FairTraxx / wav2pix_python3.x

A GAN based approach to predict facial features from a sample of a human's voice

Using GANs to predict faces from voices

About this repo

This repo is based on wav2pix, However it was optimized for the following:

• Python 3.x (previously 2.7 only)
• CUDA 10 (previously CUDA 9 or below)
• Google Colab
• All the packages from the original wav2pix have been changed, replaced or updated (including PyTorch) check requirements.txt for more info

this project was used as baseline.

Introduction

Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker. In this work, we explore its potential to generate face images of a speaker by conditioning a Generative Adversarial Network (GAN) with raw speech input. We propose a deep neural network that is trained from scratch in an end-to-end fashion, generating a face directly from the raw speech waveform without any additional identity information (e.g reference image or one-hot encoding). Our model is trained in a self-supervised fashion by exploiting the audio and visual signals naturally aligned in videos. With the purpose of training from video data, we present a novel dataset collected for this work, with high-quality videos of ten youtubers with notable expressiveness in both the speech and visual signals.

Training

python runtime.py

Arguments:

• lr_D : The learning rate of the disciminator. default = 0.0004
• lr_G : The learning rate of the generator. default = 0.0001
• vis_screen : The visdom env name for visualization. default = gan
• save_path : Name of the directory (inside checkpoints) where the parameters of them odel will be stored.
• l1_coef : L1 loss coefficient in the generator loss fucntion. default=50
• l2_coef : Feature matching coefficient in the generator loss fucntion. default=100
• pre_trained_disc : Discriminator pre-tranined model path used for intializing training.
• pre_trained_gen : Generator pre-tranined model path used for intializing training.
• batch_size : Batch size. default= 64
• num_workers: Number of dataloader workers used for fetching data. default = 8
• epochs : Number of training epochs. default=200
• softmax_coef: Paramete for the scale of the loss of the classifier on top of the embedding
• image_size : Number of pixels per dimension. They are assumed to be squared. Two possible values: 64 | 128. default = 64
• inference : Boolean for choosing whether train or test. default = False