def main(args):
# get model
model = load_obj(os.path.join(args.folder_model, 'model'))
feature_extractor = load_obj(os.path.join(args.folder_model, 'feature_extractor'))
if not os.path.exists(args.folder_out):
os.makedirs(args.folder_out)
dic_forward = dict()
dic_forward['model_used'] = args.folder_model
dic_forward['data_forwarded'] = args.folder_audio
with open(os.path.join(args.folder_out, 'forward_info.json'), 'w') as fp:
json.dump(dic_forward, fp)
# plot result
forward_model(args.folder_out,
args.folder_audio,
model,
feature_extractor)
def main(args):
# get model
with open(os.path.join(args.folder_forwarded, 'forward_info.json')) as config_description:
config_forward = ast.literal_eval(config_description.read())
values_possible = load_obj(os.path.join(config_forward['model_used'], 'values_possible'))
# plot result
plot_forwarded(args.folder_out,
config_forward['data_forwarded'],
os.path.join(args.folder_forwarded, 'clusters'),
values_possible,
args.freq_max)
