Use get_optimizer in Encoder

TTS/bin/train_encoder.py

@@ -20,7 +20,7 @@ from TTS.tts.datasets import load_tts_samples
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.generic_utils import count_parameters, remove_experiment_folder, set_init_dict
 from TTS.utils.io import load_fsspec, copy_model_files
-from TTS.utils.radam import RAdam
+from trainer.trainer_utils import get_optimizer
 from TTS.utils.training import check_update
 
 torch.backends.cudnn.enabled = True
@@ -244,7 +244,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     ap = AudioProcessor(**c.audio)
     model = setup_speaker_encoder_model(c)
 
-    optimizer = RAdam(model.parameters(), lr=c.lr, weight_decay=c.wd)
+    optimizer = get_optimizer(c.optimizer, c.optimizer_params, c.lr, model)
 
     # pylint: disable=redefined-outer-name
     meta_data_train, meta_data_eval = load_tts_samples(c.datasets, eval_split=True)

TTS/encoder/configs/base_encoder_config.py

@@ -32,9 +32,13 @@ class BaseEncoderConfig(BaseTrainingConfig):
     loss: str = "angleproto"
     grad_clip: float = 3.0
     lr: float = 0.0001
+    optimizer: str = "radam"
+    optimizer_params: Dict = field(default_factory=lambda: {
+        "betas": [0.9, 0.999],
+        "weight_decay": 0
+    })
     lr_decay: bool = False
     warmup_steps: int = 4000
-    wd: float = 1e-6
 
     # logging params
     tb_model_param_stats: bool = False

TTS/tts/configs/shared_configs.py

@@ -264,7 +264,7 @@ class BaseTTSConfig(BaseTrainingConfig):
     # dataset
     datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
     # optimizer
-    optimizer: str = None
+    optimizer: str = "radam"
     optimizer_params: dict = None
     # scheduler
     lr_scheduler: str = ""