While the Wilcoxon signed-rank test in general doesn't assume any distribution, most exact implementations are restricted to <50 samples (i.e. scipy). Above that a normal distribution is assumed to calculate approximate values. This raises two questions:
Why do exact calculation have such a hard and low limit?
How can you handle larger datasets that don't have a normal distribution and thus the approximation can't be used?