parser = argparse.ArgumentParser()
parser.add_argument("--tune_period", type=int, default=100, help="How many steps per learning rate.")
parser.add_argument("--checkpoint", type=str, default="-1", help="The checkpoint in the pre-trained model. The default is to load the BEST checkpoint (according to valid_loss)")
parser.add_argument("--config", type=str, help="The configuration file.")
parser.add_argument("train_dir", type=str, help="The data directory of the training set.")
parser.add_argument("train_spklist", type=str, help="The spklist file maps the TRAINING speakers to the indices.")
parser.add_argument("valid_dir", type=str, help="The data directory of the validation set.")
parser.add_argument("valid_spklist", type=str, help="The spklist maps the VALID speakers to the indices.")
parser.add_argument("pretrain_model", type=str, help="The pre-trained model directory.")
parser.add_argument("finetune_model", type=str, help="The fine-tuned model directory")
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
args = parser.parse_args()
params = save_codes_and_config(False, args.finetune_model, args.config)
# Load the pre-trained model to the target model directory.
# The pre-trained model will be copied as the fine-tuned model and can be loaded from the new directory.
# The pre-trained model is now just like an initialized model.
get_pretrain_model(os.path.join(args.pretrain_model, "nnet"),
os.path.join(args.finetune_model, "nnet"),
args.checkpoint)
# The model directory always has a folder named nnet
model_dir = os.path.join(args.finetune_model, "nnet")
# Set the random seed. The random operations may appear in data input, batch forming, etc.
tf.set_random_seed(params.seed)
random.seed(params.seed)
np.random.seed(params.seed)
from dataset.data_loader import KaldiDataRandomQueue
from dataset.kaldi_io import FeatureReader
parser = argparse.ArgumentParser()
parser.add_argument("data_dir",
type=str,
help="The data directory of the dataset.")
parser.add_argument("data_spklist",
type=str,
help="The spklist maps the speakers to the indices.")
parser.add_argument("model", type=str, help="The output model directory.")
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
args = parser.parse_args()
params = save_codes_and_config(True, args.model, None)
# The model directory always has a folder named nnet
model_dir = os.path.join(args.model, "nnet")
# Set the random seed. The random operations may appear in data input, batch forming, etc.
tf.set_random_seed(params.seed)
random.seed(params.seed)
np.random.seed(params.seed)
dim = FeatureReader(args.data_dir).get_dim()
if "selected_dim" in params.dict:
dim = params.selected_dim
with open(args.data_spklist, 'r') as f:
num_total_train_speakers = len(f.readlines())
parser.add_argument("train_data_dir",
type=str,
help="The data directory of the training set.")
parser.add_argument("train_ali_dir",
type=str,
help="The ali directory of the training set.")
parser.add_argument(
"train_spklist",
type=str,
help="The spklist file maps the TRAINING speakers to the indices.")
parser.add_argument("model", type=str, help="The output model directory.")
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
args = parser.parse_args()
params = save_codes_and_config(False, args.model, args.config)
# The model directory always has a folder named nnet
model_dir = os.path.join(args.model, "nnet")
# Set the random seed. The random operations may appear in data input, batch forming, etc.
tf.set_random_seed(params.seed)
random.seed(params.seed)
np.random.seed(params.seed)
start_epoch = 0
feat_reader = FeatureReaderV2(args.train_data_dir, args.train_ali_dir)
dim = feat_reader.get_dim()
feat_reader = KaldiDataRandomQueueV2(args.train_data_dir,
args.train_ali_dir,
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--cont", action="store_true", help="Continue training from an existing model.")
parser.add_argument("--config", type=str, help="The configuration file.")
parser.add_argument("train_dir", type=str, help="The data directory of the training set.")
parser.add_argument("train_spklist", type=str, help="The spklist file maps the TRAINING speakers to the indices.")
parser.add_argument("valid_dir", type=str, help="The data directory of the validation set.")
parser.add_argument("valid_spklist", type=str, help="The spklist maps the VALID speakers to the indices.")
parser.add_argument("model", type=str, help="The output model directory.")
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
args = parser.parse_args()
params = save_codes_and_config(args.cont, args.model, args.config)
# The model directory always has a folder named nnet
model_dir = os.path.join(args.model, "nnet")
# Set the random seed. The random operations may appear in data input, batch forming, etc.
tf.set_random_seed(params.seed)
random.seed(params.seed)
np.random.seed(params.seed)
if args.cont:
# If we continue training, we can figure out how much steps the model has been trained,
# using the index of the checkpoint
import re
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
action="store_true",
help="About whether to continue training.")
parser.add_argument("--config", type=str, help="The configuration file.")
parser.add_argument("train_dir",
type=str,
help="The data directory of the training set.")
parser.add_argument(
"train_spklist",
type=str,
help="The spklist file maps the TRAINING speakers to the indices.")
parser.add_argument("model", type=str, help="The output model directory.")
if __name__ == '__main__':
args = parser.parse_args()
params = save_codes_and_config(args.continue_training, args.model,
args.config)
model_dir = os.path.join(args.model, "nnet")
os.environ['CUDA_VISIBLE_DEVICES'] = params.gpu_id
torch.manual_seed(params.random_seed)
np.random.seed(params.random_seed)
random.seed(params.random_seed)
dim = FeatureReader(args.train_dir).get_dim()
with open(os.path.join(model_dir, "feature_dim"), 'w') as f:
f.write("%d\n" % dim)
num_total_train_speakers = KaldiDataRandomQueue(
args.train_dir, args.train_spklist).num_total_speakers # 训练说话人数目
