PaddleSpeech/utils/compute_statistics.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Calculate statistics of feature files."""
import argparse
import logging
from pathlib import Path

import jsonlines
import numpy as np
from sklearn.preprocessing import StandardScaler
from tqdm import tqdm

from paddlespeech.t2s.datasets.data_table import DataTable


def main():
    """Run preprocessing process."""
    parser = argparse.ArgumentParser(
        description="Compute mean and variance of dumped raw features.")
    parser.add_argument(
        "--metadata", type=str, help="json file with id and file paths ")
    parser.add_argument(
        "--field-name",
        type=str,
        help="name of the field to compute statistics for.")

    parser.add_argument(
        "--output",
        type=str,
        help="path to save statistics. if not provided, "
        "stats will be saved in the above root directory with name stats.npy")

    def str2bool(str):
        return True if str.lower() == 'true' else False

    parser.add_argument(
        "--use-relative-path",
        type=str2bool,
        default=False,
        help="whether use relative path in metadata")

    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')

    # check directory existence
    if args.output is None:
        args.output = Path(
            args.metadata).parent.with_name(args.field_name + "_stats.npy")
    else:
        args.output = Path(args.output)
    args.output.parent.mkdir(parents=True, exist_ok=True)

    with jsonlines.open(args.metadata, 'r') as reader:
        metadata = list(reader)

    if args.use_relative_path:
        # if use_relative_path in preprocess, covert it to absolute path here
        metadata_dir = Path(args.metadata).parent
        for item in metadata:
            item["feats"] = str(metadata_dir / item["feats"])
    dataset = DataTable(
        metadata,
        fields=[args.field_name],
        converters={args.field_name: np.load}, )
    logging.info(f"The number of files = {len(dataset)}.")

    # calculate statistics
    scaler = StandardScaler()
    for datum in tqdm(dataset):
        # StandardScalar supports (*, num_features) by default
        scaler.partial_fit(datum[args.field_name])

    stats = np.stack([scaler.mean_, scaler.scale_], axis=0)
    np.save(str(args.output), stats.astype(np.float32), allow_pickle=False)


if __name__ == "__main__":
    main()