diff --git a/spkr-attr/classifier/model.py b/spkr-attr/classifier/model.py
deleted file mode 100644
index af88da54..00000000
--- a/spkr-attr/classifier/model.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import argparse
-import pickle
-
-import numpy as np
-import tensorflow as tf
-from clearml import Task
-from sklearn.metrics import accuracy_score, confusion_matrix, f1_score
-from sklearn.model_selection import KFold, cross_val_score
-
-
-def load_data(train, labels):
-    t  = np.load(train)
-    l = np.load(labels)
-    return t,l
-
-def compute_metrics(y_pred, y_true):
-    acc = accuracy_score(y_true.argmax(axis=1),y_pred.argmax(axis=1))
-    print(f"Model Accuracy on untrained data: {acc}")
-    conf_matrix = confusion_matrix(y_true.argmax(axis=1),y_pred.argmax(axis=1))
-    print(f"Confusion matrix on {y_true.shape[-1]} classes: \n {conf_matrix}")
-    f1 = f1_score(y_true.argmax(axis=1),y_pred.argmax(axis=1),average='weighted')
-    print(f"Weighted f1 score: {f1}")
-
-def test_model(test,test_labels,model):
-    test, y_true = load_data(test, test_labels)
-    y_pred = model.predict(test, verbose=1)
-    compute_metrics(y_pred,y_true)
-
-def baseline_model(class_num):
-    model = tf.keras.models.Sequential()
-    model.add(tf.keras.layers.Dense(1024, input_dim=512, activation='relu'))
-    model.add(tf.keras.layers.Dropout(0.2))
-    model.add(tf.keras.layers.Dense(512, activation='relu'))
-    model.add(tf.keras.layers.Dropout(0.5))
-    model.add(tf.keras.layers.Dense(128, activation='relu'))
-    model.add(tf.keras.layers.Dense(64, activation='relu'))
-    model.add(tf.keras.layers.Dense(class_num, activation='softmax'))
-    opt = tf.keras.optimizers.Adam(learning_rate=0.0001)
-    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
-    return model
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "train_data",
-        nargs='?',
-    )
-    parser.add_argument(
-        "train_labels",
-        nargs='?',
-    )
-    parser.add_argument(
-        "test_data",
-        nargs='?',
-    )
-    parser.add_argument(
-        "test_labels",
-        nargs='?',
-    )
-    parser.add_argument(
-            '--eval',
-            action='store_true'
-    )
-    parser.add_argument(
-            '--load_ckpt',
-            required=False,
-    )
-    parser.add_argument(
-            '--predict',
-            action='store_true'
-    )
-    parser.add_argument(
-            'class_weights',
-    )
-    parser.add_argument(
-            'epoch',
-    )
-    parser.add_argument(
-        "--clearml_project",
-        default="YourTTS-sprint2",
-    )
-    parser.add_argument(
-        "--clearml_task",
-        default="attribute-classifier",
-    )
-    parser.add_argument(
-        "label",
-    )
-
-
-    args = parser.parse_args()
-    task = Task.init(
-        project_name=args.clearml_project,
-        task_name=f"{args.clearml_task}-{args.label}",
-    )
-    train, labels = load_data(args.train_data, args.train_labels)
-    model = baseline_model(len(np.unique(labels,axis=0)))
-    with open(args.class_weights, 'rb') as f:
-        class_weights = pickle.load(f)
-    if not args.eval and not args.predict:
-        print(f"Training on: {train.shape} and testing on {labels.shape}")
-        EPOCHS = int(args.epoch)
-        checkpoint_filepath = "checkpoints/checkpoint"
-        model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
-        filepath=checkpoint_filepath,
-        save_weights_only=True,
-        monitor='val_accuracy',
-        verbose=1,
-        mode='max',
-        save_best_only=True)
-        model.fit(epochs=EPOCHS,x=train,y=labels,validation_split=0.2,batch_size=100, callbacks=[model_checkpoint_callback], class_weight=class_weights)
-    else:
-        model.load_weights(args.load_ckpt)
-    test_model(args.test_data, args.test_labels,model)
-    if(args.predict):
-        test = np.load(test)
-        y_pred = model.predict(test, verbose=1)
-        num_labels = np.where(y_pred==1)[1]
-        np.save("predicted_labels.npy",num_labels)
-if __name__ == "__main__":
-    main()
-#    kfold = KFold(n_splits=10, shuffle=True)
-#    results = cross_val_score(estimator, train, labels, cv=kfold)
-#    print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))