model, you have to download and preprocess the dataset first. Since your data might be different from mine, I recommend you to read and fix the logic I used in preprocess.py (the dataset I used is here).
The fixed size utterances from each speaker will be extracted, resampled to 22,050 Hz, and converted to Mel-spectrogram with window and hop length of size 1024 and 256. This will preprocess the speaker embeddings as well, so that you don't have to embed them one-by-one.
The processed dataset will look like this
data/
train/
spk1.npy # contains N samples of (80, 128) mel-spectrogram
spk2.npy
...
test/
spk1.npy
spk2.npy
...
embeddings/
spk1.npy # a (256, ) speaker embedding vector
spk2.npy
...
You can customize some of the training hyperparameters or select resuming checkpoint in config.json. Finally, train the models by
python main.py \
--config_file config.json
--num_epoch 3000
You will now see new checkpoint pops up in the checkpoints folder.
Please check out my code and modify them for improvement. Have fun training! :v:
