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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| import argparse | |
| import glob | |
| import os | |
| import numpy as np | |
| from tqdm import tqdm | |
| # from TTS.utils.io import load_config | |
| from TTS.config import load_config | |
| from TTS.tts.datasets import load_tts_samples | |
| 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, help="TTS config file path to define audio processin parameters.") | |
| parser.add_argument("out_path", type=str, help="save path (directory and filename).") | |
| parser.add_argument( | |
| "--data_path", | |
| type=str, | |
| required=False, | |
| help="folder including the target set of wavs overriding dataset config.", | |
| ) | |
| args, overrides = parser.parse_known_args() | |
| CONFIG = load_config(args.config_path) | |
| CONFIG.parse_known_args(overrides, relaxed_parser=True) | |
| # load config | |
| 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.to_dict()) | |
| # load the meta data of target dataset | |
| if args.data_path: | |
| dataset_items = glob.glob(os.path.join(args.data_path, "**", "*.wav"), recursive=True) | |
| else: | |
| dataset_items = load_tts_samples(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["audio_file"]) | |
| 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 linear 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.to_dict() | |
| np.save(output_file_path, stats, allow_pickle=True) | |
| print(f" > stats saved to {output_file_path}") | |
| if __name__ == "__main__": | |
| main() | |