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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Calculate statistics of feature files."""
import argparse
import logging
import os
import numpy as np
import yaml
from sklearn.preprocessing import StandardScaler
from tqdm import tqdm
from parallel_wavegan.datasets import MelDataset
from parallel_wavegan.datasets import MelSCPDataset
from parallel_wavegan.utils import read_hdf5
from parallel_wavegan.utils import write_hdf5
def main():
"""Run preprocessing process."""
parser = argparse.ArgumentParser(
description="Compute mean and variance of dumped raw features "
"(See detail in parallel_wavegan/bin/compute_statistics.py)."
)
parser.add_argument(
"--feats-scp",
"--scp",
default=None,
type=str,
help="kaldi-style feats.scp file. "
"you need to specify either feats-scp or rootdir.",
)
parser.add_argument(
"--rootdir",
type=str,
help="directory including feature files. "
"you need to specify either feats-scp or rootdir.",
)
parser.add_argument(
"--config",
type=str,
required=True,
help="yaml format configuration file.",
)
parser.add_argument(
"--dumpdir",
default=None,
type=str,
required=True,
help="directory to save statistics. if not provided, "
"stats will be saved in the above root directory. (default=None)",
)
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)",
)
args = parser.parse_args()
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
else:
logging.basicConfig(
level=logging.WARN,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
logging.warning("Skip DEBUG/INFO messages")
# load config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
# check arguments
if (args.feats_scp is not None and args.rootdir is not None) or (
args.feats_scp is None and args.rootdir is None
):
raise ValueError("Please specify either --rootdir or --feats-scp.")
# check directory existence
if not os.path.exists(args.dumpdir):
os.makedirs(args.dumpdir)
# get dataset
if args.feats_scp is None:
if config["format"] == "hdf5":
mel_query = "*.h5"
mel_load_fn = lambda x: read_hdf5(x, "feats") # NOQA
elif config["format"] == "npy":
mel_query = "*-feats.npy"
mel_load_fn = np.load
else:
raise ValueError("support only hdf5 or npy format.")
dataset = MelDataset(args.rootdir, mel_query=mel_query, mel_load_fn=mel_load_fn)
else:
dataset = MelSCPDataset(args.feats_scp)
logging.info(f"The number of files = {len(dataset)}.")
# calculate statistics
scaler = StandardScaler()
for mel in tqdm(dataset):
scaler.partial_fit(mel)
if config["format"] == "hdf5":
write_hdf5(
os.path.join(args.dumpdir, "stats.h5"),
"mean",
scaler.mean_.astype(np.float32),
)
write_hdf5(
os.path.join(args.dumpdir, "stats.h5"),
"scale",
scaler.scale_.astype(np.float32),
)
else:
stats = np.stack([scaler.mean_, scaler.scale_], axis=0)
np.save(
os.path.join(args.dumpdir, "stats.npy"),
stats.astype(np.float32),
allow_pickle=False,
)
if __name__ == "__main__":
main()
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