97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
|
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
|
||
|
|
import argparse
|
||
|
|
from pathlib import Path
|
||
|
|
from multiprocessing import Pool
|
||
|
|
from functools import partial
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import librosa
|
||
|
|
import soundfile as sf
|
||
|
|
from tqdm import tqdm
|
||
|
|
from praatio import tgio
|
||
|
|
|
||
|
|
|
||
|
|
def get_valid_part(fpath):
|
||
|
|
f = tgio.openTextgrid(fpath)
|
||
|
|
|
||
|
|
start = 0
|
||
|
|
phone_entry_list = f.tierDict['phones'].entryList
|
||
|
|
first_entry = phone_entry_list[0]
|
||
|
|
if first_entry.label == "sil":
|
||
|
|
start = first_entry.end
|
||
|
|
|
||
|
|
last_entry = phone_entry_list[-1]
|
||
|
|
if last_entry.label == "sp":
|
||
|
|
end = last_entry.start
|
||
|
|
else:
|
||
|
|
end = last_entry.end
|
||
|
|
return start, end
|
||
|
|
|
||
|
|
|
||
|
|
def process_utterance(fpath, source_dir, target_dir, alignment_dir):
|
||
|
|
rel_path = fpath.relative_to(source_dir)
|
||
|
|
opath = target_dir / rel_path
|
||
|
|
apath = (alignment_dir / rel_path).with_suffix(".TextGrid")
|
||
|
|
opath.parent.mkdir(parents=True, exist_ok=True)
|
||
|
|
|
||
|
|
start, end = get_valid_part(apath)
|
||
|
|
wav, _ = librosa.load(fpath, sr=22050, offset=start, duration=end - start)
|
||
|
|
normalized_wav = wav / np.max(wav) * 0.999
|
||
|
|
sf.write(opath, normalized_wav, samplerate=22050, subtype='PCM_16')
|
||
|
|
# print(f"{fpath} => {opath}")
|
||
|
|
|
||
|
|
|
||
|
|
def preprocess_aishell3(source_dir, target_dir, alignment_dir):
|
||
|
|
source_dir = Path(source_dir).expanduser()
|
||
|
|
target_dir = Path(target_dir).expanduser()
|
||
|
|
alignment_dir = Path(alignment_dir).expanduser()
|
||
|
|
|
||
|
|
wav_paths = list(source_dir.rglob("*.wav"))
|
||
|
|
print(f"there are {len(wav_paths)} audio files in total")
|
||
|
|
fx = partial(
|
||
|
|
process_utterance,
|
||
|
|
source_dir=source_dir,
|
||
|
|
target_dir=target_dir,
|
||
|
|
alignment_dir=alignment_dir)
|
||
|
|
with Pool(16) as p:
|
||
|
|
list(
|
||
|
|
tqdm(
|
||
|
|
p.imap(fx, wav_paths), total=len(wav_paths), unit="utterance"))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
parser = argparse.ArgumentParser(
|
||
|
|
description="Process audio in AiShell3, trim silence according to the alignment "
|
||
|
|
"files generated by MFA, and normalize volume by peak.")
|
||
|
|
parser.add_argument(
|
||
|
|
"--input",
|
||
|
|
type=str,
|
||
|
|
default="~/datasets/aishell3/train/wav",
|
||
|
|
help="path of the original audio folder in aishell3.")
|
||
|
|
parser.add_argument(
|
||
|
|
"--output",
|
||
|
|
type=str,
|
||
|
|
default="~/datasets/aishell3/train/normalized_wav",
|
||
|
|
help="path of the folder to save the processed audio files.")
|
||
|
|
parser.add_argument(
|
||
|
|
"--alignment",
|
||
|
|
type=str,
|
||
|
|
default="~/datasets/aishell3/train/alignment",
|
||
|
|
help="path of the alignment files.")
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
preprocess_aishell3(args.input, args.output, args.alignment)
|