109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
# Copyright (c) 2020 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 os
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import pickle
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import argparse
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from pathlib import Path
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import tqdm
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import numpy as np
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from parakeet.audio import AudioProcessor
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from parakeet.audio import LogMagnitude
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from parakeet.datasets import LJSpeechMetaData
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from parakeet.frontend import English
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from config import get_cfg_defaults
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def create_dataset(config, source_path, target_path, verbose=False):
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# create output dir
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target_path = Path(target_path).expanduser()
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mel_path = target_path / "mel"
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os.makedirs(mel_path, exist_ok=True)
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meta_data = LJSpeechMetaData(source_path)
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frontend = English()
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processor = AudioProcessor(
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sample_rate=config.data.sample_rate,
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n_fft=config.data.n_fft,
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n_mels=config.data.n_mels,
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win_length=config.data.win_length,
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hop_length=config.data.hop_length,
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fmax=config.data.fmax,
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fmin=config.data.fmin)
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normalizer = LogMagnitude()
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records = []
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for (fname, text, _) in tqdm.tqdm(meta_data):
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wav = processor.read_wav(fname)
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mel = processor.mel_spectrogram(wav)
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mel = normalizer.transform(mel)
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phonemes = frontend.phoneticize(text)
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ids = frontend.numericalize(phonemes)
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mel_name = os.path.splitext(os.path.basename(fname))[0]
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# save mel spectrogram
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records.append((mel_name, text, phonemes, ids))
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np.save(mel_path / mel_name, mel)
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if verbose:
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print("save mel spectrograms into {}".format(mel_path))
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# save meta data as pickle archive
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with open(target_path / "metadata.pkl", 'wb') as f:
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pickle.dump(records, f)
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if verbose:
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print("saved metadata into {}".format(target_path / "metadata.pkl"))
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# also save meta data into text format for inspection
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with open(target_path / "metadata.txt", 'wt') as f:
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for mel_name, text, phonemes, _ in records:
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phoneme_str = "|".join(phonemes)
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f.write("{}\t{}\t{}\n".format(mel_name, text, phoneme_str))
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if verbose:
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print("saved metadata into {}".format(target_path / "metadata.txt"))
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print("Done.")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="create dataset")
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parser.add_argument(
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"--config",
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type=str,
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metavar="FILE",
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help="extra config to overwrite the default config")
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parser.add_argument(
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"--input", type=str, help="path of the ljspeech dataset")
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parser.add_argument(
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"--output", type=str, help="path to save output dataset")
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parser.add_argument(
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"--opts",
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nargs=argparse.REMAINDER,
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help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
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)
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parser.add_argument(
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"-v", "--verbose", action="store_true", help="print msg")
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config = get_cfg_defaults()
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args = parser.parse_args()
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config.data)
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create_dataset(config, args.input, args.output, args.verbose)
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