ParallelWaveGAN/parallel_wavegan/bin/normalize.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Tomoki Hayashi
#  MIT License (https://opensource.org/licenses/MIT)

"""Normalize feature files and dump them."""

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 AudioMelDataset
from parallel_wavegan.datasets import AudioMelSCPDataset
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="Normalize dumped raw features (See detail in parallel_wavegan/bin/normalize.py)."
    )
    parser.add_argument(
        "--rootdir",
        default=None,
        type=str,
        help="directory including feature files to be normalized. "
        "you need to specify either *-scp or rootdir.",
    )
    parser.add_argument(
        "--wav-scp",
        default=None,
        type=str,
        help="kaldi-style wav.scp file. "
        "you need to specify either *-scp or rootdir.",
    )
    parser.add_argument(
        "--feats-scp",
        default=None,
        type=str,
        help="kaldi-style feats.scp file. "
        "you need to specify either *-scp or rootdir.",
    )
    parser.add_argument(
        "--segments",
        default=None,
        type=str,
        help="kaldi-style segments file.",
    )
    parser.add_argument(
        "--dumpdir",
        type=str,
        required=True,
        help="directory to dump normalized feature files.",
    )
    parser.add_argument(
        "--stats",
        type=str,
        required=True,
        help="statistics file.",
    )
    parser.add_argument(
        "--skip-wav-copy",
        default=False,
        action="store_true",
        help="whether to skip the copy of wav files.",
    )
    parser.add_argument(
        "--config", type=str, required=True, help="yaml format configuration file."
    )
    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.rootdir is not None:
        if config["format"] == "hdf5":
            audio_query, mel_query = "*.h5", "*.h5"
            audio_load_fn = lambda x: read_hdf5(x, "wave")  # NOQA
            mel_load_fn = lambda x: read_hdf5(x, "feats")  # NOQA
        elif config["format"] == "npy":
            audio_query, mel_query = "*-wave.npy", "*-feats.npy"
            audio_load_fn = np.load
            mel_load_fn = np.load
        else:
            raise ValueError("support only hdf5 or npy format.")
        if not args.skip_wav_copy:
            dataset = AudioMelDataset(
                root_dir=args.rootdir,
                audio_query=audio_query,
                mel_query=mel_query,
                audio_load_fn=audio_load_fn,
                mel_load_fn=mel_load_fn,
                return_utt_id=True,
            )
        else:
            dataset = MelDataset(
                root_dir=args.rootdir,
                mel_query=mel_query,
                mel_load_fn=mel_load_fn,
                return_utt_id=True,
            )
    else:
        if not args.skip_wav_copy:
            dataset = AudioMelSCPDataset(
                wav_scp=args.wav_scp,
                feats_scp=args.feats_scp,
                segments=args.segments,
                return_utt_id=True,
            )
        else:
            dataset = MelSCPDataset(
                feats_scp=args.feats_scp,
                return_utt_id=True,
            )
    logging.info(f"The number of files = {len(dataset)}.")

    # restore scaler
    scaler = StandardScaler()
    if config["format"] == "hdf5":
        scaler.mean_ = read_hdf5(args.stats, "mean")
        scaler.scale_ = read_hdf5(args.stats, "scale")
    elif config["format"] == "npy":
        scaler.mean_ = np.load(args.stats)[0]
        scaler.scale_ = np.load(args.stats)[1]
    else:
        raise ValueError("support only hdf5 or npy format.")
    # from version 0.23.0, this information is needed
    scaler.n_features_in_ = scaler.mean_.shape[0]

    # process each file
    for items in tqdm(dataset):
        if not args.skip_wav_copy:
            utt_id, audio, mel = items
        else:
            utt_id, mel = items

        # normalize
        mel = scaler.transform(mel)

        # save
        if config["format"] == "hdf5":
            write_hdf5(
                os.path.join(args.dumpdir, f"{utt_id}.h5"),
                "feats",
                mel.astype(np.float32),
            )
            if not args.skip_wav_copy:
                write_hdf5(
                    os.path.join(args.dumpdir, f"{utt_id}.h5"),
                    "wave",
                    audio.astype(np.float32),
                )
        elif config["format"] == "npy":
            np.save(
                os.path.join(args.dumpdir, f"{utt_id}-feats.npy"),
                mel.astype(np.float32),
                allow_pickle=False,
            )
            if not args.skip_wav_copy:
                np.save(
                    os.path.join(args.dumpdir, f"{utt_id}-wave.npy"),
                    audio.astype(np.float32),
                    allow_pickle=False,
                )
        else:
            raise ValueError("support only hdf5 or npy format.")


if __name__ == "__main__":
    main()