def main():
args = parse()
save_model = Saver(args.modelfolder)
use_cuda = args.cuda
model_G = SpeechEggEncoder()
model_D = Discriminator()
model_G, model_D, _, _, _, _ = save_model.load_checkpoint(
model_G, model_D, file_name=args.modelfile)
speechfiles = glob(os.path.join(args.speechfolder, "*.npy"))
eggfiles = glob(os.path.join(args.eggfolder, "*.npy"))
reconstruction_save_path = args.outputfolder
test_data = AudioFileDataset(speechfiles,
eggfiles,
args.window,
args.stride,
transform=detrend)
test_dataloader = DataLoader(test_data, 1, num_workers=4, shuffle=False)
os.makedirs(reconstruction_save_path, exist_ok=True)
for egg_reconstructed, f in test(model_G,
model_D,
test_dataloader,
args.window,
args.stride,
use_cuda=use_cuda):
outputfile = os.path.join(reconstruction_save_path, f[0])
np.save(outputfile, egg_reconstructed)
def main():
test_data = create_dataloader(
64,
"CMU_new/bdl_test/speech",
"CMU_new/bdl_test/egg_detrended",
# "Childers/M_test/speech",
# "Childers/M_test/egg_detrended",
# "Temp1/speech",
# "Temp1/egg_detrended",
200,
200,
select=1,
)
# save_model = Saver('Models/DotModel/Childers_clean')
save_model = Saver("checkpoints/clean300")
use_cuda = True
model_G = SpeechEggEncoder()
model_D = Discriminator()
model_G, model_D, _, _, _, _ = save_model.load_checkpoint(
model_G, model_D, file_name="bce_epoch_45.pt")
test(model_G, model_D, test_data, use_cuda=use_cuda)
