diff --git a/lm_optimizer.py b/lm_optimizer.py
new file mode 100644
index 0000000000..9e01ab96d0
--- /dev/null
+++ b/lm_optimizer.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import, print_function
+
+import sys
+
+import optuna
+import absl.app
+from ds_ctcdecoder import Scorer
+import tensorflow.compat.v1 as tfv1
+
+from DeepSpeech import create_model
+from evaluate import evaluate
+from util.config import Config, initialize_globals
+from util.flags import create_flags, FLAGS
+from util.logging import log_error
+from util.evaluate_tools import wer_cer_batch
+
+
+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()
+    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)
+
+    tfv1.reset_default_graph()
+    samples = evaluate(FLAGS.test_files.split(','), create_model)
+
+    is_character_based = trial.study.user_attrs['is_character_based']
+
+    wer, cer = wer_cer_batch(samples)
+    return cer if is_character_based else wer
+
+def main(_):
+    initialize_globals()
+
+    if not FLAGS.test_files:
+        log_error('You need to specify what files to use for evaluation via '
+                  'the --test_files flag.')
+        sys.exit(1)
+
+    is_character_based = character_based()
+
+    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)
+    print('Best params: lm_alpha={} and lm_beta={} with WER={}'.format(study.best_params['lm_alpha'],
+                                                                       study.best_params['lm_beta'],
+                                                                       study.best_value))
+
+
+if __name__ == '__main__':
+    create_flags()
+    absl.app.run(main)
diff --git a/requirements.txt b/requirements.txt
index 742b824461..54159a372b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -18,3 +18,6 @@ bs4
 requests
 librosa
 soundfile
+
+# Requirements for optimizer
+optuna
diff --git a/util/flags.py b/util/flags.py
index 5057d76c25..195e62f716 100644
--- a/util/flags.py
+++ b/util/flags.py
@@ -166,6 +166,12 @@ def create_flags():
 
     f.DEFINE_string('one_shot_infer', '', 'one-shot inference mode: specify a wav file and the script will load the checkpoint and perform inference on it.')
 
+    # Optimizer mode
+
+    f.DEFINE_float('lm_alpha_max', 5, 'the maximum of the alpha hyperparameter of the CTC decoder explored during hyperparameter optimization. Language Model weight.')
+    f.DEFINE_float('lm_beta_max', 5, 'the maximum beta hyperparameter of the CTC decoder explored during hyperparameter optimization. Word insertion weight.')
+    f.DEFINE_integer('n_trials', 2400, 'the number of trials to run during hyperparameter optimization.')
+
     # Register validators for paths which require a file to be specified
 
     f.register_validator('alphabet_config_path',