synthesizer_train.py

from synthesizer.hparams import hparams
from synthesizer.train import tacotron_train
from utils.argutils import print_args
from synthesizer import infolog
import argparse
import os


def prepare_run(args):
    modified_hp = hparams.parse(args.hparams)
    os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(args.tf_log_level)
    run_name = args.name
    log_dir = os.path.join(args.models_dir, "logs-{}".format(run_name))
    os.makedirs(log_dir, exist_ok=True)
    infolog.init(os.path.join(log_dir, "Terminal_train_log"), run_name, args.slack_url)
    return log_dir, modified_hp


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("name", help="Name of the run and of the logging directory.")
    parser.add_argument("synthesizer_root", type=str, help=\
        "Path to the synthesizer training data that contains the audios and the train.txt file. "
        "If you let everything as default, it should be <datasets_root>/SV2TTS/synthesizer/.")
    parser.add_argument("-m", "--models_dir", type=str, default="synthesizer/saved_models/", help=\
        "Path to the output directory that will contain the saved model weights and the logs.")
    parser.add_argument("--mode", default="synthesis",
                        help="mode for synthesis of tacotron after training")
    parser.add_argument("--GTA", default="True",
                        help="Ground truth aligned synthesis, defaults to True, only considered "
							 "in Tacotron synthesis mode")
    parser.add_argument("--restore", type=bool, default=True,
                        help="Set this to False to do a fresh training")
    parser.add_argument("--warm_start_path", type=str, default=None,
                        help="Set to True if finetuning, need restore to be true too")
    parser.add_argument("--summary_interval", type=int, default=2500,
                        help="Steps between running summary ops")
    parser.add_argument("--embedding_interval", type=int, default=10000,
                        help="Steps between updating embeddings projection visualization")
    parser.add_argument("--checkpoint_interval", type=int, default=2000, # Was 5000
                        help="Steps between writing checkpoints")
    parser.add_argument("--eval_interval", type=int, default=100000, # Was 10000
                        help="Steps between eval on test data")
    parser.add_argument("--tacotron_train_steps", type=int, default=2000000, # Was 100000
                        help="total number of tacotron training steps")
    parser.add_argument("--tf_log_level", type=int, default=1, help="Tensorflow C++ log level.")
    parser.add_argument("--slack_url", default=None,
                        help="slack webhook notification destination link")
    parser.add_argument("--hparams", default="",
                        help="Hyperparameter overrides as a comma-separated list of name=value "
							 "pairs")
    args = parser.parse_args()
    print_args(args, parser)
    
    log_dir, hparams = prepare_run(args)
    
    tacotron_train(args, log_dir, hparams)

#python synthesizer_preprocess_embeds.py /mnt/data1/waris/datasets/data/arctic_dataset/all_data/SV2TTS/synthesizer
#python synthesizer_train.py Accetron_train /mnt/data1/waris/datasets/data/arctic_dataset/all_data/SV2TTS/synthesizer --models_dir /mnt/data1/waris/model_outputs/accentron/saved_models_gpu
#python synthesizer_train.py Accetron_train_parallel_vctk /mnt/data1/waris/datasets/data/arctic_dataset/all_data_for_ac_vc/SV2TTS/synthesizer --models_dir=/mnt/data1/waris/model_outputs/accentron/parallel