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PaddleSpeech/paddlespeech/vector/exps/ge2e/preprocess.py

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# 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 paddlespeech.vector.exps.ge2e.audio_processor import SpeakerVerificationPreprocessor
from paddlespeech.vector.exps.ge2e.config import get_cfg_defaults
from paddlespeech.vector.exps.ge2e.dataset_processors import process_aidatatang_200zh
from paddlespeech.vector.exps.ge2e.dataset_processors import process_librispeech
from paddlespeech.vector.exps.ge2e.dataset_processors import process_magicdata
from paddlespeech.vector.exps.ge2e.dataset_processors import process_voxceleb1
from paddlespeech.vector.exps.ge2e.dataset_processors import process_voxceleb2
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="preprocess dataset for speaker verification task")
parser.add_argument(
"--datasets_root",
type=Path,
help="Path to the directory containing your LibriSpeech, LibriTTS and VoxCeleb datasets."
)
parser.add_argument(
"--output_dir", type=Path, help="Path to save processed dataset.")
parser.add_argument(
"--dataset_names",
type=str,
default="librispeech_other,voxceleb1,voxceleb2",
help="comma-separated list of names of the datasets you want to preprocess. only "
"the train set of these datastes will be used. Possible names: librispeech_other, "
"voxceleb1, voxceleb2, aidatatang_200zh, magicdata.")
parser.add_argument(
"--skip_existing",
action="store_true",
help="Whether to skip ouput files with the same name. Useful if this script was interrupted."
)
parser.add_argument(
"--no_trim",
action="store_true",
help="Preprocess audio without trimming silences (not recommended).")
args = parser.parse_args()
if not args.no_trim:
try:
import webrtcvad
print(webrtcvad.__version__)
except Exception as e:
raise ModuleNotFoundError(
"Package 'webrtcvad' not found. This package enables "
"noise removal and is recommended. Please install and "
"try again. If installation fails, "
"use --no_trim to disable this error message.")
del args.no_trim
args.datasets = [item.strip() for item in args.dataset_names.split(",")]
if not hasattr(args, "output_dir"):
args.output_dir = args.dataset_root / "SV2TTS" / "encoder"
args.output_dir = args.output_dir.expanduser()
args.datasets_root = args.datasets_root.expanduser()
assert args.datasets_root.exists()
args.output_dir.mkdir(exist_ok=True, parents=True)
config = get_cfg_defaults()
print(args)
c = config.data
processor = SpeakerVerificationPreprocessor(
sampling_rate=c.sampling_rate,
audio_norm_target_dBFS=c.audio_norm_target_dBFS,
vad_window_length=c.vad_window_length,
vad_moving_average_width=c.vad_moving_average_width,
vad_max_silence_length=c.vad_max_silence_length,
mel_window_length=c.mel_window_length,
mel_window_step=c.mel_window_step,
n_mels=c.n_mels,
partial_n_frames=c.partial_n_frames,
min_pad_coverage=c.min_pad_coverage,
partial_overlap_ratio=c.min_pad_coverage, )
preprocess_func = {
"librispeech_other": process_librispeech,
"voxceleb1": process_voxceleb1,
"voxceleb2": process_voxceleb2,
"aidatatang_200zh": process_aidatatang_200zh,
"magicdata": process_magicdata,
}
for dataset in args.datasets:
print("Preprocessing %s" % dataset)
preprocess_func[dataset](processor, args.datasets_root, args.output_dir,
args.skip_existing)