#####################################
params = tune_window_preprocessing_params(audio_dirs, params)
###################################################
# 2) Train a generative model on these syllables. #
###################################################
partition = get_window_partition(audio_dirs, roi_dirs, 1)
partition['test'] = partition['train']
num_workers = min(7, os.cpu_count() - 1)
loaders = get_fixed_window_data_loaders(partition, params, \
num_workers=num_workers, batch_size=128)
loaders['test'] = loaders['train']
model = VAE(save_dir=root)
model.train_loop(loaders, epochs=101, test_freq=None)
########################
# 3) Plot and analyze. #
########################
from ava.plotting.tooltip_plot import tooltip_plot_DC
from ava.plotting.latent_projection import latent_projection_plot_DC
from ava.plotting.trace_plot import warped_trace_plot_DC
loaders['test'].dataset.write_hdf5_files(spec_dirs[0], num_files=1000)
latent_projection_plot_DC(dc, alpha=0.25, s=0.5)
tooltip_plot_DC(dc, num_imgs=2000)
if __name__ == '__main__':
pass
###
##################
# 4) Preprocess. #
##################
n_jobs = os.cpu_count() - 1
gen = zip(audio_dirs, seg_dirs, spec_dirs, repeat(params['preprocess']))
Parallel(n_jobs=n_jobs)(delayed(process_sylls)(*args) for args in gen)
###################################################
# 5) Train a generative model on these syllables. #
###################################################
model = VAE(save_dir=root)
# model.load_state(root+'checkpoint_150.tar')
partition = get_syllable_partition(spec_dirs, split=1, max_num_files=2500)
num_workers = os.cpu_count() - 1
loaders = get_syllable_data_loaders(partition, num_workers=num_workers)
loaders['test'] = loaders['train']
model.train_loop(loaders, epochs=151, test_freq=None)
########################
# 6) Plot and analyze. #
########################
from ava.plotting.tooltip_plot import tooltip_plot_DC
from ava.plotting.latent_projection import latent_projection_plot_DC
latent_projection_plot_DC(dc)
tooltip_plot_DC(dc, num_imgs=2000)
if __name__ == '__main__':
pass
###