155 lines
5.0 KiB
Python
155 lines
5.0 KiB
Python
# 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 parakeet.datasets.data_table import DataTable
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from parakeet.frontend.vocab import Vocab
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from config import get_cfg_default
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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 (See detail in parallel_wavegan/bin/normalize.py)."
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)
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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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"--phones",
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type=str,
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default="phones.txt",
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help="phone vocabulary file.")
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parser.add_argument(
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"--tones", type=str, default="tones.txt", help="tone vocabulary file.")
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parser.add_argument(
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"--config", type=str, help="yaml format configuration file.")
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parser.add_argument(
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"--verbose",
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type=int,
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default=1,
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help="logging level. higher is more logging. (default=1)")
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args = parser.parse_args()
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# set logger
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if args.verbose > 1:
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
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)
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elif args.verbose > 0:
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
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)
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else:
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logging.basicConfig(
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level=logging.WARN,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
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)
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logging.warning('Skip DEBUG/INFO messages')
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# load config
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config = get_cfg_default()
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if args.config:
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config.merge_from_file(args.config)
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# check directory existence
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dumpdir = Path(args.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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metadata_dir = Path(args.metadata).parent
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for item in metadata:
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item["feats"] = str(metadata_dir / item["feats"])
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dataset = DataTable(
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metadata, converters={
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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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with open(args.phones, 'rt') as f:
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phones = [line.strip() for line in f.readlines()]
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with open(args.tones, 'rt') as f:
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tones = [line.strip() for line in f.readlines()]
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voc_phones = Vocab(phones, start_symbol=None, end_symbol=None)
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voc_tones = Vocab(tones, start_symbol=None, end_symbol=None)
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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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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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phone_ids = [voc_phones.lookup(p) for p in item['phones']]
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tone_ids = [voc_tones.lookup(t) for t in item['tones']]
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output_metadata.append({
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'utt_id': utt_id,
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'phones': phone_ids,
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'tones': tone_ids,
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'num_phones': item['num_phones'],
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'num_frames': item['num_frames'],
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'durations': item['durations'],
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'feats': str(mel_path.relative_to(dumpdir)),
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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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