Official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

Related tags

Deep Learning WadaIN-VC
Overview

One-Shot Voice Conversion with Weight Adaptive Instance Normalization

image

By Shengjie Huang, Yanyan Xu*, Dengfeng Ke*, Mingjie Chen, Thomas Hain.

This repo is the official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

Audio samples are available at here.

Dependencies

  • python 3.6.0
  • pytorch 1.4.0
  • pyyaml 5.4.1
  • numpy 1.19.5
  • librosa 0.8.0
  • soundfile 0.10.2
  • tensorboardX 2.1

Preprocess

What you need to prepare first before running this project and how to prepare them

  • We use the ParallelWaveGAN as our vocoder, and VCTK as our data set.

  • If you wanna run our project, please install as the description of ParallelWaveGAN project first.

  • And then prepare all the mel-spectrogram data as ParallelWaveGAN do.

  • Prepare the speaker_used.json file by yourself, as ./data/80_train_speaker_used.json and ./data/fine_tune_speaker_used.json show.

  • Prepare the feats.scp file by runing ./convert_decode/convert_mel/get_scp.py .

Assume that your prepared mel-spectrograms are sorted in the files tree like:

├── p225
│   ├── p225_001-feats.npy
│   ├── p225_004-feats.npy
│   ├── p225_005-feats.npy
│   ......
├── p226
│   ├── p226_001-feats.npy
│   ├── p226_003-feats.npy
│   ├── p226_004-feats.npy
│   ......
├── p227
│   ......
├── p228
│   ......
│   ...
│   ...

Training

Run the pretrain stage by bash run_main.sh. We use 80 speakers of VCTK data set, and all utterances for each person.

Fine Tuning

Run the fine tune stage by bash run_fine_tune.sh. We use the other 10 speakers of VCTK data set, and only 1 utterance for each person used.

Inference

$ cd convert_decode/convert_mel
$ bash run_convert.sh

We generate one-shot voice conversion utterances between the 10 one-shot speakers , and use their other unseen utterances to perform one-shot voice conversion!

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