import tensorflow as tf

    
    log("Checkpoint path: {}".format(checkpoint_fpath))
    log("Loading training data from: {}".format(metadat_fpath))
    log("Using model: Tacotron")
    log(hparams_debug_string())
    
    # Start by setting a seed for repeatability
    tf.set_random_seed(hparams.tacotron_random_seed)
    
    # Set up data feeder
    coord = tf.train.Coordinator()
    with tf.variable_scope("datafeeder") as scope:
        feeder = Feeder(coord, metadat_fpath, hparams)
    
    # Set up model:
    global_step = tf.Variable(0, name="global_step", trainable=False)
    model, stats = model_train_mode(args, feeder, hparams, global_step)
    eval_model = model_test_mode(args, feeder, hparams, global_step)
    
    # Embeddings metadata
