  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. 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. After implementation, the accuracy of each module was evaluated via
systematic block-by-block validation. 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. The results indicate improved speech quality metrics in the
DiffWave-enhanced output as compared to the baseline. This enhancement would
have been difficult or impossible to accomplish in the original Matlab
implementation. This proof-of-concept demonstrates the value TorchDIVA will
bring to the research community. Researchers can download the new
implementation at: https://github.com/skinahan/DIVA_PyTorch
