Update BaseTrainingConfig

TTS/config/shared_configs.py

@@ -2,6 +2,7 @@ from dataclasses import asdict, dataclass
 from typing import List
 
 from coqpit import Coqpit, check_argument
+from trainer import TrainerConfig
 
 
 @dataclass
@@ -237,130 +238,24 @@ class BaseDatasetConfig(Coqpit):
 
 
 @dataclass
-class BaseTrainingConfig(Coqpit):
+class BaseTrainingConfig(TrainerConfig):
-    """Base config to define the basic training parameters that are shared
+    """Base config to define the basic 🐸TTS training parameters that are shared
-    among all the models.
+    among all the models. It is based on ```Trainer.TrainingConfig```.
 
     Args:
         model (str):
             Name of the model that is used in the training.
 
-        run_name (str):
-            Name of the experiment. This prefixes the output folder name. Defaults to `coqui_tts`.
-
-        run_description (str):
-            Short description of the experiment.
-
-        epochs (int):
-            Number training epochs. Defaults to 10000.
-
-        batch_size (int):
-            Training batch size.
-
-        eval_batch_size (int):
-            Validation batch size.
-
-        mixed_precision (bool):
-            Enable / Disable mixed precision training. It reduces the VRAM use and allows larger batch sizes, however
-            it may also cause numerical unstability in some cases.
-
-        scheduler_after_epoch (bool):
-            If true, run the scheduler step after each epoch else run it after each model step.
-
-        run_eval (bool):
-            Enable / Disable evaluation (validation) run. Defaults to True.
-
-        test_delay_epochs (int):
-            Number of epochs before starting to use evaluation runs. Initially, models do not generate meaningful
-            results, hence waiting for a couple of epochs might save some time.
-
-        print_eval (bool):
-            Enable / Disable console logging for evalutaion steps. If disabled then it only shows the final values at
-            the end of the evaluation. Default to ```False```.
-
-        print_step (int):
-            Number of steps required to print the next training log.
-
-        log_dashboard (str): "tensorboard" or "wandb"
-            Set the experiment tracking tool
-
-        plot_step (int):
-            Number of steps required to log training on Tensorboard.
-
-        model_param_stats (bool):
-            Enable / Disable logging internal model stats for model diagnostic. It might be useful for model debugging.
-            Defaults to ```False```.
-
-        project_name (str):
-            Name of the project. Defaults to config.model
-
-        wandb_entity (str):
-            Name of W&B entity/team. Enables collaboration across a team or org.
-
-        log_model_step (int):
-            Number of steps required to log a checkpoint as W&B artifact
-
-        save_step (int):
-            Number of steps required to save the next checkpoint.
-
-        checkpoint (bool):
-            Enable / Disable checkpointing.
-
-        keep_all_best (bool):
-            Enable / Disable keeping all the saved best models instead of overwriting the previous one. Defaults
-            to ```False```.
-
-        keep_after (int):
-            Number of steps to wait before saving all the best models. In use if ```keep_all_best == True```. Defaults
-            to 10000.
-
         num_loader_workers (int):
             Number of workers for training time dataloader.
 
         num_eval_loader_workers (int):
             Number of workers for evaluation time dataloader.
-
-        output_path (str):
-            Path for training output folder, either a local file path or other
-            URLs supported by both fsspec and tensorboardX, e.g. GCS (gs://) or
-            S3 (s3://) paths. The nonexist part of the given path is created
-            automatically. All training artefacts are saved there.
     """
 
     model: str = None
-    run_name: str = "coqui_tts"
-    run_description: str = ""
-    # training params
-    epochs: int = 10000
-    batch_size: int = None
-    eval_batch_size: int = None
-    mixed_precision: bool = False
-    scheduler_after_epoch: bool = False
-    # eval params
-    run_eval: bool = True
-    test_delay_epochs: int = 0
-    print_eval: bool = False
-    # logging
-    dashboard_logger: str = "tensorboard"
-    print_step: int = 25
-    plot_step: int = 100
-    model_param_stats: bool = False
-    project_name: str = None
-    log_model_step: int = None
-    wandb_entity: str = None
-    # checkpointing
-    save_step: int = 10000
-    checkpoint: bool = True
-    keep_all_best: bool = False
-    keep_after: int = 10000
     # dataloading
     num_loader_workers: int = 0
     num_eval_loader_workers: int = 0
     use_noise_augment: bool = False
     use_language_weighted_sampler: bool = False
-
-    # paths
-    output_path: str = None
-    # distributed
-    distributed_backend: str = "nccl"
-    distributed_url: str = "tcp://localhost:54321"