dpss-exp3-VC-BNF

Voice Conversion Experiments for THUHCSI Course : <Digital Processing of Speech Signals>

Set up environment

1. Install sox from http://sox.sourceforge.net/ or apt install sox

2. Install ffmpeg from https://www.ffmpeg.org/download.html#build-linux or apt install FFmpeg

3. Set up python environment through:

python3 -m venv /path/to/new/virtual/environment
source /path/to/new/virtual/environment/bin/activate
pip3 install -r dpss-exp3-VC-BNF/requirement_torch19.txt
# or you can use this if you prefer torch1.8 version
pip3 install -r dpss-exp3-VC-BNF/requirement_torch18.txt

Tips: You can also setup your own environment depends on cuda you have. We recommend that you use pytorch 1.9.0 with the corresponding cuda version to avoid bug.

Data Preparation

1. Download bzn/mst-male/mst-female corpus from here https://cloud.tsinghua.edu.cn/d/a30bef6b8d504e46bdb9/files/?p=%2Fsub_dataset.tar
2. Extract the dataset, and organize your data directories as follows:

dataset/
├── mst-female
├── mst-male
├── bzn

3. Download pretrained ASR model from here https://cloud.tsinghua.edu.cn/d/a30bef6b8d504e46bdb9/files/?p=%2Ffinal.pt
4. Move final.pt to ./pretrained_model/asr_model

Any-to-One Voice Conversion Model

Feature Extraction

CUDA_VISIBLE_DEVICES=0 python preprocess.py --data_dir /path/to/dataset/bzn --save_dir /path/to/save_data/bzn/

Your extracted features will be organized as follows:

bzn/
├── dev_meta.csv
├── f0s
│   ├── bzn_000001.npy
│   ├── ...
├── linears
│   ├── bzn_000001.npy
│   ├── ...
├── mels
│   ├── bzn_000001.npy
│   ├── ...
├── BNFs
│   ├── bzn_000001.npy
│   ├── ...
├── test_meta.csv
└── train_meta.csv

Tips: If you get 'Could not find a version for torch==1.9.0+cu111', run the following script to solve the problem. More details please refer to: https://jishuin.proginn.com/p/763bfbd5e54b.

pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

Train

If you have GPU (one typical GPU is enough, nearly 1s/batch):

CUDA_VISIBLE_DEVICES=0 python train_to_one.py --model_dir ./exps/model_dir_to_bzn --test_dir ./exps/test_dir_to_bzn --data_dir /path/to/save_data/bzn/

If you have no GPU (nearly 5s/batch):

python train_to_one.py --model_dir ./exps/model_dir_to_bzn --test_dir ./exps/test_dir_to_bzn --data_dir /path/to/save_data/bzn/

Inference

CUDA_VISIBLE_DEVICES=0 python inference_to_one.py --src_wav /path/to/source/xx.wav --ckpt ../exps/model_dir_to_bzn/bnf-vc-to-one-49.pt --save_dir ./test_dir/

Any-to-Many Voice Conversion Model

Feature Extraction

# In any-to-many VC task, we use all the above 3 speakers as the target speaker set.
CUDA_VISIBLE_DEVICES=0 python preprocess.py --data_dir /path/to/dataset/ --save_dir /path/to/save_data/exp3-data-all

Your extracted features will be organized as follows:

exp3-data-all/
├── dev_meta.csv
├── f0s
│   ├── bzn_000001.npy
│   ├── ...
├── linears
│   ├── bzn_000001.npy
│   ├── ...
├── mels
│   ├── bzn_000001.npy
│   ├── ...
├── BNFs
│   ├── bzn_000001.npy
│   ├── ...
├── test_meta.csv
└── train_meta.csv

Train

If you have GPU (one typical GPU is enough, nearly 1s/batch):

CUDA_VISIBLE_DEVICES=0 python train_to_many.py --model_dir ./exps/model_dir_to_many --test_dir ./exps/test_dir_to_many --data_dir /path/to/save_data/exp3-data-all

If you have no GPU (nearly 5s/batch):

python train_to_many.py --model_dir ./exps/model_dir_to_many --test_dir ./exps/test_dir_to_many --data_dir /path/to/save_data/exp3-data-all

Inference

# Here for inference, we use 'mst-male' as the target speaker. you can change the tgt_spk argument to any of the above 3 speakers. 
CUDA_VISIBLE_DEVICES=0 python inference_to_many.py --src_wav /path/to/source/*.wav --tgt_spk bzn/mst-female/mst-male --ckpt ./model_dir/bnf-vc-to-many-49.pt --save_dir ./test_dir/

Assignment requirements

This project is a vanilla voice conversion system based on BNFs.

When you encounter problems while finishing your project, search the issues first to see if there are similar problems. If there are no similar problems, you can create new issues and state you problems clearly.