mirror of https://github.com/coqui-ai/TTS.git
Add `scheduler_after_epoch` to `BaseTrainingConfig`
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@ -79,7 +79,7 @@ class BaseAudioConfig(Coqpit):
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preemphasis: float = 0.0
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preemphasis: float = 0.0
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ref_level_db: int = 20
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ref_level_db: int = 20
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do_sound_norm: bool = False
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do_sound_norm: bool = False
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log_func = "np.log10"
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log_func: str = "np.log10"
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# silence trimming
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# silence trimming
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do_trim_silence: bool = True
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do_trim_silence: bool = True
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trim_db: int = 45
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trim_db: int = 45
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@ -182,48 +182,70 @@ class BaseTrainingConfig(Coqpit):
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Args:
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Args:
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model (str):
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model (str):
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Name of the model that is used in the training.
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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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run_name (str):
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Name of the experiment. This prefixes the output folder name.
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Name of the experiment. This prefixes the output folder name.
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run_description (str):
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run_description (str):
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Short description of the experiment.
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Short description of the experiment.
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epochs (int):
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epochs (int):
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Number training epochs. Defaults to 10000.
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Number training epochs. Defaults to 10000.
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batch_size (int):
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batch_size (int):
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Training batch size.
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Training batch size.
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eval_batch_size (int):
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eval_batch_size (int):
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Validation batch size.
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Validation batch size.
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mixed_precision (bool):
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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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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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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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run_eval (bool):
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Enable / Disable evaluation (validation) run. Defaults to True.
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Enable / Disable evaluation (validation) run. Defaults to True.
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test_delay_epochs (int):
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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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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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results, hence waiting for a couple of epochs might save some time.
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print_eval (bool):
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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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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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the end of the evaluation. Default to ```False```.
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print_step (int):
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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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Number of steps required to print the next training log.
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tb_plot_step (int):
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tb_plot_step (int):
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Number of steps required to log training on Tensorboard.
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Number of steps required to log training on Tensorboard.
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tb_model_param_stats (bool):
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tb_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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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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Defaults to ```False```.
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save_step (int):ipt
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save_step (int):ipt
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Number of steps required to save the next checkpoint.
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Number of steps required to save the next checkpoint.
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checkpoint (bool):
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checkpoint (bool):
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Enable / Disable checkpointing.
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Enable / Disable checkpointing.
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keep_all_best (bool):
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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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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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to ```False```.
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keep_after (int):
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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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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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to 10000.
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num_loader_workers (int):
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num_loader_workers (int):
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Number of workers for training time dataloader.
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Number of workers for training time dataloader.
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num_eval_loader_workers (int):
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num_eval_loader_workers (int):
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Number of workers for evaluation time dataloader.
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Number of workers for evaluation time dataloader.
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output_path (str):
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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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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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URLs supported by both fsspec and tensorboardX, e.g. GCS (gs://) or
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@ -239,6 +261,7 @@ class BaseTrainingConfig(Coqpit):
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batch_size: int = None
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batch_size: int = None
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eval_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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mixed_precision: bool = False
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scheduler_after_epoch: bool = False
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# eval params
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# eval params
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run_eval: bool = True
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run_eval: bool = True
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test_delay_epochs: int = 0
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test_delay_epochs: int = 0
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