defaults:
  - _self_
  - model: null
  - effects: null

seed: 12345
train: True
sample_rate: 48000
logs_dir: "./logs"
log_every_n_steps: 1000
render_files: True

callbacks:
  model_checkpoint:
    _target_: pytorch_lightning.callbacks.ModelCheckpoint
    monitor: "valid_loss"   # name of the logged metric which determines when model is improving
    save_top_k: 1           # save k best models (determined by above metric)
    save_last: True         # additionaly always save model from last epoch
    mode: "min"             # can be "max" or "min"
    verbose: False
    dirpath: ${logs_dir}/ckpts/${now:%Y-%m-%d-%H-%M-%S}
    filename: '{epoch:02d}-{valid_loss:.3f}'

datamodule:
  _target_: remfx.datasets.VocalSetDatamodule
  train_dataset:
    _target_: remfx.datasets.VocalSet
    sample_rate: ${sample_rate}
    root: ${oc.env:DATASET_ROOT}
    chunk_size_in_sec: 6
    mode: "train"
    effect_types: ${effects.train_effects}
    render_files: ${render_files}
  val_dataset:
    _target_: remfx.datasets.VocalSet
    sample_rate: ${sample_rate}
    root: ${oc.env:DATASET_ROOT}
    chunk_size_in_sec: 6
    mode: "val"
    effect_types: ${effects.val_effects}
    render_files: ${render_files}
  test_dataset:
    _target_: remfx.datasets.VocalSet
    sample_rate: ${sample_rate}
    root: ${oc.env:DATASET_ROOT}
    chunk_size_in_sec: 6
    mode: "test"
    effect_types: ${effects.val_effects}
    render_files: ${render_files}

  batch_size: 16
  num_workers: 8
  pin_memory: True
  persistent_workers: True

logger:
  _target_: pytorch_lightning.loggers.WandbLogger
  project: ${oc.env:WANDB_PROJECT}
  entity: ${oc.env:WANDB_ENTITY}
  # offline: False  # set True to store all logs only locally
  job_type: "train"
  group: ""
  save_dir: "."

trainer:
  _target_: pytorch_lightning.Trainer
  precision: 32 # Precision used for tensors, default `32`
  min_epochs: 0
  max_epochs: -1
  enable_model_summary: False
  log_every_n_steps: 1 # Logs metrics every N batches
  accumulate_grad_batches: 1
  accelerator: null
  devices: 1