diff --git a/lm_optimizer.py b/lm_optimizer.py
index 85ca1fd5..ae919640 100644
--- a/lm_optimizer.py
+++ b/lm_optimizer.py
@@ -4,36 +4,36 @@ from __future__ import absolute_import, print_function
 
 import sys
 
-import absl.app
 import optuna
 import tensorflow.compat.v1 as tfv1
 from coqui_stt_ctcdecoder import Scorer
 from coqui_stt_training.evaluate import evaluate
-from coqui_stt_training.train import create_model
-from coqui_stt_training.util.config import Config, initialize_globals_from_cli
+from coqui_stt_training.train import create_model, early_training_checks
+from coqui_stt_training.util.config import (
+    Config,
+    initialize_globals_from_cli,
+    log_error,
+)
 from coqui_stt_training.util.evaluate_tools import wer_cer_batch
-from coqui_stt_training.util.flags import FLAGS, create_flags
-from coqui_stt_training.util.logging import log_error
 
 
 def character_based():
     is_character_based = False
-    if FLAGS.scorer_path:
-        scorer = Scorer(
-            FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet
-        )
-        is_character_based = scorer.is_utf8_mode()
+    scorer = Scorer(
+        Config.lm_alpha, Config.lm_beta, Config.scorer_path, Config.alphabet
+    )
+    is_character_based = scorer.is_utf8_mode()
     return is_character_based
 
 
 def objective(trial):
-    FLAGS.lm_alpha = trial.suggest_uniform("lm_alpha", 0, FLAGS.lm_alpha_max)
-    FLAGS.lm_beta = trial.suggest_uniform("lm_beta", 0, FLAGS.lm_beta_max)
+    Config.lm_alpha = trial.suggest_uniform("lm_alpha", 0, Config.lm_alpha_max)
+    Config.lm_beta = trial.suggest_uniform("lm_beta", 0, Config.lm_beta_max)
 
     is_character_based = trial.study.user_attrs["is_character_based"]
 
     samples = []
-    for step, test_file in enumerate(FLAGS.test_files.split(",")):
+    for step, test_file in enumerate(Config.test_files):
         tfv1.reset_default_graph()
 
         current_samples = evaluate([test_file], create_model)
@@ -51,10 +51,18 @@ def objective(trial):
     return cer if is_character_based else wer
 
 
-def main(_):
+def main():
     initialize_globals_from_cli()
+    early_training_checks()
 
-    if not FLAGS.test_files:
+    if not Config.scorer_path:
+        log_error(
+            "Missing --scorer_path: can't optimize scorer alpha and beta "
+            "parameters without a scorer!"
+        )
+        sys.exit(1)
+
+    if not Config.test_files:
         log_error(
             "You need to specify what files to use for evaluation via "
             "the --test_files flag."
@@ -65,7 +73,7 @@ def main(_):
 
     study = optuna.create_study()
     study.set_user_attr("is_character_based", is_character_based)
-    study.optimize(objective, n_jobs=1, n_trials=FLAGS.n_trials)
+    study.optimize(objective, n_jobs=1, n_trials=Config.n_trials)
     print(
         "Best params: lm_alpha={} and lm_beta={} with WER={}".format(
             study.best_params["lm_alpha"],
@@ -76,5 +84,4 @@ def main(_):
 
 
 if __name__ == "__main__":
-    create_flags()
-    absl.app.run(main)
+    main()