This is an Oneflow implementation of the paper One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization. By separately learning speaker and content representations, we can achieve one-shot VC by only one utterance from source speaker and one utterace from target speaker. You can download the pretrain model from here and the coresponding normalization parameters for inference from here.

Our code is inspired by the Pytorch implementation adaptive_voice_conversion.

• python 3.6+
• oneflow 0.5.0
• numpy 1.16.0
• librosa 0.6.3
• SoundFile 0.10.2 We also use some preprocess script from Kyubyong/tacotron and magenta/magenta/models/gansynth.

We make use of VCTK dataset, which can be downloaded from CSTR VCTK Corpus

bash preprocess/preprocess_vctk.sh

The preprocess code is at preprocess/.The configuation for preprocessing is at preprocess/vctk.config. where:

• segment_size is the segment size for training. Default: 128
• data_dir is the directory to put preprocessed files.
• raw_data_dir is the directory to put the raw data.
• n_out_speakers is the number of speakers for testing. Default: 20.
• test_prop is the proportion for validation utterances. Default: 0.1
• training_samples is the number of sampled segments for training (we sample it in the preprocess stage). Default: 10000000.
• testing_samples is the number of sampled segments for testing. Default: 10000.
• n_utt_attr is the number of utterances to compute mean and standard deviation for normalization. Default: 5000.
• train_set: only for LibriTTS. The subset used for training. Default: train-clean-100.
• test_set: only for LibriTTS. The subset used for testing. Default: dev-clean.

Once you edited the config file, you can run preprocess_vctk.sh to preprocess the dataset.
Also, you can change the feature extraction config in preprocess/tacotron/hyperparams.py

bash train.sh

The default arguments can be found in train.sh. The usage of each arguments are listed below.

• -c: the path of config file, the default hyper-parameters can be found at config.yaml.
• -iters: train the model with how many iterations. default: 200000
• -summary_steps: record training loss every n steps.
• -train_set: the data file for training (train if the file is train.pkl). Default: train
• -train_index_file: the name of training index file. Default: train_samples_128.json
• -data_dir: the directory for processed data.
• -store_model_path: the path to store the model.

bash infer.sh

• -c: the path of config file.
• -m: the path of model checkpoint.
• -a: the attribute file for normalization ad denormalization.
• -s: the path of source file (.wav).
• -t: the path of target file (.wav).
• -o: the path of output converted file (.wav).

Ju-chieh Chou, Cheng-chieh Yeh, Hung-yi Lee. One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization[J]. arXiv preprint arXiv:1904.05742, 2019.