diff --git a/synthesis.py b/synthesis.py
deleted file mode 100644
index c1ff8290..00000000
--- a/synthesis.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from network import *
-from data import inv_spectrogram, find_endpoint, save_wav, spectrogram
-import numpy as np
-import argparse
-import os
-import sys
-import io
-from text import text_to_sequence
-
-use_cuda = torch.cuda.is_available()
-
-
-def main(args):
-
-    # Make model
-    if use_cuda:
-        model = nn.DataParallel(Tacotron().cuda())
-
-    # Load checkpoint
-    try:
-        checkpoint = torch.load(os.path.join(
-            hp.checkpoint_path, 'checkpoint_%d.pth.tar' % args.restore_step))
-        model.load_state_dict(checkpoint['model'])
-        print("\n--------model restored at step %d--------\n" %
-              args.restore_step)
-
-    except:
-        raise FileNotFoundError("\n------------Model not exists------------\n")
-
-    # Evaluation
-    model = model.eval()
-
-    # Make result folder if not exists
-    if not os.path.exists(hp.output_path):
-        os.mkdir(hp.output_path)
-
-    # Sentences for generation
-    sentences = [
-        "I try my best to translate text to speech. But I know I need more work",
-        "The new Firefox, Fast for good.",
-        "Technology is continually providing us with new ways to create and publish stories.",
-        "For these stories to achieve their full impact, it requires tool.",
-        "I am allien and I am here to destron your world."
-    ]
-
-    # Synthesis and save to wav files
-    for i, text in enumerate(sentences):
-        wav = generate(model, text)
-        path = os.path.join(hp.output_path, 'result_%d_%d.wav' %
-                            (args.restore_step, i + 1))
-        with open(path, 'wb') as f:
-            f.write(wav)
-
-        f.close()
-        print("save wav file at step %d ..." % (i + 1))
-
-
-def generate(model, text):
-
-    # Text to index sequence
-    cleaner_names = [x.strip() for x in hp.cleaners.split(',')]
-    seq = np.expand_dims(np.asarray(text_to_sequence(
-        text, cleaner_names), dtype=np.int32), axis=0)
-
-    # Provide [GO] Frame
-    mel_input = np.zeros([seq.shape[0], hp.num_mels, 1], dtype=np.float32)
-
-    # Variables
-    characters = Variable(torch.from_numpy(seq).type(
-        torch.cuda.LongTensor), volatile=True).cuda()
-    mel_input = Variable(torch.from_numpy(mel_input).type(
-        torch.cuda.FloatTensor), volatile=True).cuda()
-
-    # Spectrogram to wav
-    _, linear_output = model.forward(characters, mel_input)
-    wav = inv_spectrogram(linear_output[0].data.cpu().numpy())
-    wav = wav[:find_endpoint(wav)]
-    out = io.BytesIO()
-    save_wav(wav, out)
-
-    return out.getvalue()
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--restore_step', type=int,
-                        help='Global step to restore checkpoint', default=0)
-    parser.add_argument('--batch_size', type=int, help='Batch size', default=1)
-    args = parser.parse_args()
-    main(args)