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112 lines
3.5 KiB
112 lines
3.5 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Normalize feature files and dump them."""
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import argparse
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import logging
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from operator import itemgetter
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from pathlib import Path
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import jsonlines
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from tqdm import tqdm
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from paddlespeech.t2s.datasets.data_table import DataTable
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def main():
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"""Run preprocessing process."""
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parser = argparse.ArgumentParser(
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description="Normalize dumped raw features.")
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parser.add_argument(
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"--metadata",
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type=str,
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required=True,
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help="directory including feature files to be normalized. "
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"you need to specify either *-scp or rootdir.")
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parser.add_argument(
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"--dumpdir",
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type=str,
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required=True,
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help="directory to dump normalized feature files.")
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parser.add_argument(
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"--stats", type=str, required=True, help="statistics file.")
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parser.add_argument(
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"--skip-wav-copy",
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default=False,
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action="store_true",
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help="whether to skip the copy of wav files.")
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args = parser.parse_args()
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dumpdir = Path(args.dumpdir).expanduser()
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# use absolute path
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dumpdir = dumpdir.resolve()
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dumpdir.mkdir(parents=True, exist_ok=True)
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# get dataset
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with jsonlines.open(args.metadata, 'r') as reader:
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metadata = list(reader)
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dataset = DataTable(
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metadata,
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fields=["utt_id", "wave", "feats"],
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converters={
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'utt_id': None,
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'wave': None if args.skip_wav_copy else np.load,
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'feats': np.load,
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})
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logging.info(f"The number of files = {len(dataset)}.")
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# restore scaler
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scaler = StandardScaler()
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scaler.mean_ = np.load(args.stats)[0]
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scaler.scale_ = np.load(args.stats)[1]
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# from version 0.23.0, this information is needed
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scaler.n_features_in_ = scaler.mean_.shape[0]
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# process each file
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output_metadata = []
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for item in tqdm(dataset):
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utt_id = item['utt_id']
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wave = item['wave']
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mel = item['feats']
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# normalize
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mel = scaler.transform(mel)
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# save
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mel_path = dumpdir / f"{utt_id}_feats.npy"
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np.save(mel_path, mel.astype(np.float32), allow_pickle=False)
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if not args.skip_wav_copy:
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wav_path = dumpdir / f"{utt_id}_wave.npy"
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np.save(wav_path, wave.astype(np.float32), allow_pickle=False)
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else:
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wav_path = wave
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output_metadata.append({
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'utt_id': utt_id,
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'wave': str(wav_path),
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'feats': str(mel_path),
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})
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output_metadata.sort(key=itemgetter('utt_id'))
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output_metadata_path = Path(args.dumpdir) / "metadata.jsonl"
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with jsonlines.open(output_metadata_path, 'w') as writer:
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for item in output_metadata:
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writer.write(item)
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logging.info(f"metadata dumped into {output_metadata_path}")
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if __name__ == "__main__":
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main()
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