use Adam for wavegras instead of RAdam

TTS/bin/train_wavegrad.py

@@ -6,15 +6,19 @@ import time
 import traceback
 
 import torch
+# DISTRIBUTED
+from apex.parallel import DistributedDataParallel as DDP_apex
+from torch.nn.parallel import DistributedDataParallel as DDP_th
+from torch.optim import Adam
 from torch.utils.data import DataLoader
-
+from torch.utils.data.distributed import DistributedSampler
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.console_logger import ConsoleLogger
+from TTS.utils.distribute import init_distributed
 from TTS.utils.generic_utils import (KeepAverage, count_parameters,
                                      create_experiment_folder, get_git_branch,
                                      remove_experiment_folder, set_init_dict)
 from TTS.utils.io import copy_config_file, load_config
-from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.training import setup_torch_training_env
 from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
@@ -22,13 +26,6 @@ from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset
 from TTS.vocoder.utils.generic_utils import plot_results, setup_generator
 from TTS.vocoder.utils.io import save_best_model, save_checkpoint
 
-# DISTRIBUTED
-from apex.parallel import DistributedDataParallel as DDP_apex
-from torch.nn.parallel import DistributedDataParallel as DDP_th
-from torch.utils.data.distributed import DistributedSampler
-from TTS.utils.distribute import init_distributed
-
-
 use_cuda, num_gpus = setup_torch_training_env(True, True)
 
 
@@ -329,7 +326,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     model = setup_generator(c)
 
     # setup optimizers
-    optimizer = RAdam(model.parameters(), lr=c.lr, weight_decay=0)
+    optimizer = Adam(model.parameters(), lr=c.lr, weight_decay=0)
 
     # DISTRIBUTED
     if c.apex_amp_level is not None: