233 lines
7.3 KiB
Python
233 lines
7.3 KiB
Python
# 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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from operator import itemgetter
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from typing import Any
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from typing import Dict
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from typing import List
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import argparse
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import jsonlines
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import librosa
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import logging
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import numpy as np
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import tqdm
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from concurrent.futures import ThreadPoolExecutor
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from parakeet.data.get_feats import LogMelFBank
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from pathlib import Path
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from praatio import tgio
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from config import get_cfg_default
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def process_sentence(config: Dict[str, Any],
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fp: Path,
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alignment_fp: Path,
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output_dir: Path,
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mel_extractor=None):
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utt_id = fp.stem
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# reading
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y, sr = librosa.load(str(fp), sr=config.sr) # resampling may occur
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assert len(y.shape) == 1, f"{utt_id} is not a mono-channel audio."
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assert np.abs(
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y).max() <= 1.0, f"{utt_id} is seems to be different that 16 bit PCM."
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duration = librosa.get_duration(y, sr=sr)
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# trim according to the alignment file
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alignment = tgio.openTextgrid(alignment_fp)
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intervals = alignment.tierDict[alignment.tierNameList[0]].entryList
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first, last = intervals[0], intervals[-1]
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start = 0
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end = last.end
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if first.label == "sil" and first.end < duration:
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start = first.end
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else:
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logging.warning(
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f" There is something wrong with the fisrt interval {first} in utterance: {utt_id}"
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)
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if last.label == "sil" and last.start < duration:
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end = last.start
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else:
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end = duration
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logging.warning(
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f" There is something wrong with the last interval {last} in utterance: {utt_id}"
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)
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# silence trimmed
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start, end = librosa.time_to_samples([first.end, last.start], sr=sr)
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y = y[start:end]
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# energy based silence trimming
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if config.trim_silence:
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y, _ = librosa.effects.trim(
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y,
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top_db=config.top_db,
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frame_length=config.trim_frame_length,
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hop_length=config.trim_hop_length)
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# extract mel feats
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logmel = mel_extractor.get_log_mel_fbank(y)
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# adjust time to make num_samples == num_frames * hop_length
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num_frames = logmel.shape[0]
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if y.size < num_frames * config.hop_length:
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y = np.pad(
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y, (0, num_frames * config.hop_length - y.size), mode="reflect")
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else:
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y = y[:num_frames * config.hop_length]
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num_sample = y.shape[0]
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mel_path = output_dir / (utt_id + "_feats.npy")
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wav_path = output_dir / (utt_id + "_wave.npy")
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np.save(wav_path, y) # (num_samples, )
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np.save(mel_path, logmel) # (num_frames, n_mels)
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record = {
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"utt_id": utt_id,
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"num_samples": num_sample,
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"num_frames": num_frames,
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"feats": str(mel_path.resolve()),
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"wave": str(wav_path.resolve()),
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}
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return record
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def process_sentences(config,
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fps: List[Path],
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alignment_fps: List[Path],
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output_dir: Path,
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mel_extractor=None,
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nprocs: int=1):
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if nprocs == 1:
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results = []
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for fp, alignment_fp in tqdm.tqdm(zip(fps, alignment_fps)):
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results.append(
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process_sentence(config, fp, alignment_fp, output_dir,
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mel_extractor))
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else:
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with ThreadPoolExecutor(nprocs) as pool:
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futures = []
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with tqdm.tqdm(total=len(fps)) as progress:
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for fp, alignment_fp in zip(fps, alignment_fps):
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future = pool.submit(process_sentence, config, fp,
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alignment_fp, output_dir,
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mel_extractor)
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future.add_done_callback(lambda p: progress.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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results.sort(key=itemgetter("utt_id"))
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with jsonlines.open(output_dir / "metadata.jsonl", 'w') as writer:
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for item in results:
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writer.write(item)
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print("Done")
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def main():
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# parse config and args
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parser = argparse.ArgumentParser(
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description="Preprocess audio and then extract features .")
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parser.add_argument(
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"--rootdir", default=None, type=str, help="directory to baker dataset.")
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parser.add_argument(
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"--dumpdir",
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type=str,
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required=True,
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help="directory to dump feature files.")
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parser.add_argument(
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"--config", type=str, help="yaml format configuration file.")
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parser.add_argument(
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"--verbose",
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type=int,
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default=1,
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help="logging level. higher is more logging. (default=1)")
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parser.add_argument(
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"--num_cpu", type=int, default=1, help="number of process.")
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args = parser.parse_args()
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C = get_cfg_default()
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if args.config:
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C.merge_from_file(args.config)
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C.freeze()
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if args.verbose > 1:
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print(vars(args))
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print(C)
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root_dir = Path(args.rootdir).expanduser()
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dumpdir = Path(args.dumpdir).expanduser()
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dumpdir.mkdir(parents=True, exist_ok=True)
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wav_files = sorted(list((root_dir / "Wave").rglob("*.wav")))
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alignment_files = sorted(
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list((root_dir / "PhoneLabeling").rglob("*.interval")))
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# split data into 3 sections
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num_train = 9800
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num_dev = 100
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train_wav_files = wav_files[:num_train]
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dev_wav_files = wav_files[num_train:num_train + num_dev]
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test_wav_files = wav_files[num_train + num_dev:]
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train_alignment_files = alignment_files[:num_train]
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dev_alignment_files = alignment_files[num_train:num_train + num_dev]
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test_alignment_files = alignment_files[num_train + num_dev:]
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train_dump_dir = dumpdir / "train" / "raw"
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train_dump_dir.mkdir(parents=True, exist_ok=True)
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dev_dump_dir = dumpdir / "dev" / "raw"
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dev_dump_dir.mkdir(parents=True, exist_ok=True)
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test_dump_dir = dumpdir / "test" / "raw"
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test_dump_dir.mkdir(parents=True, exist_ok=True)
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mel_extractor = LogMelFBank(
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sr=C.sr,
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n_fft=C.n_fft,
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hop_length=C.hop_length,
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win_length=C.win_length,
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window=C.window,
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n_mels=C.n_mels,
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fmin=C.fmin,
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fmax=C.fmax)
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# process for the 3 sections
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process_sentences(
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C,
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train_wav_files,
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train_alignment_files,
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train_dump_dir,
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mel_extractor=mel_extractor,
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nprocs=args.num_cpu)
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process_sentences(
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C,
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dev_wav_files,
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dev_alignment_files,
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dev_dump_dir,
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mel_extractor=mel_extractor,
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nprocs=args.num_cpu)
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process_sentences(
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C,
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test_wav_files,
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test_alignment_files,
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test_dump_dir,
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mel_extractor=mel_extractor,
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nprocs=args.num_cpu)
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if __name__ == "__main__":
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main()
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