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Train Music Source Separate Model

Use train.py. If you use multi-GPUs, try to use train_accelerate.py.

usage: train.py [-h] [--model_type MODEL_TYPE] [--config_path CONFIG_PATH] [--start_check_point START_CHECK_POINT] [--results_path RESULTS_PATH] [--data_path DATA_PATH [DATA_PATH ...]]
                [--dataset_type DATASET_TYPE] [--valid_path VALID_PATH [VALID_PATH ...]] [--num_workers NUM_WORKERS] [--pin_memory PIN_MEMORY] [--seed SEED]
                [--device_ids DEVICE_IDS [DEVICE_IDS ...]] [--use_multistft_loss] [--use_mse_loss] [--use_l1_loss] [--pre_valid]
                [--metrics {sdr,l1_freq,si_sdr,log_wmse,aura_stft,aura_mrstft,bleedless,fullness} [{sdr,l1_freq,si_sdr,log_wmse,aura_stft,aura_mrstft,bleedless,fullness} ...]]
                [--metric_for_scheduler {sdr,l1_freq,si_sdr,log_wmse,aura_stft,aura_mrstft,bleedless,fullness}]

options:
  -h, --help                                                show this help message and exit
  --model_type MODEL_TYPE                                   One of mdx23c, htdemucs, segm_models, mel_band_roformer, bs_roformer, swin_upernet, bandit
  --config_path CONFIG_PATH                                 path to config file
  --start_check_point START_CHECK_POINT                     Initial checkpoint to start training
  --results_path RESULTS_PATH                               path to folder where results will be stored (weights, metadata)
  --data_path DATA_PATH [DATA_PATH ...]                     Dataset data paths. You can provide several folders.
  --dataset_type DATASET_TYPE                               Dataset type. Must be one of: 1, 2, 3 or 4. Details here: https://github.com/ZFTurbo/Music-Source-Separation-Training/blob/main/docs/dataset_types.md
  --valid_path VALID_PATH [VALID_PATH ...]                  validation data paths. You can provide several folders.
  --num_workers NUM_WORKERS                                 dataloader num_workers
  --pin_memory PIN_MEMORY                                   dataloader pin_memory
  --seed SEED                                               random seed
  --device_ids DEVICE_IDS [DEVICE_IDS ...]                  list of gpu ids
  --use_multistft_loss                                      Use MultiSTFT Loss (from auraloss package)
  --use_mse_loss                                            Use default MSE loss
  --use_l1_loss                                             Use L1 loss
  --pre_valid                                               Run validation before training
  --metrics {sdr,l1_freq,si_sdr,log_wmse,aura_stft,aura_mrstft,bleedless,fullness} [{sdr,l1_freq,si_sdr,log_wmse,aura_stft,aura_mrstft,bleedless,fullness} ...]
                                                            List of metrics to use.
  --metric_for_scheduler {sdr,l1_freq,si_sdr,log_wmse,aura_stft,aura_mrstft,bleedless,fullness}
                                                            Metric which will be used for scheduler.