help='experiment case name of train1')
parser.add_argument('case2',
type=str,
help='experiment case name of train2')
parser.add_argument('-ckpt', help='checkpoint to load model.')
parser.add_argument('-gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('-r',
action='store_true',
help='start training from the beginning.')
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
args = get_arguments()
print(args.case2)
hp.set_hparam_yaml(args.case2,
default_file='hparams/{}.yaml'.format(args.case2))
logdir_train1 = '{}/{}/train1'.format(hp.logdir_path, args.case1)
logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2)
if args.r:
remove_all_files(logdir_train2)
print('case1: {}, case2: {}, logdir1: {}, logdir2: {}'.format(
args.case1, args.case2, logdir_train1, logdir_train2))
train(args, logdir1=logdir_train1, logdir2=logdir_train2)
print("Done")
return not_converted
def preprocessing(dataset_path, isConverting=False):
s = datetime.datetime.now()
wav_files = glob.glob(dataset_path)
dataset_path = dataset_path.replace('WAV', 'npz')
npz_files = glob.glob(dataset_path)
if len(npz_files) is 0:
generate_npz(wav_files)
else:
convert_list = matching_list(wav_files, npz_files)
if len(convert_list) is 0:
print('All WAV files in dataset directory are already converted!')
else:
generate_npz(convert_list)
e = datetime.datetime.now()
diff = e - s
print("Done. elapsed time:{}s".format(diff.seconds))
if __name__ == '__main__':
hp.set_hparam_yaml("TIMIT2")
data_path = "/home/cocoonmola/datasets/TIMIT2/TRAIN/*/*/*.WAV"
preprocessing(data_path)
type=str,
help='experiment case name of train1')
parser.add_argument('case2',
type=str,
help='experiment case name of train2')
parser.add_argument('-ckpt', help='checkpoint to load model.')
parser.add_argument('-gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('-r',
action='store_true',
help='start training from the beginning.')
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
args = get_arguments()
hp.set_hparam_yaml(args.case2)
logdir_train1 = '{}/{}/train1'.format(hp.logdir_path, args.case1)
logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2)
if args.r:
remove_all_files(logdir_train2)
print('case1: {}, case2: {}, logdir1: {}, logdir2: {}'.format(
args.case1, args.case2, logdir_train1, logdir_train2))
print('dataset : {}'.format(hp.train2.data_path))
train(args, logdir1=logdir_train1, logdir2=logdir_train2)
print("Done")
def ckpt2mel(predictor, ppgs_dir, mel_dir, save_dir):
print("get into ckpt")
for fi in os.listdir(ppgs_dir):
print("fi",fi)
#ppgs_name = os.path.join(ppgs_dir, fi)
mel, ppgs = queue_input(fi, ppgs_dir, mel_dir)
pred_mel = predictor(mel, ppgs)
#print("pred_mel",pred_mel.size())
pred_mel = np.array(pred_mel)
print("pred_mel",pred_mel.shape)
length = pred_mel.shape[2]
width = pred_mel.shape[3]
pred_mel = pred_mel.reshape((length, width))
save_name = fi.split('.npy')[0]
if hp.default.n_mels == 20:
npy_dir = os.path.join(save_dir,'lpc20')
if not os.path.exists(npy_dir):
os.makedirs(npy_dir)
npy_path = os.path.join(npy_dir, '%s_20.npy' %save_name)
np.save(npy_path, pred_mel)
print('saved',npy_dir)if hp.default.n_mels == 32:
npy_dir = os.path.join(save_dir,'lpc32')
if not os.path.exists(npy_dir):
os.makedirs(npy_dir)
npy_path = os.path.join(npy_dir, '%s_32.npy' %save_name)
np.save(npy_path, pred_mel)
print('saved',npy_dir)def do_convert(args, logdir2):
# Load graph
model = Net2()
index = 0
ppgs_dir = hp.convert.ppgs_path
mel_dir = hp.convert.mel_path
#for fi in os.listdir(ppgs_dir):
#print("fi",fi)
#ppgs_path = os.path.join(ppgs_dir, fi)
#df = Net2DataFlow(hp.convert.mel_path, ppgs_path, hp.convert.batch_size)
#print("finish df")
ckpt2 = '{}/{}'.format(logdir2, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
print("ckpt2",ckpt2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
pred_conf = PredictConfig( model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
print("after predictor")
#import pdb;pdb.set_trace()
ckpt2mel(predictor, ppgs_dir, mel_dir, hp.convert.save_path)
print("success")
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('case2', type=str, help='experiment case name of train2')
parser.add_argument('-ckpt', help='checkpoint to load model.')
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
args = get_arguments()
hp.set_hparam_yaml(args.case2)
logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2)
print('case2: {},logdir2: {}'.format(args.case2, logdir_train2))
s = datetime.datetime.now()
do_convert(args, logdir2=logdir_train2)
e = datetime.datetime.now()
diff = e - s
print("Done. elapsed time:{}s".format(diff.seconds))