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95 lines
3.1 KiB
95 lines
3.1 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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from functools import partial
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from multiprocessing import Pool
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from pathlib import Path
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import librosa
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import numpy as np
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import soundfile as sf
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from praatio import tgio
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from tqdm import tqdm
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def get_valid_part(fpath):
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f = tgio.openTextgrid(fpath)
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start = 0
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phone_entry_list = f.tierDict['phones'].entryList
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first_entry = phone_entry_list[0]
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if first_entry.label == "sil":
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start = first_entry.end
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last_entry = phone_entry_list[-1]
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if last_entry.label == "sp":
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end = last_entry.start
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else:
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end = last_entry.end
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return start, end
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def process_utterance(fpath, source_dir, target_dir, alignment_dir):
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rel_path = fpath.relative_to(source_dir)
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opath = target_dir / rel_path
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apath = (alignment_dir / rel_path).with_suffix(".TextGrid")
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opath.parent.mkdir(parents=True, exist_ok=True)
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start, end = get_valid_part(apath)
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wav, _ = librosa.load(fpath, sr=22050, offset=start, duration=end - start)
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normalized_wav = wav / np.max(wav) * 0.999
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sf.write(opath, normalized_wav, samplerate=22050, subtype='PCM_16')
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# print(f"{fpath} => {opath}")
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def preprocess_aishell3(source_dir, target_dir, alignment_dir):
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source_dir = Path(source_dir).expanduser()
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target_dir = Path(target_dir).expanduser()
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alignment_dir = Path(alignment_dir).expanduser()
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wav_paths = list(source_dir.rglob("*.wav"))
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print(f"there are {len(wav_paths)} audio files in total")
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fx = partial(
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process_utterance,
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source_dir=source_dir,
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target_dir=target_dir,
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alignment_dir=alignment_dir)
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with Pool(16) as p:
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list(
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tqdm(p.imap(fx, wav_paths), total=len(wav_paths), unit="utterance"))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Process audio in AiShell3, trim silence according to the alignment "
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"files generated by MFA, and normalize volume by peak.")
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parser.add_argument(
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"--input",
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type=str,
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default="~/datasets/aishell3/train/wav",
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help="path of the original audio folder in aishell3.")
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parser.add_argument(
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"--output",
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type=str,
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default="~/datasets/aishell3/train/normalized_wav",
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help="path of the folder to save the processed audio files.")
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parser.add_argument(
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"--alignment",
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type=str,
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default="~/datasets/aishell3/train/alignment",
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help="path of the alignment files.")
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args = parser.parse_args()
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preprocess_aishell3(args.input, args.output, args.alignment)
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