diffqe_librispeech_5.yaml

# @package _global_

sampling_rate: 16000
length: 131072
log_every_n_steps: 1000

channels: 1
patch_size: 8
factors: [4, 4, 4]
encoder_channels: 256
encoder_depth: 3

quantizer_type: block
quantizer_split_size: 4
quantizer_codebook_size: 8192
quantizer_num_residuals: 4
quantizer_num_groups: 1

model:
  _target_: main.module_diffqe.Model
  lr: 1e-4
  lr_beta1: 0.95
  lr_beta2: 0.999
  lr_eps: 1e-6
  lr_weight_decay: 1e-3
  use_scheduler: False
  scheduler_inv_gamma: 20000.0
  scheduler_power: 1.0
  scheduler_warmup: 0.99

  in_channels: ${channels}
  channels: 128
  patch_size: ${patch_size}
  resnet_groups: 8
  kernel_multiplier_downsample: 2
  kernel_sizes_init: [1, 3, 7]
  multipliers: [1, 2, 4, 4]
  factors: ${factors}
  num_blocks: [2, 2, 2]
  attentions: [False, False, False]
  use_nearest_upsample: False
  use_skip_scale: True
  diffusion_sigma_distribution:
    _target_: audio_diffusion_pytorch.LogNormalDistribution
    mean: -3.0
    std: 1.0
  diffusion_sigma_data: 0.1
  diffusion_dynamic_threshold: 0.0

  encoder_channels: ${encoder_channels}
  encoder_depth: ${encoder_depth}
  quantizer_type: ${quantizer_type}
  quantizer_codebook_size: ${quantizer_codebook_size}
  quantizer_num_residuals: ${quantizer_num_residuals}
  quantizer_loss_weight: 1e-5
  quantizer_groups: ${quantizer_num_groups}
  quantizer_split_size: ${quantizer_split_size}
  quantizer_expire_threshold: 0.1
  quantizer_shared_codebook: True
  quantizer_ema_decay: 0.9



datamodule:
  _target_: main.module_diffqe.Datamodule
  dataset:
    _target_: audio_data_pytorch.LibriSpeechDataset
    root: ${data_dir}
    transforms:
      _target_: audio_data_pytorch.AllTransform
      source_rate: 16000
      target_rate: ${sampling_rate}
      random_crop_size: ${length}
      loudness: -20
  val_split: 0.001
  batch_size: 24
  num_workers: 8
  pin_memory: True

callbacks:
  rich_progress_bar:
    _target_: pytorch_lightning.callbacks.RichProgressBar

  model_checkpoint:
    _target_: pytorch_lightning.callbacks.ModelCheckpoint
    monitor: "valid_loss"   # name of the logged metric which determines when model is improving
    save_top_k: 1           # save k best models (determined by above metric)
    save_last: True         # additionaly always save model from last epoch
    mode: "min"             # can be "max" or "min"
    verbose: False
    dirpath: ${logs_dir}/ckpts/${now:%Y-%m-%d-%H-%M-%S}
    filename: '{epoch:02d}-{valid_loss:.3f}'

  model_summary:
    _target_: pytorch_lightning.callbacks.RichModelSummary
    max_depth: 2

  audio_samples_logger:
    _target_: main.module_diffqe.SampleLogger
    num_items: 2
    channels: ${channels}
    sampling_rate: ${sampling_rate}
    length: ${length}
    sampling_steps: [3, 10, 25, 50]
    diffusion_sampler:
      _target_: audio_diffusion_pytorch.ADPM2Sampler
      rho: 1.0
    diffusion_schedule:
      _target_: audio_diffusion_pytorch.KarrasSchedule
      sigma_min: 0.0001
      sigma_max: 3.0
      rho: 9.0

  quantization_info:
    _target_: main.module_diffqe.QuantizationInfoLogger
    sample_rate: ${sampling_rate}
    patch_size: ${patch_size}
    split_size: ${quantizer_split_size}
    num_residuals: ${quantizer_num_residuals}
    num_groups: ${quantizer_num_groups}
    downsample_factors: ${factors}
    channels: ${encoder_channels}
    encoder_depth: ${encoder_depth}
    quantizer_type: ${quantizer_type}

loggers:
  wandb:
    _target_: pytorch_lightning.loggers.wandb.WandbLogger
    project: ${oc.env:WANDB_PROJECT}
    entity: ${oc.env:WANDB_ENTITY}
    # offline: False  # set True to store all logs only locally
    job_type: "train"
    group: ""
    save_dir: ${logs_dir}

trainer:
  _target_: pytorch_lightning.Trainer
  gpus: 0 # Set `1` to train on GPU, `0` to train on CPU only, and `-1` to train on all GPUs, default `0`
  precision: 32 # Precision used for tensors, default `32`
  accelerator: null # `ddp` GPUs train individually and sync gradients, default `None`
  min_epochs: 0
  max_epochs: -1
  enable_model_summary: False
  log_every_n_steps: 1 # Logs metrics every N batches
  check_val_every_n_epoch: null
  val_check_interval: ${log_every_n_steps}
  accumulate_grad_batches: 2