mirror of https://github.com/coqui-ai/TTS.git
Update BaseTrainingConfig
Eren Gölge
parent
d3a58ed07a
commit
aa81454721
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@ -2,6 +2,7 @@ from dataclasses import asdict, dataclass
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from typing import List
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from coqpit import Coqpit, check_argument
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from trainer import TrainerConfig
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@dataclass
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@ -237,130 +238,24 @@ class BaseDatasetConfig(Coqpit):
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@dataclass
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class BaseTrainingConfig(Coqpit):
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"""Base config to define the basic training parameters that are shared
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among all the models.
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class BaseTrainingConfig(TrainerConfig):
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"""Base config to define the basic 🐸TTS training parameters that are shared
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among all the models. It is based on ```Trainer.TrainingConfig```.
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Args:
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model (str):
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Name of the model that is used in the training.
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run_name (str):
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Name of the experiment. This prefixes the output folder name. Defaults to `coqui_tts`.
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run_description (str):
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Short description of the experiment.
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epochs (int):
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Number training epochs. Defaults to 10000.
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batch_size (int):
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Training batch size.
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eval_batch_size (int):
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Validation batch size.
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mixed_precision (bool):
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Enable / Disable mixed precision training. It reduces the VRAM use and allows larger batch sizes, however
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it may also cause numerical unstability in some cases.
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scheduler_after_epoch (bool):
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If true, run the scheduler step after each epoch else run it after each model step.
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run_eval (bool):
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Enable / Disable evaluation (validation) run. Defaults to True.
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test_delay_epochs (int):
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Number of epochs before starting to use evaluation runs. Initially, models do not generate meaningful
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results, hence waiting for a couple of epochs might save some time.
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print_eval (bool):
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Enable / Disable console logging for evalutaion steps. If disabled then it only shows the final values at
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the end of the evaluation. Default to ```False```.
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print_step (int):
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Number of steps required to print the next training log.
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log_dashboard (str): "tensorboard" or "wandb"
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Set the experiment tracking tool
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plot_step (int):
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Number of steps required to log training on Tensorboard.
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model_param_stats (bool):
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Enable / Disable logging internal model stats for model diagnostic. It might be useful for model debugging.
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Defaults to ```False```.
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project_name (str):
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Name of the project. Defaults to config.model
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wandb_entity (str):
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Name of W&B entity/team. Enables collaboration across a team or org.
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log_model_step (int):
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Number of steps required to log a checkpoint as W&B artifact
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save_step (int):
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Number of steps required to save the next checkpoint.
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checkpoint (bool):
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Enable / Disable checkpointing.
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keep_all_best (bool):
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Enable / Disable keeping all the saved best models instead of overwriting the previous one. Defaults
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to ```False```.
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keep_after (int):
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Number of steps to wait before saving all the best models. In use if ```keep_all_best == True```. Defaults
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to 10000.
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num_loader_workers (int):
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Number of workers for training time dataloader.
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num_eval_loader_workers (int):
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Number of workers for evaluation time dataloader.
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output_path (str):
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Path for training output folder, either a local file path or other
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URLs supported by both fsspec and tensorboardX, e.g. GCS (gs://) or
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S3 (s3://) paths. The nonexist part of the given path is created
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automatically. All training artefacts are saved there.
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"""
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model: str = None
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run_name: str = "coqui_tts"
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run_description: str = ""
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# training params
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epochs: int = 10000
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batch_size: int = None
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eval_batch_size: int = None
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mixed_precision: bool = False
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scheduler_after_epoch: bool = False
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# eval params
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run_eval: bool = True
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test_delay_epochs: int = 0
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print_eval: bool = False
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# logging
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dashboard_logger: str = "tensorboard"
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print_step: int = 25
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plot_step: int = 100
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model_param_stats: bool = False
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project_name: str = None
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log_model_step: int = None
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wandb_entity: str = None
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# checkpointing
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save_step: int = 10000
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checkpoint: bool = True
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keep_all_best: bool = False
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keep_after: int = 10000
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# dataloading
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num_loader_workers: int = 0
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num_eval_loader_workers: int = 0
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use_noise_augment: bool = False
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use_language_weighted_sampler: bool = False
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# paths
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output_path: str = None
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# distributed
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distributed_backend: str = "nccl"
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distributed_url: str = "tcp://localhost:54321"
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