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131 lines
4.3 KiB
131 lines
4.3 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 (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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"--speech-stats",
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type=str,
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required=True,
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help="speech statistics file.")
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parser.add_argument(
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"--phones-dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--speaker-dict", type=str, default=None, help="speaker id map file.")
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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, converters={
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"speech": 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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speech_scaler = StandardScaler()
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speech_scaler.mean_ = np.load(args.speech_stats)[0]
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speech_scaler.scale_ = np.load(args.speech_stats)[1]
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speech_scaler.n_features_in_ = speech_scaler.mean_.shape[0]
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vocab_phones = {}
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with open(args.phones_dict, 'rt') as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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for phn, id in phn_id:
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vocab_phones[phn] = int(id)
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vocab_speaker = {}
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with open(args.speaker_dict, 'rt') as f:
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spk_id = [line.strip().split() for line in f.readlines()]
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for spk, id in spk_id:
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vocab_speaker[spk] = int(id)
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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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speech = item['speech']
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# normalize
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speech = speech_scaler.transform(speech)
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speech_dir = dumpdir / "data_speech"
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speech_dir.mkdir(parents=True, exist_ok=True)
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speech_path = speech_dir / f"{utt_id}_speech.npy"
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np.save(speech_path, speech.astype(np.float32), allow_pickle=False)
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phone_ids = [vocab_phones[p] for p in item['phones']]
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spk_id = vocab_speaker[item["speaker"]]
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record = {
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"utt_id": item['utt_id'],
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"spk_id": spk_id,
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"text": phone_ids,
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"text_lengths": item['text_lengths'],
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"speech_lengths": item['speech_lengths'],
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"durations": item['durations'],
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"speech": str(speech_path),
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"align_start": item['align_start'],
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"align_end": item['align_end'],
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}
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# add spk_emb for voice cloning
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if "spk_emb" in item:
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record["spk_emb"] = str(item["spk_emb"])
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output_metadata.append(record)
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output_metadata.sort(key=itemgetter('speech_lengths'))
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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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