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