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diff_textcond_test.yaml
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# @package _global_
sampling_rate: 48000
length: 262144
channels: 1
log_every_n_steps: 1000
encoder_max_length: 512
encoder_features: 768
model:
_target_: main.module_diff_textcond.Model
lr: 1e-4
lr_beta1: 0.95
lr_beta2: 0.999
lr_eps: 1e-6
lr_weight_decay: 1e-3
ema_beta: 0.9999
ema_power: 0.7
model:
_target_: audio_diffusion_pytorch.AudioDiffusionConditional
in_channels: ${channels}
channels: 160
patch_size: 16
resnet_groups: 8
kernel_multiplier_downsample: 2
multipliers: [1, 2, 4, 4, 4, 4, 4]
factors: [4, 2, 2, 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.UniformDistribution
embedding_max_length: ${encoder_max_length}
embedding_features: ${encoder_features}
embedding_mask_proba: 0.1
tokenizer: t5-base
encoder_num_tokens: 32128 # T5 vocab size
encoder_features: ${encoder_features}
encoder_max_length: ${encoder_max_length}
encoder:
_target_: a_transformers_pytorch.transformers.Transformer
features: 768
max_length: 512
num_layers: 8
head_features: 64
num_heads: 8
multiplier: 4
batch_size: 4
datamodule:
_target_: main.module_diff_textcond.Datamodule
dataset_train:
_target_: audio_data_pytorch.ClothoDataset
root: ${data_dir}
split: train
batch_size: ${batch_size}
preprocess_sample_rate: 48000
preprocess_transforms:
_target_: audio_data_pytorch.AllTransform
crop_size: 480000
stereo: True
transforms:
_target_: audio_data_pytorch.AllTransform
target_rate: ${sampling_rate}
random_crop_size: ${length}
mono: True
loudness: -20
dataset_valid:
_target_: audio_data_pytorch.ClothoDataset
root: ${data_dir}
split: valid
batch_size: ${batch_size}
preprocess_sample_rate: 48000
preprocess_transforms:
_target_: audio_data_pytorch.AllTransform
crop_size: 480000
stereo: True
transforms:
_target_: audio_data_pytorch.AllTransform
target_rate: ${sampling_rate}
random_crop_size: ${length}
mono: True
loudness: -20
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_diff_textcond.SampleLogger
num_items: 3
channels: ${channels}
sampling_rate: ${sampling_rate}
length: ${length}
sampling_steps: [3]
embedding_scale: 7.0
use_ema_model: False
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
limit_val_batches: 20
val_check_interval: ${log_every_n_steps}