NAM LSTM Configuration

data_config = {
    "train": {
        "x_path": "drive/MyDrive/x_train.wav",
        "y_path": "drive/MyDrive/y_train.wav",
        "ny": 48000
    },
    "validation": {
        "x_path": "drive/MyDrive/x_test.wav",
        "y_path": "drive/MyDrive/y_test.wav",
        "ny": None
    },
    "common": {
        "delay": int(input("What is the latency (in samples) of your reamp? "))
    },
}
model_config = {
    "net": {
        "name": "LSTM",
        "config": {
            "num_layers": 2,
            "hidden_size": 14,
            "train_burn_in": 64, #!# Much larger originally
            "train_truncate": 20480 #!# Much smaller originally
                  }
           },
    "loss": {
          "val_loss": "mse",
          "mask_first": 64, #!# 4096
#         "pre_emph_weight": 1.0,
#         "pre_emph_coef": 0.90
    },
    "optimizer": {
        "lr": 0.005 #!# 0.004
    },
    "lr_scheduler": {
        "class": "ExponentialLR",
        "kwargs": {
            "gamma": 0.997
        }
    }
}
learning_config = {
    "train_dataloader": {
        "batch_size": 8, #!# 16
        "shuffle": True,
        "pin_memory": True,
        "drop_last": False, #!# True
        "num_workers": 8 #!# 0
    },
    "val_dataloader": {},
    "trainer": {
        "accelerator": "gpu",
        "devices": 1,
        "max_epochs": 1000 #!# 500-650 is usually enough

Old/earlier and less effective settings:

data_config = {
    "train": {
        "x_path": "drive/MyDrive/x_train.wav",
        "y_path": "drive/MyDrive/y_train.wav",
        "ny": 48000
    },
    "validation": {
        "x_path": "drive/MyDrive/x_test.wav",
        "y_path": "drive/MyDrive/y_test.wav",
        "ny": None
    },
    "common": {
        "delay": int(input("What is the latency (in samples) of your reamp? "))
    },
}
model_config = {
    "net": {
        "name": "LSTM",
        "config": {
            "num_layers": 2, #!# 1 Sometimes creates artifacts
            "hidden_size": 14, #!# Try 28 for really difficult/complex models
            "train_burn_in": 256, #!# Careful!
            "train_truncate": 20480 #!# Careful!
                  }
           },
    "loss": {
          "val_loss": "mse",
          "mask_first": 4096,
          "pre_emph_weight": 1.0,
          "pre_emph_coef": 0.90
    },
    "optimizer": {
        "lr": 0.01
    },
    "lr_scheduler": {
        "class": "ExponentialLR",
        "kwargs": {
            "gamma": 0.997
        }
    }
}
learning_config = {
    "train_dataloader": {
        "batch_size": 8,
        "shuffle": True,
        "pin_memory": False,
        "drop_last": False,
        "num_workers": 8 #!# With this input and test file 8 works. Otherwise try 0
    },
    "val_dataloader": {},
    "trainer": {
        "accelerator": "gpu",
        "devices": 1,
        "max_epochs": 2000 #!# Choose how many you want. 500 is usually enough