diff --git a/compute_statistics.py b/compute_statistics.py
new file mode 100755
index 00000000..bbedf7af
--- /dev/null
+++ b/compute_statistics.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os
+import argparse
+
+import numpy as np
+from tqdm import tqdm
+
+from TTS.datasets.preprocess import load_meta_data
+from TTS.utils.generic_utils import load_config
+from TTS.utils.audio import AudioProcessor
+
+def main():
+    """Run preprocessing process."""
+    parser = argparse.ArgumentParser(
+        description="Compute mean and variance of spectrogtram features.")
+    parser.add_argument("--config_path", type=str, required=True,
+                        help="TTS config file path.")
+    parser.add_argument("--out_path", default=None, type=str,
+                        help="directory to save the output file.")
+    args = parser.parse_args()
+
+    # load config
+    CONFIG = load_config(args.config_path)
+    CONFIG.audio['signal_norm'] = False  # do not apply earlier normalization
+    CONFIG.audio['stats_path'] = None  # discard pre-defined stats
+
+    # load audio processor
+    ap = AudioProcessor(**CONFIG.audio)
+
+    # load the meta data of target dataset
+    dataset_items = load_meta_data(CONFIG.datasets)[0]  # take only train data
+    print(f" > There are {len(dataset_items)} files.")
+
+    mel_sum = 0
+    mel_square_sum = 0
+    linear_sum = 0
+    linear_square_sum = 0
+    N = 0
+    for item in tqdm(dataset_items):
+        # compute features
+        wav = ap.load_wav(item[1])
+        linear = ap.spectrogram(wav)
+        mel = ap.melspectrogram(wav)
+
+        # compute stats
+        N += mel.shape[1]
+        mel_sum += mel.sum(1)
+        linear_sum += linear.sum(1)
+        mel_square_sum += (mel ** 2).sum(axis=1)
+        linear_square_sum += (linear ** 2).sum(axis=1)
+
+    mel_mean = mel_sum / N
+    mel_scale = np.sqrt(mel_square_sum / N - mel_mean ** 2)
+    linear_mean = linear_sum / N
+    linear_scale = np.sqrt(linear_square_sum / N - linear_mean ** 2)
+
+    output_file_path = os.path.join(args.out_path, "scale_stats.npy")
+    stats = {}
+    stats['mel_mean'] = mel_mean
+    stats['mel_std'] = mel_scale
+    stats['linear_mean'] = linear_mean
+    stats['linear_std'] = linear_scale
+
+    # set default config values for mean-var scaling
+    CONFIG.audio['stats_path'] = output_file_path
+    CONFIG.audio['signal_norm'] = True
+    # remove redundant values
+    del CONFIG.audio['max_norm']
+    del CONFIG.audio['min_level_db']
+    del CONFIG.audio['symmetric_norm']
+    del CONFIG.audio['clip_norm']
+    stats['audio_config'] = CONFIG.audio
+    np.save(output_file_path, stats, allow_pickle=True)
+
+
+if __name__ == "__main__":
+    main()