exp: 
  dir: ./exps # directory to save the experiment
  name: bluearchive # name of the experiment

datas:
  _target_: look2hear.datas.DataModule
  dataset_type: 1 # 1 or 2. see README for more details
  sr: 44100 # sample rate
  segments: 4 # cropped audio in seconds. chunksize = sr * segments
  num_steps: 1000 # number of samples to be used for training in one epoch.
  batch_size: 1 # batch size
  num_workers: 0 # number of workers for data loading
  pin_memory: true # pin memory for data loading

  stems:
    original: original # key for the original audio files, don't change it
    codec: codec # key for the codec audio files, don't change it

  train:
    dir: # dataset where the training audio files are stored 
    - output # list of directories
    original_format: wav # the format of the original audio files
    codec_format: mp3 # the format of the codec audio files

  valid:
    dir: # dataset where the validation audio files are stored
    - output_v # list of directories
    original_format: wav # the format of the original audio files
    codec_format: mp3 # the format of the codec audio files

model:
  _target_: look2hear.models.apollo.Apollo
  sr: 44100 # sample rate
  win: 20 # window size in milliseconds
  feature_dim: 256 # feature dimension
  layer: 6 # number of layers

discriminator:
  _target_: look2hear.discriminators.frequencydis.MultiFrequencyDiscriminator
  nch: 2
  window: [32, 64, 128, 256, 512, 1024, 2048]

optimizer_g:
  _target_: torch.optim.AdamW
  lr: 0.001
  weight_decay: 0.01

optimizer_d:
  _target_: torch.optim.AdamW
  lr: 0.0001
  weight_decay: 0.01
  betas: [0.5, 0.99]

scheduler_g:
  _target_: torch.optim.lr_scheduler.StepLR
  step_size: 2
  gamma: 0.98

scheduler_d:
  _target_: torch.optim.lr_scheduler.StepLR
  step_size: 2
  gamma: 0.98

loss_g:
  _target_: look2hear.losses.gan_losses.MultiFrequencyGenLoss
  eps: 1e-8

loss_d:
  _target_: look2hear.losses.gan_losses.MultiFrequencyDisLoss
  eps: 1e-8

metrics:
  _target_: look2hear.losses.MultiSrcNegSDR
  sdr_type: sisdr # metric for validation, one of [snr, sisdr, sdsdr]

system:
  _target_: look2hear.system.audio_litmodule.AudioLightningModule

# comment out the early_topping content below, if you do not wish to have early_topping
early_stopping:
  _target_: pytorch_lightning.callbacks.EarlyStopping
  monitor: val_loss # metric to monitor
  patience: 50 # number of epochs with no improvement after which training will be stopped
  mode: min
  verbose: true

checkpoint:
  _target_: pytorch_lightning.callbacks.ModelCheckpoint
  dirpath: ${exp.dir}/${exp.name}/checkpoints
  monitor: val_loss # metric to monitor
  mode: min
  verbose: true
  save_top_k: 10 # number of best models to save
  save_last: true # save the last checkpoint
  filename: '{epoch}-{val_loss:.4f}'

logger:
  _target_: pytorch_lightning.loggers.WandbLogger
  name: ${exp.name}
  save_dir: ${exp.dir}/${exp.name}/logs
  offline: false # if true, the logs will not be uploaded to wandb
  project: Audio-Restoration

trainer:
  _target_: pytorch_lightning.Trainer
  devices: [0] # number of GPUs to use
  max_epochs: 1000 # max number of epochs
  sync_batchnorm: true
  default_root_dir: ${exp.dir}/${exp.name}/
  accelerator: cuda
  limit_train_batches: 1.0
  fast_dev_run: false
  precision: bf16 # [16, bf16, 32, 64]
  enable_model_summary: true