task:
  _target_: pyannote.audio.tasks.SpeakerDiarization
  duration: 5.0
  max_speakers_per_chunk: 3
  max_speakers_per_frame: 2
  batch_size: 32
  num_workers: 10
  pin_memory: false
model:
  _target_: pyannote.audio.models.segmentation.debug.SimpleSegmentationModel
optimizer:
  _target_: torch.optim.Adam
  lr: 0.001
  betas:
    - 0.9
    - 0.999
  eps: 1.0e-08
  weight_decay: 0
  amsgrad: false
scheduler:
  _target_: pyannote.audio.cli.lr_schedulers.CosineAnnealingWarmRestarts
  min_lr: 1.0e-08
  max_lr: 0.001
  patience: 1
trainer:
  _target_: pytorch_lightning.Trainer
  accelerator: auto
  accumulate_grad_batches: 1
  benchmark: null
  deterministic: false
  check_val_every_n_epoch: 1
  devices: auto
  detect_anomaly: false
  enable_checkpointing: true
  enable_model_summary: true
  enable_progress_bar: true
  fast_dev_run: false
  gradient_clip_val: null
  gradient_clip_algorithm: norm
  limit_predict_batches: 1.0
  limit_test_batches: 1.0
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  log_every_n_steps: 50
  max_epochs: 1
  max_steps: -1
  max_time: null
  min_epochs: 1
  min_steps: null
  num_nodes: 1
  num_sanity_val_steps: 2
  overfit_batches: 0.0
  precision: 32
  profiler: null
  reload_dataloaders_every_n_epochs: 0
  use_distributed_sampler: true
  strategy: auto
  sync_batchnorm: false
  val_check_interval: 1.0
protocol: AMI.SpeakerDiarization.only_words
registry: REDACTED/pyannote-audio/tutorials/AMI-diarization-setup/pyannote/database.yml