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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import glob
import argparse
import numpy as np
from tqdm import tqdm
from TTS.tts.datasets.preprocess import load_meta_data
from TTS.utils.io 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 to define audio processin parameters.")
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
if 'data_path' in CONFIG.keys():
dataset_items = glob.glob(os.path.join(CONFIG.data_path, '**', '*.wav'), recursive=True)
else:
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 if isinstance(item, str) else 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 = args.out_path
stats = {}
stats['mel_mean'] = mel_mean
stats['mel_std'] = mel_scale
stats['linear_mean'] = linear_mean
stats['linear_std'] = linear_scale
print(f' > Avg mel spec mean: {mel_mean.mean()}')
print(f' > Avg mel spec scale: {mel_scale.mean()}')
print(f' > Avg linear spec mean: {linear_mean.mean()}')
print(f' > Avg lienar spec scale: {linear_scale.mean()}')
# 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)
print(f' > stats saved to {output_file_path}')
if __name__ == "__main__":
main()
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