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tiny-audio-diffusion-percussion-finetuned-triton

audio
diffusion
waveform diffusion
audio diffusion
unet
korg
triton
grime
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tiny-audio-diffusion-percussion-finetuned-triton
File size: 2,824 Bytes 
01b6887
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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seed: 12345
train: true
ignore_warnings: true
print_config: false
work_dir: ${hydra:runtime.cwd}
logs_dir: ${work_dir}${oc.env:DIR_LOGS}
data_dir: ${work_dir}${oc.env:DIR_DATA}
ckpt_dir: ${logs_dir}/runs/${now:%Y-%m-%d-%H-%M-%S}
module: main.module_base
batch_size: 16
accumulate_grad_batches: 2
num_workers: 16
sampling_rate: 44100
length: 32768
channels: 2
log_every_n_steps: 500
model:
  _target_: ${module}.Model
  lr: 0.0001
  lr_beta1: 0.95
  lr_beta2: 0.999
  lr_eps: 1.0e-06
  lr_weight_decay: 0.001
  ema_beta: 0.995
  ema_power: 0.7
  model:
    _target_: main.DiffusionModel
    net_t:
      _target_: ${module}.UNetT
    in_channels: 2
    channels:
    - 32
    - 32
    - 64
    - 64
    - 128
    - 128
    - 256
    - 256
    factors:
    - 1
    - 2
    - 2
    - 2
    - 2
    - 2
    - 2
    - 2
    items:
    - 2
    - 2
    - 2
    - 2
    - 2
    - 2
    - 4
    - 4
    attentions:
    - 0
    - 0
    - 0
    - 0
    - 0
    - 1
    - 1
    - 1
    attention_heads: 8
    attention_features: 64
datamodule:
  _target_: main.module_base.Datamodule
  dataset:
    _target_: audio_data_pytorch.WAVDataset
    path: ./data/triton_aug
    recursive: true
    sample_rate: ${sampling_rate}
    transforms:
      _target_: audio_data_pytorch.AllTransform
      crop_size: ${length}
      stereo: true
      source_rate: ${sampling_rate}
      target_rate: ${sampling_rate}
      loudness: -20
  val_split: 0.05
  batch_size: ${batch_size}
  num_workers: ${num_workers}
  pin_memory: true
callbacks:
  rich_progress_bar:
    _target_: pytorch_lightning.callbacks.RichProgressBar
  model_checkpoint:
    _target_: pytorch_lightning.callbacks.ModelCheckpoint
    monitor: valid_loss
    save_top_k: 1
    save_last: true
    mode: 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_base.SampleLogger
    num_items: 4
    channels: ${channels}
    sampling_rate: ${sampling_rate}
    length: ${length}
    sampling_steps:
    - 50
    use_ema_model: true
loggers:
  wandb:
    _target_: pytorch_lightning.loggers.wandb.WandbLogger
    project: ${oc.env:WANDB_PROJECT}
    entity: ${oc.env:WANDB_ENTITY}
    name: percussion_v0-finetuned-triton
    job_type: train
    group: ''
    save_dir: ${logs_dir}
trainer:
  _target_: pytorch_lightning.Trainer
  gpus: 1
  precision: 16
  accelerator: gpu
  min_epochs: 0
  max_epochs: -1
  enable_model_summary: false
  log_every_n_steps: 1
  check_val_every_n_epoch: null
  val_check_interval: ${log_every_n_steps}
  accumulate_grad_batches: ${accumulate_grad_batches}
ckpt: saved_models/percussion-finetuned-triton/percussion_v0-finetuned-triton.ckpt