

When fine-tuning the Audio Spectrogram Transformer (AST) on your own dataset, it's recommended to take care of the input normalization (to make
sure the input has mean of 0 and std of 0.5). [ASTFeatureExtractor] takes care of this. Note that it uses the AudioSet
mean and std by default. You can check ast/src/get_norm_stats.py to see how
the authors compute the stats for a downstream dataset.
Note that the AST needs a low learning rate (the authors use a 10 times smaller learning rate compared to their CNN model proposed in the
PSLA paper) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task.