bsrnn-drums / hparams.yaml
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model:
sr: 44100
n_fft: 2048
bandsplits:
- - 1000
- 50
- - 2000
- 100
- - 4000
- 250
- - 8000
- 500
- - 16000
- 1000
bottleneck_layer: rnn
t_timesteps: 263
fc_dim: 128
rnn_dim: 256
rnn_type: LSTM
bidirectional: true
num_layers: 10
mlp_dim: 512
return_mask: false
complex_as_channel: true
is_mono: false
train_dataset:
file_dir: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/datasets/Drums
txt_dir: files/
txt_path: null
target: drums
is_training: true
is_mono: false
sr: 44100
preload_dataset: false
silent_prob: 0.1
mix_prob: 0.25
mix_tgt_too: false
test_dataset:
in_fp: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/datasets/Drums
target: drums
is_mono: false
sr: 44100
win_size: 3
hop_size: 0.5
batch_size: 4
window: null
sad:
sr: 44100
window_size_in_sec: 6
overlap_ratio: 0.5
n_chunks_per_segment: 10
eps: 1.0e-05
gamma: 0.001
threshold_max_quantile: 0.15
threshold_segment: 0.5
augmentations:
randomcrop:
_target_: data.augmentations.RandomCrop
p: 1
chunk_size_sec: 3
sr: 44100
window_stft: 2048
hop_stft: 512
gainscale:
_target_: data.augmentations.GainScale
p: 0.5
min_db: -10.0
max_db: 10.0
featurizer:
direct_transform:
_target_: torchaudio.transforms.Spectrogram
n_fft: 2048
win_length: 2048
hop_length: 512
power: null
inverse_transform:
_target_: torchaudio.transforms.InverseSpectrogram
n_fft: 2048
win_length: 2048
hop_length: 512
callbacks:
lr_monitor:
_target_: pytorch_lightning.callbacks.LearningRateMonitor
logging_interval: epoch
model_ckpt:
_target_: pytorch_lightning.callbacks.ModelCheckpoint
monitor: train/loss
mode: min
save_top_k: 5
dirpath: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/src/logs/bandsplitrnn/2023-05-08_15-35/weights
filename: epoch{epoch:02d}-train_loss{train/loss:.2f}
auto_insert_metric_name: false
model_ckpt_usdr:
_target_: pytorch_lightning.callbacks.ModelCheckpoint
monitor: train/usdr
mode: max
save_top_k: 5
dirpath: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/src/logs/bandsplitrnn/2023-05-08_15-35/weights
filename: epoch{epoch:02d}-train_usdr{train/usdr:.2f}
auto_insert_metric_name: false
ema:
_target_: utils.callbacks.EMA
decay: 0.9999
validate_original_weights: false
every_n_steps: 1
logger:
tensorboard:
_target_: pytorch_lightning.loggers.TensorBoardLogger
save_dir: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/src/logs/bandsplitrnn/2023-05-08_15-35/tb_logs
name: ''
version: ''
log_graph: false
default_hp_metric: false
prefix: ''
wandb:
_target_: pytorch_lightning.loggers.WandbLogger
project: MDX_BSRNN_23
name: drums
save_dir: wandb_logs
offline: false
id: null
log_model: false
prefix: ''
job_type: train
group: ''
tags: []
train_loader:
batch_size: 8
num_workers: 12
shuffle: true
drop_last: true
val_loader:
batch_size: 2
num_workers: 8
shuffle: false
drop_last: false
opt:
_target_: torch.optim.Adam
lr: 0.001
sch:
warmup_step: 10
alpha: 1
gamma: 0.9899494936611665
ckpt_path: logs/bandsplitrnn/2023-04-29_14-45/weights/drums-193-usdr-5.29.ckpt
trainer:
fast_dev_run: false
min_epochs: 100
max_epochs: 500
log_every_n_steps: 10
accelerator: auto
devices: auto
gradient_clip_val: 5
precision: 32
enable_progress_bar: true
benchmark: true
deterministic: false
experiment_dirname: bandsplitrnn
wandb_api_key: d5c4447e39b2b10b95f05f907d57845ded16bc13