First we select five clips with melodic content from the Lakh MIDI test set: split f. Selected clips are stored to the qualify directory.
python melody-select.py $DATAPATH/lmd_full/f/ -o qualify -c 5 -s 1 -v
Then we generate accompaniments to these clips. We specify the reference midis (-d option) for the retrieval baseline.
python accompany.py qualify -r -d $DATAPATH/lmd_full/f/
We generate prompted completions using an autoregressive model (or an anticipatory autoregressive model) checkpoint stored at $MODELPATH.
First, we randomly select 50 prompts and completions from a collection of completions generated using the FIGARO Music Transformer (stored at $FIGARO). Store these prompts at $PROMPTPATH:
python figaro-select.py $FIGARO -o $PROMPTPATH -c 50 -s 999 -v
Generate completions using a model stored at $MODELPATH and store the results to $PROMPTPATH/$OUTPUT:
python prompt.py $PROMPTPATH $MODELPATH -o $OUTPUT -c 50 -v
Generate completions using an interarrival-time model:
python prompt-interarrival.py $PROMPTPATH $MODELPATH $OUTPUT -c 50 -v
We generate accompaniments using an anticipatory autoregressive model checkpoint stored at $MODELPATH.
First, select 50 clips with melodic content:
python melody-select.py $DATAPATH/lmd_full/f/ -o accompany -c 50 -v
Generate anticipatory accompaniments (-a flag):
python accompany.py accompany --model $MODELPATH -av -c 50
Generate the autoregressive baseline (-b flag):
python accompany.py accompany --model $MODELPATH -bv -c 50
Generate the retrieval baseline (-r flag):
python accompany.py accompany -d $DATAPATH/lmd_full/f/ -rv -c 50