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Audio to Face Blendshape

Implementation with PyTorch.

  • Base model
    • LSTM using MFCC audio features
    • CNN(ref simplified version) with LPC features

Prerequisites

  • Python3
  • PyTorch v0.3.0
  • numpy
  • librosa & audiolazy
  • scipy
  • etc.

Files

  • Scripts to run

    • main.py: change net name and set checkpoints folder to train different models
    • test_model.py: generate blendshape sequences given extracted audio features (need audio features as input)
    • synthesis.py: generate blendshape directly from input wav (need arguements of input audio path)

  • Classes

    • models.py: Classes with LSTM and CNN (simplified NvidiaNet) model.
    • models_testae.py: Advanced models with audoencoder design.
    • dataset.py: Class for loading dataset.

  • Input preprocessing

    • misc/audio_mfcc.py: extract mfcc features from input wav files
    • misc/audio_lpc.py: extract lpc features
    • misc/combine.py: combine certain audio feature/blendshape files to obtain a single file for data loading

Usage

Input

To build your own dataset, you need to preprocess your wav/blendshape pairs with misc/audio_mfcc.py or misc/audio_lpc.py. Then combine those feature/blendshape files misc/combine.py to a single feature/blendshape file.

Training

Modify main.py. Set model to the one you need and also specify checkpoint folder.

Evaluation

  • Both test_model.py and synthesis.py can be used to generate blendshape sequences.
    • test_model.py accepts extrated audio features (MFCC/LPC).
    • synthesis.py takes raw wav file as input
    • State the arguments and it will produce a blenshape test file.