deepSpeech_test.py 文件源码

def inference(predictions_op, true_labels_op, display, sess):
    """ Perform inference per batch on pre-trained model.
    This function performs inference and computes the CER per utterance.
    Args:
        predictions_op: Prediction op
        true_labels_op: True Labels op
        display: print sample predictions if True
        sess: default session to evaluate the ops.
    Returns:
        char_err_rate: list of CER per utterance.
    """
    char_err_rate = []
    # Perform inference of batch worth of data at a time.
    [predictions, true_labels] = sess.run([predictions_op,
                                           true_labels_op])
    pred_label = sparse_to_labels(predictions[0][0])
    actual_label = sparse_to_labels(true_labels)
    for (label, pred) in zip(actual_label, pred_label):
        char_err_rate.append(distance(label, pred)/len(label))

    if display:
        # Print sample responses
        for i in range(ARGS.batch_size):
            print(actual_label[i] + ' vs ' + pred_label[i])
    return char_err_rate