def __init__(self,
path,
save_conversion_params=False,
to_list=False,
meta_prep_f=None):
super().__init__(path,
save_conversion_params=False,
to_list=to_list,
meta_prep_f=meta_prep_f)
song_iter = self.get_chords_together(with_metaData=False)
_, self.label_d = label([
chord for instruments in song_iter for s in instruments
for bar in s for chord in bar
],
start=0)
self.V = len(self.label_d)
if save_conversion_params:
self.conversion_params["chords"] = self.label_d
self.save_conversion_params(filename=save_conversion_params +
"/ChordGenerator.conversion_params")
self.melody_gen = MelodyGenerator(path,
save_conversion_params=False,
to_list=to_list,
meta_prep_f=None)
def __init__(self, path, save_conversion_params=True):
super().__init__(path, save_conversion_params=save_conversion_params)
song_iter = self.get_rhythms(with_metaData=False)
label_f, self.label_d = label(
[beat for s in song_iter for bar in s for beat in bar], start=0)
self.null_elem = ()
self.V = len(self.label_d)
self.conversion_params["rhythm"] = self.label_d
if self.save_params_eager:
self.save_conversion_params()
def __init__(self,
path,
save_conversion_params=True,
to_list=False,
meta_prep_f=None):
super().__init__(path,
save_conversion_params=save_conversion_params,
to_list=to_list,
meta_prep_f=meta_prep_f)
song_iter = self.get_rhythms_together(with_metaData=False)
label_f, self.label_d = label([
beat for instruments in song_iter for s in instruments for bar in s
for beat in bar
],
start=0)
self.null_elem = ()
self.V = len(self.label_d)
self.conversion_params["rhythm"] = self.label_d
if self.save_params_eager:
self.save_conversion_params()
from Data.DataGenerators import RhythmGenerator
from Nets.RhythmNetwork import RhythmNetwork
import pickle
import numpy.random as rand
from Data.utils import label
#%%
with open("bach_beats.pkl", "rb") as handle:
data = pickle.load(handle)
data = rand.permutation(data)
parts = [list(map(tuple, part)) for score in data for part in score]
_, label_d = label([b for p in parts for b in p], start=1)
pad_symb = "<s>"
label_d[pad_symb] = 0
#%%
r = RhythmGenerator(4, label_d, "<s>")
r_gen = r.generate_data(parts, shuffle=False)
#%%
rnet = RhythmNetwork(num_categories=len(label_d),
embed_size=32,
lstm_size=64)
#%%
rnet.fit_generator(r_gen, steps_per_epoch=len(parts), epochs=1)
