diff --git a/TTS/bin/distribute.py b/TTS/bin/distribute.py
new file mode 100644
index 00000000..390bd738
--- /dev/null
+++ b/TTS/bin/distribute.py
@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os
+import sys
+import pathlib
+import time
+import subprocess
+import argparse
+import torch
+
+
+def main():
+    """
+    Call train.py as a new process and pass command arguments
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--script',
+        type=str,
+        help='Target training script to distibute.')
+    parser.add_argument(
+        '--continue_path',
+        type=str,
+        help='Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
+        default='',
+        required='--config_path' not in sys.argv)
+    parser.add_argument(
+        '--restore_path',
+        type=str,
+        help='Model file to be restored. Use to finetune a model.',
+        default='')
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        help='Path to config file for training.',
+        required='--continue_path' not in sys.argv
+    )
+    args = parser.parse_args()
+
+    num_gpus = torch.cuda.device_count()
+    group_id = time.strftime("%Y_%m_%d-%H%M%S")
+
+    # set arguments for train.py
+    folder_path = pathlib.Path(__file__).parent.absolute()
+    command = [os.path.join(folder_path, args.script)]
+    command.append('--continue_path={}'.format(args.continue_path))
+    command.append('--restore_path={}'.format(args.restore_path))
+    command.append('--config_path={}'.format(args.config_path))
+    command.append('--group_id=group_{}'.format(group_id))
+    command.append('')
+
+    # run processes
+    processes = []
+    for i in range(num_gpus):
+        my_env = os.environ.copy()
+        my_env["PYTHON_EGG_CACHE"] = "/tmp/tmp{}".format(i)
+        command[-1] = '--rank={}'.format(i)
+        stdout = None if i == 0 else open(os.devnull, 'w')
+        p = subprocess.Popen(['python3'] + command, stdout=stdout, env=my_env)
+        processes.append(p)
+        print(command)
+
+    for p in processes:
+        p.wait()
+
+
+if __name__ == '__main__':
+    main()