PaddleSpeech/examples/vctk/fastspeech2/normalize.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.
"""Normalize feature files and dump them."""

import argparse
import logging
from operator import itemgetter
from pathlib import Path

import jsonlines
import numpy as np
from parakeet.datasets.data_table import DataTable
from sklearn.preprocessing import StandardScaler
from tqdm import tqdm


def main():
    """Run preprocessing process."""
    parser = argparse.ArgumentParser(
        description="Normalize dumped raw features (See detail in parallel_wavegan/bin/normalize.py)."
    )
    parser.add_argument(
        "--metadata",
        type=str,
        required=True,
        help="directory including feature files to be normalized. "
        "you need to specify either *-scp or rootdir.")

    parser.add_argument(
        "--dumpdir",
        type=str,
        required=True,
        help="directory to dump normalized feature files.")
    parser.add_argument(
        "--speech-stats",
        type=str,
        required=True,
        help="speech statistics file.")
    parser.add_argument(
        "--pitch-stats", type=str, required=True, help="pitch statistics file.")
    parser.add_argument(
        "--energy-stats",
        type=str,
        required=True,
        help="energy statistics file.")
    parser.add_argument(
        "--phones-dict", type=str, default=None, help="phone vocabulary file.")
    parser.add_argument(
        "--speaker-dict", type=str, default=None, help="speaker id map 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')

    dumpdir = Path(args.dumpdir).expanduser()
    # use absolute path
    dumpdir = dumpdir.resolve()
    dumpdir.mkdir(parents=True, exist_ok=True)

    # get dataset
    with jsonlines.open(args.metadata, 'r') as reader:
        metadata = list(reader)
    dataset = DataTable(
        metadata,
        converters={
            "speech": np.load,
            "pitch": np.load,
            "energy": np.load,
        })
    logging.info(f"The number of files = {len(dataset)}.")

    # restore scaler
    speech_scaler = StandardScaler()
    speech_scaler.mean_ = np.load(args.speech_stats)[0]
    speech_scaler.scale_ = np.load(args.speech_stats)[1]
    speech_scaler.n_features_in_ = speech_scaler.mean_.shape[0]

    pitch_scaler = StandardScaler()
    pitch_scaler.mean_ = np.load(args.pitch_stats)[0]
    pitch_scaler.scale_ = np.load(args.pitch_stats)[1]
    pitch_scaler.n_features_in_ = pitch_scaler.mean_.shape[0]

    energy_scaler = StandardScaler()
    energy_scaler.mean_ = np.load(args.energy_stats)[0]
    energy_scaler.scale_ = np.load(args.energy_stats)[1]
    energy_scaler.n_features_in_ = energy_scaler.mean_.shape[0]

    vocab_phones = {}
    with open(args.phones_dict, 'rt') as f:
        phn_id = [line.strip().split() for line in f.readlines()]
    for phn, id in phn_id:
        vocab_phones[phn] = int(id)

    vocab_speaker = {}
    with open(args.speaker_dict, 'rt') as f:
        spk_id = [line.strip().split() for line in f.readlines()]
    for spk, id in spk_id:
        vocab_speaker[spk] = int(id)

    # process each file
    output_metadata = []

    for item in tqdm(dataset):
        utt_id = item['utt_id']
        speech = item['speech']
        pitch = item['pitch']
        energy = item['energy']
        # normalize
        speech = speech_scaler.transform(speech)
        speech_dir = dumpdir / "data_speech"
        speech_dir.mkdir(parents=True, exist_ok=True)
        speech_path = speech_dir / f"{utt_id}_speech.npy"
        np.save(speech_path, speech.astype(np.float32), allow_pickle=False)

        pitch = pitch_scaler.transform(pitch)
        pitch_dir = dumpdir / "data_pitch"
        pitch_dir.mkdir(parents=True, exist_ok=True)
        pitch_path = pitch_dir / f"{utt_id}_pitch.npy"
        np.save(pitch_path, pitch.astype(np.float32), allow_pickle=False)

        energy = energy_scaler.transform(energy)
        energy_dir = dumpdir / "data_energy"
        energy_dir.mkdir(parents=True, exist_ok=True)
        energy_path = energy_dir / f"{utt_id}_energy.npy"
        np.save(energy_path, energy.astype(np.float32), allow_pickle=False)
        phone_ids = [vocab_phones[p] for p in item['phones']]
        spk_id = vocab_speaker[item["speaker"]]
        record = {
            "utt_id": item['utt_id'],
            "spk_id": spk_id,
            "text": phone_ids,
            "text_lengths": item['text_lengths'],
            "speech_lengths": item['speech_lengths'],
            "durations": item['durations'],
            "speech": str(speech_path),
            "pitch": str(pitch_path),
            "energy": str(energy_path)
        }
        output_metadata.append(record)
    output_metadata.sort(key=itemgetter('utt_id'))
    output_metadata_path = Path(args.dumpdir) / "metadata.jsonl"
    with jsonlines.open(output_metadata_path, 'w') as writer:
        for item in output_metadata:
            writer.write(item)
    logging.info(f"metadata dumped into {output_metadata_path}")


if __name__ == "__main__":
    main()