TorchDIVA: An extensible computational model of speech production built on an open-source machine learning library.The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract.  We present TorchDIVA, a full rebuild of DIVA in Python using PyTorch tensors.  The TorchDIVA model is shown to produce outputs that closely match those of the original DIVA model, with a negligible difference between the two.  We additionally present an example of the extensibility of TorchDIVA as a research platform.  Speech quality enhancement in TorchDIVA is achieved through an integration with an existing PyTorch generative vocoder called DiffWave.  A modified DiffWave mel-spectrum upsampler was trained on human speech waveforms and conditioned on the TorchDIVA speech production.  This proof-of-concept demonstrates the value TorchDIVA can bring to the research community. 
TorchDIVA
The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python. 
Matlab Simulink
We present TorchDIVA, a full rebuild of DIVA in Python using PyTorch tensors. 
PyTorch tensors
The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python.  This means there is a wealth of machine learning tools which are freely available in the Python ecosystem that cannot be easily integrated with DIVA.  We present TorchDIVA, a full rebuild of DIVA in Python using PyTorch tensors.  DIVA source code was directly translated from Matlab to Python, and built-in Simulink signal blocks were implemented from scratch. 
Python
DIVA source code was directly translated from Matlab to Python, and built-in Simulink signal blocks were implemented from scratch. 
Simulink signal blocks
Speech quality enhancement in TorchDIVA is achieved through an integration with an existing PyTorch generative vocoder called DiffWave.  A modified DiffWave mel-spectrum upsampler was trained on human speech waveforms and conditioned on the TorchDIVA speech production.  The results indicate improved speech quality metrics in the DiffWave-enhanced output as compared to the baseline. 
DiffWave