embedding/develop/hydra.yaml

hydra:
  run:
    dir: ${protocol}/${task._target_}/${now:%Y-%m-%d}/${now:%H-%M-%S}
  sweep:
    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}/${protocol}/${task._target_}
    subdir: ${hydra.job.num}
  hydra_logging:
    version: 1
    formatters:
      simple:
        format: '[%(asctime)s][HYDRA] %(message)s'
    handlers:
      console:
        class: logging.StreamHandler
        formatter: simple
        stream: ext://sys.stdout
    root:
      level: INFO
      handlers:
      - console
    loggers:
      logging_example:
        level: DEBUG
    disable_existing_loggers: false
  job_logging:
    version: 1
    formatters:
      simple:
        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
    handlers:
      console:
        class: logging.StreamHandler
        formatter: simple
        stream: ext://sys.stdout
      file:
        class: logging.FileHandler
        formatter: simple
        filename: ${hydra.job.name}.log
    root:
      level: INFO
      handlers:
      - console
      - file
    disable_existing_loggers: false
  sweeper:
    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
    max_batch_size: null
  launcher:
    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
  help:
    app_name: pyannote-audio-train
    header: == ${hydra.help.app_name} ==
    footer: 'Powered by Hydra (https://hydra.cc)

      Use --hydra-help to view Hydra specific help'
    template: "${hydra.help.header}\n\npyannote-audio-train protocol={protocol_name}\
      \ task={task} model={model}\n\n{task} can be any of the following:\n* vad (default)\
      \ = voice activity detection\n* scd = speaker change detection\n* osd = overlapped\
      \ speech detection\n* xseg = multi-task segmentation\n\n{model} can be any of\
      \ the following:\n* debug (default) = simple segmentation model for debugging\
      \ purposes\n\n{optimizer} can be any of the following\n* adam (default) = Adam\
      \ optimizer\n\n{trainer} can be any of the following\n* fast_dev_run for debugging\n\
      * default (default) for training the model\n\nOptions\n=======\n\nHere, we describe\
      \ the most common options: use \"--cfg job\" option to get a complete list.\n\
      \n* task.duration: audio chunk duration (in seconds)\n* task.batch_size: number\
      \ of audio chunks per batch\n* task.num_workers: number of workers used for\
      \ generating training chunks\n\n* optimizer.lr: learning rate\n* trainer.auto_lr_find:\
      \ use pytorch-lightning AutoLR\n\nHyper-parameter optimization\n============================\n\
      \nBecause it is powered by Hydra (https://hydra.cc), one can run grid search\
      \ using the --multirun option.\n\nFor instance, the following command will run\
      \ the same job three times, with three different learning rates:\n  pyannote-audio-train\
      \ --multirun protocol={protocol_name} task={task} optimizer.lr=1e-3,1e-2,1e-1\n\
      \nEven better, one can use Ax (https://ax.dev) sweeper to optimize learning\
      \ rate directly:\n  pyannote-audio-train --multirun hydra/sweeper=ax protocol={protocol_name}\
      \ task={task} optimizer.lr=\"interval(1e-3, 1e-1)\"\n\nSee https://hydra.cc/docs/plugins/ax_sweeper\
      \ for more details.\n\nUser-defined task or model\n==========================\n\
      \n1. define your_package.YourTask (or your_package.YourModel) class\n2. create\
      \ file /path/to/your_config/task/your_task.yaml  (or /path/to/your_config/model/your_model.yaml)\n\
      \   # @package _group_\n   _target_: your_package.YourTask  # or YourModel\n\
      \   param1: value1\n   param2: value2\n3. call pyannote-audio-train --config-dir\
      \ /path/to/your_config task=your_task task.param1=modified_value1 model=your_model\
      \ ...\n\n${hydra.help.footer}"
  hydra_help:
    hydra_help: ???
    template: 'Hydra (${hydra.runtime.version})

      See https://hydra.cc for more info.


      == Flags ==

      $FLAGS_HELP


      == Configuration groups ==

      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
      to command line)


      $HYDRA_CONFIG_GROUPS


      Use ''--cfg hydra'' to Show the Hydra config.

      '
  output_subdir: ''
  overrides:
    hydra: []
    task:
    - protocol=VoxCeleb.SpeakerVerification.VoxCeleb_X
    - task=SpeakerEmbedding
    - task.num_workers=20
    - task.min_duration=2
    - task.duration=5.
    - task.num_classes_per_batch=64
    - task.num_chunks_per_class=4
    - task.margin=10.0
    - task.scale=50.
    - model=XVectorSincNet
    - trainer.gpus=1
    - +augmentation=background_then_reverb
  job:
    name: train
    override_dirname: +augmentation=background_then_reverb,model=XVectorSincNet,protocol=VoxCeleb.SpeakerVerification.VoxCeleb_X,task.duration=5.,task.margin=10.0,task.min_duration=2,task.num_chunks_per_class=4,task.num_classes_per_batch=64,task.num_workers=20,task.scale=50.,task=SpeakerEmbedding,trainer.gpus=1
    id: ???
    num: ???
    config_name: config
    env_set: {}
    env_copy: []
    config:
      override_dirname:
        kv_sep: '='
        item_sep: ','
        exclude_keys: []
  runtime:
    version: 1.0.4
    cwd: /gpfsdswork/projects/rech/eie/uno46kl/xvectors/debug
  verbose: false