- collect some midi files and put them in the midi folder
- run prepare.py, we will count the frequency at which notes are pressed
- the model uses deep one-dimensional residual dense convolution network to extract time series characteristics
- the model predicts the state of 88 keys in 1/64 note time at a time
- so the generation process is shown in the following figure
- run train.py
- in the case of small amount of data, it is recommended that the sequence length be set to 128
- run predict.py
- give the model a short melody, which will produce a melody of similar style
- tensorflow-gpu 1.12.0
- Keras 2.2.4