| 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 | |