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113 lines
4.0 KiB
113 lines
4.0 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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"""Script to reorganize Baker dataset so as to use Montreal Force
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Aligner to align transcription and audio.
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Please refer to https://montreal-forced-aligner.readthedocs.io/en/latest/data_prep.html
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for more details about Montreal Force Aligner's requirements on cotpus.
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For scripts to reorganize other corpus, please refer to
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https://github.com/MontrealCorpusTools/MFA-reorganization-scripts
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for more details.
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"""
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import argparse
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import os
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import shutil
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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from typing import Union
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import librosa
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import soundfile as sf
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from tqdm import tqdm
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def get_transcripts(path: Union[str, Path]):
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transcripts = {}
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with open(path) as f:
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lines = f.readlines()
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for i in range(0, len(lines), 2):
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sentence_id = lines[i].split()[0]
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transcription = lines[i + 1].strip()
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# tones are dropped here
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# since the lexicon does not consider tones, too
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transcription = " ".join([item[:-1] for item in transcription.split()])
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transcripts[sentence_id] = transcription
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return transcripts
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def resample_and_save(source, target, sr=16000):
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wav, _ = librosa.load(str(source), sr=sr)
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sf.write(str(target), wav, samplerate=sr, subtype='PCM_16')
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return target
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def reorganize_baker(root_dir: Union[str, Path],
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output_dir: Union[str, Path]=None,
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resample_audio=False):
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root_dir = Path(root_dir).expanduser()
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transcript_path = root_dir / "ProsodyLabeling" / "000001-010000.txt"
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transcriptions = get_transcripts(transcript_path)
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wave_dir = root_dir / "Wave"
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wav_paths = sorted(list(wave_dir.glob("*.wav")))
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output_dir = Path(output_dir).expanduser()
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assert wave_dir != output_dir, "Don't use an the original wav's directory as output_dir"
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output_dir.mkdir(parents=True, exist_ok=True)
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if resample_audio:
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with ThreadPoolExecutor(os.cpu_count()) as pool:
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with tqdm(total=len(wav_paths), desc="resampling") as pbar:
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futures = []
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for wav_path in wav_paths:
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future = pool.submit(resample_and_save, wav_path,
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output_dir / wav_path.name)
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future.add_done_callback(lambda p: pbar.update())
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futures.append(future)
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results = []
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for ft in futures:
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results.append(ft.result())
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else:
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for wav_path in tqdm(wav_paths, desc="copying"):
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shutil.copyfile(wav_path, output_dir / wav_path.name)
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for sentence_id, transcript in tqdm(
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transcriptions.items(), desc="transcription process"):
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with open(output_dir / (sentence_id + ".lab"), 'wt') as f:
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f.write(transcript)
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f.write('\n')
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print("Done!")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Reorganize Baker dataset for MFA")
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parser.add_argument("--root-dir", type=str, help="path to baker dataset.")
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parser.add_argument(
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"--output-dir",
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type=str,
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help="path to save outputs (audio and transcriptions)")
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parser.add_argument(
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"--resample-audio",
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action="store_true",
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help="To resample audio files or just copy them")
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args = parser.parse_args()
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reorganize_baker(args.root_dir, args.output_dir, args.resample_audio)
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