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base_dataset_3.yaml
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# @package _global_
# 163M params
# To pick dataset path execute with: ++datamodule.dataset.path=/your_wav_root/
sampling_rate: 48000
length: 524288
channels: 2
log_every_n_steps: 1000
model:
_target_: main.module_base.Model
learning_rate: 1e-4
beta1: 0.9
beta2: 0.99
ema_beta: 0.9999
ema_power: 0.7
model:
_target_: audio_diffusion_pytorch.AudioDiffusionModel
in_channels: ${channels}
channels: 160
patch_factor: 16
patch_blocks: 1
resnet_groups: 8
kernel_multiplier_downsample: 2
multipliers: [1, 2, 4, 4, 4, 4, 4]
factors: [4, 4, 4, 2, 2, 2]
num_blocks: [2, 2, 2, 2, 2, 2]
attentions: [0, 0, 0, 1, 1, 1, 1]
attention_heads: 8
attention_features: 64
attention_multiplier: 2
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: 0.2
diffusion_dynamic_threshold: 0.0
datamodule:
_target_: main.module_base.Datamodule
dataset:
_target_: audio_data_pytorch.WAVDataset
recursive: True
sample_rate: ${sampling_rate}
transforms:
_target_: audio_data_pytorch.AllTransform
random_crop_size: ${length}
source_rate: ${sampling_rate}
target_rate: ${sampling_rate}
loudness: -20
val_split: 0.0005
batch_size: 32
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_base.SampleLogger
num_items: 3
channels: ${channels}
sampling_rate: ${sampling_rate}
length: ${length}
sampling_steps: [3,5,10,25,50,100]
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
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}