lym0302
3 years ago
parent
3df69e7502
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# Copyright (c) 2023 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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import argparse
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import logging
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import jsonlines
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import numpy as np
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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 find_min_max_spec(spec, min_spec, max_spec):
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# spec: [T, 80]
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for i in range(spec.shape[1]):
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min_value = np.min(spec[:, i])
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max_value = np.max(spec[:, i])
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min_spec[i] = min(min_value, min_spec[i])
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max_spec[i] = max(max_value, max_spec[i])
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return min_spec, max_spec
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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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"--speech-stretchs",
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type=str,
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required=True,
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help="min max spec file. only computer on train data")
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args = parser.parse_args()
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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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n_mel = 80
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min_spec = 100 * np.ones(shape=(n_mel), dtype=np.float32)
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max_spec = -100 * np.ones(shape=(n_mel), dtype=np.float32)
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for item in tqdm(dataset):
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spec = item['speech']
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min_spec, max_spec = find_min_max_spec(spec, min_spec, max_spec)
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print(min_spec)
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print(max_spec)
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min_max_spec = np.stack([min_spec, max_spec], axis=0)
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np.save(
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str(args.speech_stretchs),
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min_max_spec.astype(np.float32),
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allow_pickle=False)
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if __name__ == "__main__":
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main()
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