diff --git a/train_tacotron.py b/train_tacotron.py
index 168bd1b5..462ec3c3 100644
--- a/train_tacotron.py
+++ b/train_tacotron.py
@@ -1,9 +1,9 @@
 import torch
 from torch import optim
 import torch.nn.functional as F
+from utils import import_from_file
 from utils.display import *
 from utils.dataset import get_tts_datasets
-import hparams as hp
 from utils.text.symbols import symbols
 from utils.paths import Paths
 from models.tacotron import Tacotron
@@ -24,8 +24,10 @@ def main():
     parser.add_argument('--force_train', '-f', action='store_true', help='Forces the model to train past total steps')
     parser.add_argument('--force_gta', '-g', action='store_true', help='Force the model to create GTA features')
     parser.add_argument('--force_cpu', '-c', action='store_true', help='Forces CPU-only training, even when in CUDA capable environment')
+    parser.add_argument('--hp_file', '-p', metavar='FILE', default='hparams.py', help='The file to use for the hyperparameters')
     args = parser.parse_args()
 
+    hp = import_from_file('hparams', args.hp_file)   
     paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
 
     force_train = args.force_train
@@ -273,4 +275,4 @@ def restore_checkpoint(paths: Paths, model: Tacotron, optimizer, *,
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/train_wavernn.py b/train_wavernn.py
index 27bebe90..8867c009 100644
--- a/train_wavernn.py
+++ b/train_wavernn.py
@@ -3,6 +3,7 @@
 import torch
 from torch import optim
 import torch.nn.functional as F
+from utils import import_from_file
 from utils.display import stream, simple_table
 from utils.dataset import get_vocoder_datasets
 from utils.distribution import discretized_mix_logistic_loss
@@ -81,7 +82,7 @@ def voc_train_loop(model: WaveRNN, loss_func, optimizer, train_set, test_set, lr
         print(' ')
 
 
-if __name__ == "__main__":
+def main():
 
     # Parse Arguments
     parser = argparse.ArgumentParser(description='Train WaveRNN Vocoder')
@@ -90,10 +91,15 @@ def voc_train_loop(model: WaveRNN, loss_func, optimizer, train_set, test_set, lr
     parser.add_argument('--force_train', '-f', action='store_true', help='Forces the model to train past total steps')
     parser.add_argument('--gta', '-g', action='store_true', help='train wavernn on GTA features')
     parser.add_argument('--force_cpu', '-c', action='store_true', help='Forces CPU-only training, even when in CUDA capable environment')
-    parser.set_defaults(lr=hp.voc_lr)
-    parser.set_defaults(batch_size=hp.voc_batch_size)
+    parser.add_argument('--hp_file', '-p', metavar='FILE', default='hparams.py', help='The file to use for the hyperparameters')
     args = parser.parse_args()
 
+    hp = import_from_file('hparams', args.hp_file)
+    if args.lr is None:
+        args.lr = hp.voc_lr
+    if args.batch_size is None:
+        args.batch_size = hp.voc_batch_size
+    
     batch_size = args.batch_size
     force_train = args.force_train
     train_gta = args.gta
@@ -151,3 +157,6 @@ def voc_train_loop(model: WaveRNN, loss_func, optimizer, train_set, test_set, lr
 
     print('Training Complete.')
     print('To continue training increase voc_total_steps in hparams.py or use --force_train')
+
+if __name__ == "__main__":
+    main()
diff --git a/utils/__init__.py b/utils/__init__.py
index 8751513c..45258a84 100644
--- a/utils/__init__.py
+++ b/utils/__init__.py
@@ -5,6 +5,8 @@
 import sys
 import torch
 
+from importlib.util import spec_from_file_location, module_from_spec
+
 # Credit: Ryuichi Yamamoto (https://github.com/r9y9/wavenet_vocoder/blob/1717f145c8f8c0f3f85ccdf346b5209fa2e1c920/train.py#L599)
 # Modified by: Ryan Butler (https://github.com/TheButlah)
 # workaround for https://github.com/pytorch/pytorch/issues/15716
@@ -30,3 +32,10 @@ def data_parallel_workaround(model, *input):
     _replicas_ref = replicas
     return y_hat
 
+
+def import_from_file(name, path):
+    """Programmatically imports a module"""
+    spec = spec_from_file_location(name, path)
+    m = module_from_spec(spec)
+    spec.loader.exec_module(m)
+    return m