mirror of https://github.com/coqui-ai/TTS.git
90 lines
2.9 KiB
Python
Executable File
90 lines
2.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import argparse
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import glob
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import os
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import numpy as np
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from tqdm import tqdm
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.io import load_config
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def main():
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"""Run preprocessing process."""
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parser = argparse.ArgumentParser(description="Compute mean and variance of spectrogtram features.")
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parser.add_argument(
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"--config_path", type=str, required=True, help="TTS config file path to define audio processin parameters."
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)
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parser.add_argument("--out_path", type=str, required=True, help="save path (directory and filename).")
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args = parser.parse_args()
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# load config
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CONFIG = load_config(args.config_path)
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CONFIG.audio["signal_norm"] = False # do not apply earlier normalization
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CONFIG.audio["stats_path"] = None # discard pre-defined stats
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# load audio processor
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ap = AudioProcessor(**CONFIG.audio)
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# load the meta data of target dataset
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if "data_path" in CONFIG.keys():
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dataset_items = glob.glob(os.path.join(CONFIG.data_path, "**", "*.wav"), recursive=True)
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else:
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dataset_items = load_meta_data(CONFIG.datasets)[0] # take only train data
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print(f" > There are {len(dataset_items)} files.")
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mel_sum = 0
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mel_square_sum = 0
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linear_sum = 0
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linear_square_sum = 0
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N = 0
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for item in tqdm(dataset_items):
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# compute features
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wav = ap.load_wav(item if isinstance(item, str) else item[1])
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linear = ap.spectrogram(wav)
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mel = ap.melspectrogram(wav)
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# compute stats
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N += mel.shape[1]
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mel_sum += mel.sum(1)
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linear_sum += linear.sum(1)
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mel_square_sum += (mel ** 2).sum(axis=1)
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linear_square_sum += (linear ** 2).sum(axis=1)
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mel_mean = mel_sum / N
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mel_scale = np.sqrt(mel_square_sum / N - mel_mean ** 2)
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linear_mean = linear_sum / N
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linear_scale = np.sqrt(linear_square_sum / N - linear_mean ** 2)
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output_file_path = args.out_path
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stats = {}
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stats["mel_mean"] = mel_mean
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stats["mel_std"] = mel_scale
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stats["linear_mean"] = linear_mean
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stats["linear_std"] = linear_scale
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print(f" > Avg mel spec mean: {mel_mean.mean()}")
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print(f" > Avg mel spec scale: {mel_scale.mean()}")
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print(f" > Avg linear spec mean: {linear_mean.mean()}")
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print(f" > Avg lienar spec scale: {linear_scale.mean()}")
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# set default config values for mean-var scaling
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CONFIG.audio["stats_path"] = output_file_path
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CONFIG.audio["signal_norm"] = True
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# remove redundant values
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del CONFIG.audio["max_norm"]
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del CONFIG.audio["min_level_db"]
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del CONFIG.audio["symmetric_norm"]
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del CONFIG.audio["clip_norm"]
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stats["audio_config"] = CONFIG.audio
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np.save(output_file_path, stats, allow_pickle=True)
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print(f" > stats saved to {output_file_path}")
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if __name__ == "__main__":
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main()
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