uploaded drums model
Browse files- drums.ckpt +3 -0
- hparams.yaml +158 -0
drums.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:1c6e6f41b5eca279af8f6500a8d6c193f6968598fb0cb0ba33c317323cc8acbc
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size 521819269
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hparams.yaml
ADDED
@@ -0,0 +1,158 @@
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model:
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sr: 44100
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n_fft: 2048
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bandsplits:
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- - 1000
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- 50
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- - 2000
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- 100
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- - 4000
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- 250
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- - 8000
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- 500
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- - 16000
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- 1000
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bottleneck_layer: rnn
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t_timesteps: 263
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fc_dim: 128
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rnn_dim: 256
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rnn_type: LSTM
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bidirectional: true
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num_layers: 10
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mlp_dim: 512
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return_mask: false
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complex_as_channel: true
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is_mono: false
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train_dataset:
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file_dir: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/datasets/Drums
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txt_dir: files/
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txt_path: null
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target: drums
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is_training: true
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is_mono: false
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sr: 44100
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preload_dataset: false
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silent_prob: 0.1
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mix_prob: 0.25
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mix_tgt_too: false
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test_dataset:
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in_fp: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/datasets/Drums
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target: drums
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is_mono: false
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sr: 44100
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win_size: 3
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hop_size: 0.5
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batch_size: 4
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window: null
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sad:
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sr: 44100
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window_size_in_sec: 6
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overlap_ratio: 0.5
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n_chunks_per_segment: 10
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eps: 1.0e-05
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gamma: 0.001
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threshold_max_quantile: 0.15
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threshold_segment: 0.5
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augmentations:
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randomcrop:
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_target_: data.augmentations.RandomCrop
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p: 1
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chunk_size_sec: 3
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sr: 44100
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window_stft: 2048
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hop_stft: 512
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gainscale:
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_target_: data.augmentations.GainScale
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p: 0.5
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min_db: -10.0
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max_db: 10.0
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featurizer:
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direct_transform:
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_target_: torchaudio.transforms.Spectrogram
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n_fft: 2048
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win_length: 2048
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hop_length: 512
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power: null
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inverse_transform:
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_target_: torchaudio.transforms.InverseSpectrogram
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n_fft: 2048
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win_length: 2048
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hop_length: 512
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callbacks:
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lr_monitor:
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_target_: pytorch_lightning.callbacks.LearningRateMonitor
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logging_interval: epoch
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model_ckpt:
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_target_: pytorch_lightning.callbacks.ModelCheckpoint
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monitor: train/loss
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mode: min
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save_top_k: 5
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dirpath: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/src/logs/bandsplitrnn/2023-05-08_15-35/weights
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filename: epoch{epoch:02d}-train_loss{train/loss:.2f}
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auto_insert_metric_name: false
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model_ckpt_usdr:
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_target_: pytorch_lightning.callbacks.ModelCheckpoint
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monitor: train/usdr
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mode: max
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save_top_k: 5
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dirpath: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/src/logs/bandsplitrnn/2023-05-08_15-35/weights
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filename: epoch{epoch:02d}-train_usdr{train/usdr:.2f}
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auto_insert_metric_name: false
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ema:
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_target_: utils.callbacks.EMA
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decay: 0.9999
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validate_original_weights: false
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every_n_steps: 1
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logger:
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tensorboard:
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_target_: pytorch_lightning.loggers.TensorBoardLogger
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save_dir: /home/afteraugustmusician/Music-Demixing-with-Band-Split-RNN/src/logs/bandsplitrnn/2023-05-08_15-35/tb_logs
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name: ''
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version: ''
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log_graph: false
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default_hp_metric: false
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prefix: ''
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wandb:
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_target_: pytorch_lightning.loggers.WandbLogger
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project: MDX_BSRNN_23
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name: drums
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save_dir: wandb_logs
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offline: false
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id: null
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log_model: false
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prefix: ''
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job_type: train
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group: ''
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tags: []
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train_loader:
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batch_size: 8
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num_workers: 12
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shuffle: true
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drop_last: true
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val_loader:
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batch_size: 2
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num_workers: 8
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shuffle: false
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drop_last: false
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opt:
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_target_: torch.optim.Adam
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lr: 0.001
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sch:
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warmup_step: 10
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alpha: 1
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gamma: 0.9899494936611665
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ckpt_path: logs/bandsplitrnn/2023-04-29_14-45/weights/drums-193-usdr-5.29.ckpt
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trainer:
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fast_dev_run: false
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min_epochs: 100
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max_epochs: 500
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log_every_n_steps: 10
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accelerator: auto
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devices: auto
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gradient_clip_val: 5
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precision: 32
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enable_progress_bar: true
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benchmark: true
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deterministic: false
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experiment_dirname: bandsplitrnn
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wandb_api_key: d5c4447e39b2b10b95f05f907d57845ded16bc13
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