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training_fn: residual.train_residual_dancer
device: mps
seed: 42
dance_ids: &dance_ids
- BCH
- BOL
# - CHA
- ECS
- HST
- LHP
- NC2
- JIV
- QST
- RMB
- SFT
- SLS
- SMB
- SWZ
- TGO
- VWZ
- WCS
data_module:
batch_size: 128
num_workers: 10
test_proportion: 0.15
datasets:
preprocessing.dataset.BestBallroomDataset:
audio_dir: data/ballroom-songs
class_list: *dance_ids
audio_window_jitter: 0.7
preprocessing.dataset.Music4DanceDataset:
song_data_path: data/songs_cleaned.csv
song_audio_path: data/samples
class_list: *dance_ids
multi_label: True
min_votes: 1
audio_window_jitter: 0.7
model:
n_channels: 128
feature_extractor:
mask_count: 0 # Don't mask the data
snr_mean: 15.0 # Pretty much eliminate the noise
freq_mask_size: 10
time_mask_size: 80
trainer:
log_every_n_steps: 15
accelerator: gpu
max_epochs: 50
min_epochs: 2
fast_dev_run: False
# gradient_clip_val: 0.5
# overfit_batches: 1
training_environment:
learning_rate: 0.00053
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