UUVC

Official implementation for the paper A Unified One-Shot Prosody and Speaker Conversion System with Self-Supervised Discrete Speech Units.
Submitted to ICASSP 2023.
Audio samples/demo for our system can be accessed here

Setup environment

See setup.sh for details of package installation (especially if you have problem on installing textless-lib). I used 1.12.1 for pytorch and 1.7.7 for pytorch lightning. No guarantee for the compatability of other versions.

Get Vocoder

We use the pretrained HiFi-GAN vocoder. Download the Universal-V1 version from the repo and put the checkpoints at vocoder/cp_hifigan/

Dataset preprocessing (for training)

Here we show an example of preprocessing VCTK, which you can adjust to your own dataset.

Download VCTK dataset from here
Run the below commands to preprocess speech units, pitch, 22k melspectrogram, energy, 16k-resampled speech:
```
mkdir -p features/VCTK
python s2u.py --VCTK --datadir VCTK_DIR --outdir features/VCTK --with_pitch_unit
```
The operation is time-consuming due to iterative pitch extraction from textless-lib.
Run the below command to generate training and validation splits, you can adjust the script to your own dataset:
```
mkdir datasets
python make_data_vctk.py
```
This will output two file: datasets/train_vctk.txt and datasets/valid_vctk.txt contains filelists for training and validation.

Training

We provide the below command as an example, change the arguments according to your dataset:

mkdir ckpt
python train.py --saving_path ckpt/ \
                --training_step 70000 \
                --batch_size 200 \
                --check_val_every_n_epoch 5 \
                --traintxt datasets/train_vctk.txt \
                --validtxt datasets/valid_vctk.txt \
                [--distributed]

--distributed if you are training with multiple GPUs
--check_val_every_n_epoch: Eval every n epoch
--training_step: Total training step (generator + discriminator)

Tensorboard logging will be in logs/RV or LOG_DIR/RV if you specify --logdir LOG_DIR.

Inference

We provide examples for synthesis of the system in inference.py, you can adjust this script to your own usage. Example to run inference.py:

python inference.py --result_dir ./samples --ckpt CKPT_PATH --config CONFIG_PATH --metapath META_PATH

--ckpt: .ckpt file that is generated during training, or from the pretrained checkpoints
--config: .json file that is generated at the start of the training, or from the pretrained checkpoints
--result_dir: Your desired output directory for the samples, will create subdirectory for different conversions
--metapath: The txt file contains the source and target speech paths, see eval.txt for an example.

The filenames will be {source_wav_name}--{target_wav_name}.wav. For examples of passing original pitch, energy instead of reconstructed, see inference_exact_pitch.py with the same arguments.

Pretrained checkpoints

We provide checkpoints pretrained sperately on VCTK and (LibriTTS-360h + VCTK + ESD). The model is a little bit large since it contains all the training and optimizer states.

For ethical concerns, the discriminator is also in the checkpoint to distinguish fake from true speech.

b04901014 / UUVC

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