checkpoint_dir: runs/asd

evaluation:
  # The official val_orig.csv, shipped inside the pack it scores.
  ava_groundtruth: data/AVA/packed/val/groundtruth.csv
  ava_predictions: runs/asd/ava_predictions.csv

# Each corpus keeps its pack beside its own media, so a dataset can be copied
# to fast local storage on its own. AVA val is disjoint from everything and is
# the only evaluation set. Measured overlap at the source-video level:
#
#   AVA train / val              0 of 33 shared - a real held-out split
#   WASD train / val           161 of 161 shared - never held out, free to train on
#   MSDWild few.train / few.val  0 of 489 shared - a real held-out split
#   MSDWild few.train / many.val 0 of 172 shared - a real held-out split
#
# WASD val and both MSDWild val sets are trained on deliberately. WASD val is
# not a held-out set to begin with. The MSDWild pair are: training on them
# spends two genuine evaluation sets (23.4 h, and 16.9 h of the harder
# many-speaker condition) to buy 40 h of training data. AVA val is untouched
# and is what the reported mAP is computed on. Move any of them back under
# `val` to recover them as evaluation sets.
packed_asd:
  train:
    - data/AVA/packed/train
    - data/WASD/packed/train
    - data/WASD/packed/val
    - data/MSDWild/packed/few.train
    - data/MSDWild/packed/few.val
    - data/MSDWild/packed/many.val
  val:
    - data/AVA/packed/val

asd:
  num_workers: 16
  max_epochs: 5
  seed: 0

  # Frozen audio frontend: cpc (256-dim) or mimi (512-dim, Kyutai). Both emit
  # 25 Hz features, which is the rate the visual frontend runs at.
  audio_encoder: cpc

  precision: bf16-mixed
  gradient_clip_val: 1.0
  accumulate_grad_batches: 1
  matmul_precision: high
  # Metric that selects best.ckpt. Leave empty when there is no held-out
  # split to select on: last.ckpt is written either way.
  checkpoint_monitor: val/mAP_official
  checkpoint_mode: max

  # Batches are built to a frame budget rather than a fixed batch size, and
  # tracks longer than train_window are split into near-equal chunks so one
  # epoch still covers every frame. Each chunk after the first also reads the
  # projection window's history as unscored context, so a causal model never
  # has to predict from history it was not shown. See docs/preprocessing.md.
  train_window: 250
  frame_budget: 2048
  min_batch: 4
  max_batch: 16

  # Validation batches whole tracks, grouped by length and right-padded. It is
  # forward-only, so it affords a much larger budget than training.
  val_frame_budget: 8192
  val_max_batch: 64

  # Train-time augmentation. Audio mixing is the one that attacks false
  # positives: foreign speech is added while the label stays unchanged, so the
  # model has to reject speech it cannot see on the face. Donors are drawn from
  # a different clip, never from the batch.
  augment:
    audio_mix_prob: 0.5
    audio_snr_db: [-5.0, 5.0]
    visual: true
    crop_scale: [0.7, 1.0]
    rotate_degrees: 15.0
    # Ablation: replace the audio and relabel the sample silent. Off by default.
    substitute_audio_prob: 0.0

  lr: 0.001
  weight_decay: 0.01
  warmup_ratio: 0.1
  min_lr: 0.00001

  projection_window:
    bin_sec: [0.8, 0.6, 0.4, 0.2, 0.2, 0.4]
    num_hist_bins: 4
    frame_hz: 25
    threshold: 0.5
    mode: independent
    cut_center_frame: false

  cross:
    d_model: 256
    m_ff: 4
    n_heads: 4
    n_layers: 1
    dropout: 0.1
    activation: GELU
    pos_encoding: alibi        # alibi | rope | sinusoidal | none

  temporal:
    d_model: 512
    m_ff: 4
    n_heads: 8
    n_layers: 1
    dropout: 0.1
    activation: GELU
    pos_encoding: alibi        # alibi | rope | sinusoidal | none