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86 lines
2.8 KiB
86 lines
2.8 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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import argparse
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import os
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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 tqdm import tqdm
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def main():
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# parse config and args
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parser = argparse.ArgumentParser(
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description="Preprocess audio and then extract features .")
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parser.add_argument(
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"--old-dump-dir",
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default=None,
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type=str,
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help="directory to dump feature files.")
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parser.add_argument(
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"--dump-dir",
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type=str,
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required=True,
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help="directory to finetune dump feature files.")
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args = parser.parse_args()
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old_dump_dir = Path(args.old_dump_dir).expanduser()
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old_dump_dir = old_dump_dir.resolve()
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dump_dir = Path(args.dump_dir).expanduser()
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# use absolute path
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dump_dir = dump_dir.resolve()
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dump_dir.mkdir(parents=True, exist_ok=True)
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assert old_dump_dir.is_dir()
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assert dump_dir.is_dir()
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for sub in ["train", "dev", "test"]:
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# 把 old_dump_dir 里面的 *-wave.npy 软连接到 dump_dir 的对应位置
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output_dir = dump_dir / sub
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output_dir.mkdir(parents=True, exist_ok=True)
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results = []
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files = os.listdir(output_dir / "raw")
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for name in tqdm(files):
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utt_id = name.split("_feats.npy")[0]
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mel_path = output_dir / ("raw/" + name)
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gen_mel = np.load(mel_path)
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wave_name = utt_id + "_wave.npy"
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wav = np.load(old_dump_dir / sub / ("raw/" + wave_name))
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os.symlink(old_dump_dir / sub / ("raw/" + wave_name),
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output_dir / ("raw/" + wave_name))
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num_sample = wav.shape[0]
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num_frames = gen_mel.shape[0]
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wav_path = output_dir / ("raw/" + wave_name)
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record = {
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"utt_id": utt_id,
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"num_samples": num_sample,
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"num_frames": num_frames,
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"feats": str(mel_path),
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"wave": str(wav_path),
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}
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results.append(record)
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results.sort(key=itemgetter("utt_id"))
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with jsonlines.open(output_dir / "raw/metadata.jsonl", 'w') as writer:
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for item in results:
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writer.write(item)
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if __name__ == "__main__":
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main()
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