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upsampler_dataset_6.yaml
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upsampler_dataset_6.yaml
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# @package _global_
sampling_rate: 48000
length: 262144
channels: 2
upsampler_factors: [4] # [24000Hz,...,375Hz]
log_every_n_steps: 1000
model:
_target_: main.module_upsampler.Model
lr: 1e-4
lr_beta1: 0.95
lr_beta2: 0.999
lr_eps: 1e-6
lr_weight_decay: 1e-3
ema_beta: 0.995
ema_power: 0.7
upsampler:
_target_: audio_diffusion_pytorch.AudioDiffusionUpsampler
factor: ${upsampler_factors}
in_channels: ${channels}
channels: 256
patch_size: 32
resnet_groups: 8
kernel_multiplier_downsample: 2
multipliers: [1, 2, 4, 4, 4, 4, 4, 4]
factors: [2, 2, 2, 2, 2, 2, 2]
num_blocks: [2, 2, 2, 2, 2, 2, 2]
attentions: [0, 0, 0, 0, 1, 1, 1, 1]
attention_heads: 8
attention_features: 128
attention_multiplier: 4
use_nearest_upsample: False
use_skip_scale: True
diffusion_type: v
diffusion_sigma_distribution:
_target_: audio_diffusion_pytorch.UniformDistribution
datamodule:
_target_: main.module_upsampler.Datamodule
dataset:
_target_: audio_data_pytorch.WAVDataset
recursive: True
sample_rate: ${sampling_rate}
transforms:
_target_: audio_data_pytorch.AllTransform
random_crop_size: ${length}
stereo: True
source_rate: ${sampling_rate}
target_rate: ${sampling_rate}
val_split: 0.0005
batch_size: 24
num_workers: 8
pin_memory: True
callbacks:
rich_progress_bar:
_target_: pytorch_lightning.callbacks.RichProgressBar
model_summary:
_target_: pytorch_lightning.callbacks.RichModelSummary
max_depth: 2
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}'
learning_rate_monitor:
_target_: pytorch_lightning.callbacks.LearningRateMonitor
audio_samples_logger:
_target_: main.module_upsampler.SampleLogger
num_items: 3
factor: ${upsampler_factors}
channels: ${channels}
sampling_rate: ${sampling_rate}
length: ${length}
use_ema_model: False
sampling_steps: [3,5,10,25,50]
diffusion_sampler:
_target_: audio_diffusion_pytorch.VSampler
diffusion_schedule:
_target_: audio_diffusion_pytorch.LinearSchedule
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: 1