def run_custom(finetuned_model_name, pretrained_model_name, syn_files_dir,
finetuned_syn_model_dir, gpu_id):
class args_cls:
def __init__(self, name, synthesizer_root, module_name, gpu_id):
self.mode = "synthesis"
self.GTA = "True"
self.restore = True
self.name = name
self.synthesizer_root = synthesizer_root
self.module_name = module_name
self.gpu_id = gpu_id
self.tacotron_train_steps = 750000
self.checkpoint_interval = 2000
self.summary_interval = 2500
self.embedding_interval = 10000
self.eval_interval = 100000
self.tf_log_level = 1
args = args_cls(finetuned_model_name, syn_files_dir, pretrained_model_name,
gpu_id)
log_dir = finetuned_syn_model_dir
tacotron_train(args, log_dir, hparams)
def 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("--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)
default=1000, # Was 5000
help="Steps between writing checkpoints")
parser.add_argument(
"--eval_interval",
type=int,
default=100, # Was 10000
help="Steps between eval on test data")
parser.add_argument(
"--tacotron_train_steps",
type=int,
default=500000, # 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 json string, for example: '\"key1\":123,\"key2\":true'"
)
args = parser.parse_args()
print_args(args, parser)
log_dir, hparams = prepare_run(args)
tacotron_train(args, log_dir, hparams)
