  The increasing complexity of modern deep neural network models and the
expanding sizes of datasets necessitate the development of optimized and
scalable training methods. In this white paper, we addressed the challenge of
efficiently training neural network models using sequences of varying sizes. To
address this challenge, we propose a novel training scheme that enables
efficient distributed data-parallel training on sequences of different sizes
with minimal overhead. By using this scheme we were able to reduce the padding
amount by more than 100$x$ while not deleting a single frame, resulting in an
overall increased performance on both training time and Recall in our
experiments.
