import matplotlib.pyplot as plt
from copy import deepcopy
from gantools import blocks
from audioinpainting.load import load_audio_dataset
from audioinpainting.model_basic import InpaintingGAN
# # Parameters
downscale = 2
# # Data handling
# Load the data
start = time.time()
# dataset = data.load.load_audio_dataset(scaling=downscale)
dataset = load_audio_dataset(scaling=downscale,
type='solo',
spix=1024 * 16,
augmentation=True)
print('Number of samples: {}'.format(dataset.N))
# =============================================================================
# # The dataset can return an iterator.
# it = dataset.iter(10)
# print(next(it).shape)
# del it
#
# # Get all the data
# X = dataset.get_all_data().flatten()
#
# plt.hist(X, 100)
# print('min: {}'.format(np.min(X)))
uu = 0 + aa
spix = 1024 * 52
signal_length = 1024 * 52
signal_split = [1024 * 18, 1024 * 6, 1024 * 4, 1024 * 6, 1024 * 18]
elif model == 'basic':
from audioinpainting.model_basic import InpaintingGAN
uu = 50 + bb
spix = 1024 * 52
signal_length = 1024 * 52
signal_split = [1024 * 24, 1024 * 4, 1024 * 24]
else:
raise ValueError(
'Incorrect model; choose either "extend" or "basic"')
dataset = load_audio_dataset(scaling=downscale,
type=type,
spix=spix,
augmentation=True)
# Check whether number of generated samples is consistent with total number of samples
if N_f > dataset.N:
N_f = dataset.N
print('Number of samples: {}'.format(dataset.N))
# ## Parameters
bn = False
md = 64
params_discriminator = dict()
params_discriminator['stride'] = [4, 4, 4, 4, 4]
params_discriminator['nfilter'] = [md, 2 * md, 4 * md, 8 * md, 16 * md]
params_discriminator['shape'] = [[25], [25], [25], [25], [25]]
import matplotlib.pyplot as plt
from copy import deepcopy
from gantools import blocks
from audioinpainting.load import load_audio_dataset
from audioinpainting.model_extend import InpaintingGAN
# # Parameters
downscale = 2
# # Data handling
# Load the data
start = time.time()
# dataset = data.load.load_audio_dataset(scaling=downscale)
dataset = load_audio_dataset(scaling=downscale, type='piano', spix=1024*52, augmentation=True)
print('Number of samples: {}'.format(dataset.N))
# =============================================================================
# # The dataset can return an iterator.
# it = dataset.iter(10)
# print(next(it).shape)
# del it
#
# # Get all the data
# X = dataset.get_all_data().flatten()
#
# plt.hist(X, 100)
# print('min: {}'.format(np.min(X)))