def test_preprocessing():
# make arguments
args = make_args()
# prepare dummy wav files
wavdir = "data/wav"
if not os.path.exists(wavdir):
os.makedirs(wavdir)
for i in range(5):
make_dummy_wav(wavdir + "/%d.wav" % i, 8000, args.fs)
# feature extract
wav_list = find_files(wavdir, "*.wav")
if not os.path.exists(args.wavdir):
os.makedirs(args.wavdir)
world_feature_extract(wav_list, args)
# calc_stats
file_list = find_files(args.hdf5dir, "*.h5")
calc_stats(file_list, args)
# noise shaping
wav_list = find_files(args.wavdir, "*.wav")
if not os.path.exists(args.writedir):
os.makedirs(args.writedir)
noise_shaping(wav_list, args)
# assert list length
wav_ns_list = find_files(args.writedir, "*.wav")
assert len(wav_list) == len(file_list)
assert len(wav_list) == len(wav_ns_list)
def test_preprocessing():
# make arguments
args = make_args()
# prepare dummy wav files
wavdir = "data/wav"
if not os.path.exists(wavdir):
os.makedirs(wavdir)
for i in range(5):
make_dummy_wav(wavdir + "/%d.wav" % i, 8000, args.fs)
# feature extract
wav_list = find_files(wavdir, "*.wav")
if not os.path.exists(args.wavdir):
os.makedirs(args.wavdir)
args.feature_type = "world"
world_feature_extract(wav_list, args)
args.feature_type = "melspc"
melspectrogram_extract(wav_list, args)
args.feature_type = "mcep"
melcepstrum_extract(wav_list, args)
# calc_stats
file_list = find_files(args.hdf5dir, "*.h5")
args.feature_type = "world"
calc_stats(file_list, args)
args.feature_type = "melspc"
calc_stats(file_list, args)
args.feature_type = "mcep"
calc_stats(file_list, args)
# noise shaping
wav_list = find_files(args.wavdir, "*.wav")
args.feature_type = "world"
args.writedir = "data/wav_ns/world"
if not os.path.exists(args.writedir):
os.makedirs(args.writedir)
world_noise_shaping(wav_list, args)
args.feature_type = "mcep"
args.writedir = "data/wav_ns/mcep"
if not os.path.exists(args.writedir):
os.makedirs(args.writedir)
melcepstrum_noise_shaping(wav_list, args)
def main():
parser = argparse.ArgumentParser(
description='Run storage task net experiments.')
parser.add_argument('--save',
type=str,
metavar='save-folder',
help='prefix to add to save path')
parser.add_argument('--nRuns',
type=int,
default=10,
metavar='runs',
help='number of runs')
parser.add_argument('--paramSet',
type=int,
choices=range(4),
default=0,
metavar='hyperparams',
help='(lambda, epsilon) in given row of Table 1')
args = parser.parse_args()
save_folder_main = 'params{}'.format(args.paramSet) if args.save is None \
else '{}-params{}'.format(args.save, args.paramSet)
save_folder_main = os.path.join('results', save_folder_main)
setproctitle.setproctitle('storage-{}'.format(args.paramSet))
# Initialize problem parameters
params = init_params(args.paramSet)
bsz = 500
# Train, test split
train_frac = 0.8
input_tensors = get_train_test_split(params, train_frac)
loaders = get_loaders_tt(input_tensors, bsz)
if not os.path.exists(save_folder_main):
os.makedirs(save_folder_main)
for run in range(args.nRuns):
save_folder = os.path.join(save_folder_main, str(run))
if not os.path.exists(save_folder):
os.makedirs(save_folder)
# Randomly construct hold-out set for task net training.
tensors_task = get_train_hold_split(input_tensors, 0.8, save_folder)
loaders_task = get_loaders_tth(tensors_task, bsz)
# Run and eval rmse-minimizing net
model_rmse = model_classes.Net(tensors_task['X_train'],
tensors_task['Y_train'], [200, 200],
params['T'])
if USE_GPU:
model_rmse = model_rmse.cuda()
model_rmse = nets.run_rmse_net(model_rmse, loaders_task, params,
tensors_task)
nets.eval_net('rmse_net', model_rmse, loaders_task, params,
save_folder)
# Run and eval task-minimizing net
model_task = model_classes.Net(tensors_task['X_train'],
tensors_task['Y_train'], [200, 200],
params['T'])
if USE_GPU:
model_task = model_task.cuda()
model_task = nets.run_rmse_net(model_task, loaders_task, params,
tensors_task) # seed with rmse soln
model_task = \
nets.run_task_net(model_task, loaders_task, params, args, tensors_task)
nets.eval_net('task_net', model_task, loaders_task, params,
save_folder)
calc_stats.calc_stats(
map(lambda x: os.path.join(save_folder_main, str(x)),
range(args.nRuns)), save_folder_main)