File size: 2,727 Bytes
811799b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
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: ${logs_dir}/ckpts/${now:%Y-%m-%d-%H-%M-%S}
module: main.module_base
batch_size: 1
accumulate_grad_batches: 32
num_workers: 8
sampling_rate: 44100
length: 32768
channels: 2
log_every_n_steps: 1000
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/wav_dataset/kicks
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: kicks_v7
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}
|