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# @package _global_
# specify here default configuration
# order of defaults determines the order in which configs override each other
defaults:
- _self_
- data: ljspeech
- model: matcha
- callbacks: default
- logger: tensorboard # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
- trainer: default
- paths: default
- extras: default
- hydra: default
# experiment configs allow for version control of specific hyperparameters
# e.g. best hyperparameters for given model and datamodule
- experiment: null
# config for hyperparameter optimization
- hparams_search: null
# optional local config for machine/user specific settings
# it's optional since it doesn't need to exist and is excluded from version control
- optional local: default
# debugging config (enable through command line, e.g. `python train.py debug=default)
- debug: null
# task name, determines output directory path
task_name: "train"
run_name: ???
# tags to help you identify your experiments
# you can overwrite this in experiment configs
# overwrite from command line with `python train.py tags="[first_tag, second_tag]"`
tags: ["dev"]
# set False to skip model training
train: True
# evaluate on test set, using best model weights achieved during training
# lightning chooses best weights based on the metric specified in checkpoint callback
test: True
# simply provide checkpoint path to resume training
ckpt_path: null
# seed for random number generators in pytorch, numpy and python.random
seed: 1234
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