AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

Modified by 22601 Casper Wang, 22625 Sean Liu @ CKHS with lots of help from Inventec

This repository provides a PyTorch implementation of AUTOVC.

AUTOVC is a many-to-many non-parallel voice conversion framework.

To ensure respect for privacy rights and responsible use of our code, we are only releasing a portion of our code to allow users to convert voices among a predefined set of speakers in VCTK. Conversions from and to other voices have been disabled.

Audio Demo

The audio demo for AUTOVC can be found here

Dependencies

• Python 3
• Numpy
• PyTorch >= v0.4.1
• TensorFlow >= v1.3 (only for tensorboard)
• librosa
• tqdm
• wavenet_vocoder pip install wavenet_vocoder for more information, please refer to https://github.com/r9y9/wavenet_vocoder

Pre-trained models

AUTOVC	WaveNet Vocoder
link	link

0.Converting Mel-Spectrograms

Download pre-trained AUTOVC model, and run the conversion.ipynb in the same directory.

1.Mel-Spectrograms to waveform

Download pre-trained WaveNet Vocoder model, and run the vocoder.ipynb in the same the directory.

Modified Stuff

Dataset:

Chinese dataset taken from https://www.data-baker.com/open_source.html, about 12 hours of Mandarin Chinese spoken by the same woman.

Current Issues

Cannot do anything with CPU as laptops do not have GPU :(, keep on raising error: RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU., I've tried to modify whatever it tells me to do, but it seems to be to no avail :(