235 lines
8.8 KiB
Python
235 lines
8.8 KiB
Python
# Copyright (c) 2019, NVIDIA CORPORATION. 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 json
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from pathlib import Path
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import numpy as np
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import torch
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from torch.utils.data import Dataset, DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from .audio import (audio_from_file, AudioSegment, GainPerturbation,
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ShiftPerturbation, SpeedPerturbation)
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from .text import _clean_text, punctuation_map
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def normalize_string(s, labels, punct_map):
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"""Normalizes string.
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Example:
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'call me at 8:00 pm!' -> 'call me at eight zero pm'
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"""
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labels = set(labels)
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try:
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text = _clean_text(s, ["english_cleaners"], punct_map).strip()
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return ''.join([tok for tok in text if all(t in labels for t in tok)])
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except:
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print(f"WARNING: Normalizing failed: {s}")
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return None
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class FilelistDataset(Dataset):
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def __init__(self, filelist_fpath):
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self.samples = [line.strip() for line in open(filelist_fpath, 'r')]
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, index):
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audio, audio_len = audio_from_file(self.samples[index])
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return (audio.squeeze(0), audio_len, torch.LongTensor([0]),
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torch.LongTensor([0]))
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class SingleAudioDataset(FilelistDataset):
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def __init__(self, audio_fpath):
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self.samples = [audio_fpath]
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class AudioDataset(Dataset):
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def __init__(self, data_dir, manifest_fpaths, labels,
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sample_rate=16000, min_duration=0.1, max_duration=float("inf"),
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pad_to_max_duration=False, max_utts=0, normalize_transcripts=True,
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sort_by_duration=False, trim_silence=False,
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speed_perturbation=None, gain_perturbation=None,
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shift_perturbation=None, ignore_offline_speed_perturbation=False):
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"""Loads audio, transcript and durations listed in a .json file.
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Args:
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data_dir: absolute path to dataset folder
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manifest_filepath: relative path from dataset folder
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to manifest json as described above. Can be coma-separated paths.
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labels (str): all possible output symbols
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min_duration (int): skip audio shorter than threshold
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max_duration (int): skip audio longer than threshold
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pad_to_max_duration (bool): pad all sequences to max_duration
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max_utts (int): limit number of utterances
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normalize_transcripts (bool): normalize transcript text
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sort_by_duration (bool): sort sequences by increasing duration
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trim_silence (bool): trim leading and trailing silence from audio
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ignore_offline_speed_perturbation (bool): use precomputed speed perturbation
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Returns:
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tuple of Tensors
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"""
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self.data_dir = data_dir
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self.labels = labels
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self.labels_map = dict([(labels[i], i) for i in range(len(labels))])
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self.punctuation_map = punctuation_map(labels)
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self.blank_index = len(labels)
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self.pad_to_max_duration = pad_to_max_duration
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self.sort_by_duration = sort_by_duration
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self.max_utts = max_utts
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self.normalize_transcripts = normalize_transcripts
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self.ignore_offline_speed_perturbation = ignore_offline_speed_perturbation
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self.min_duration = min_duration
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self.max_duration = max_duration
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self.trim_silence = trim_silence
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self.sample_rate = sample_rate
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perturbations = []
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if speed_perturbation is not None:
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perturbations.append(SpeedPerturbation(**speed_perturbation))
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if gain_perturbation is not None:
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perturbations.append(GainPerturbation(**gain_perturbation))
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if shift_perturbation is not None:
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perturbations.append(ShiftPerturbation(**shift_perturbation))
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self.perturbations = perturbations
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self.max_duration = max_duration
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self.samples = []
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self.duration = 0.0
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self.duration_filtered = 0.0
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for fpath in manifest_fpaths:
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self._load_json_manifest(fpath)
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if sort_by_duration:
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self.samples = sorted(self.samples, key=lambda s: s['duration'])
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def __getitem__(self, index):
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s = self.samples[index]
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rn_indx = np.random.randint(len(s['audio_filepath']))
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duration = s['audio_duration'][rn_indx] if 'audio_duration' in s else 0
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offset = s.get('offset', 0)
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segment = AudioSegment(
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s['audio_filepath'][rn_indx], target_sr=self.sample_rate,
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offset=offset, duration=duration, trim=self.trim_silence)
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for p in self.perturbations:
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p.maybe_apply(segment, self.sample_rate)
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segment = torch.FloatTensor(segment.samples)
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return (segment,
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torch.tensor(segment.shape[0]).int(),
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torch.tensor(s["transcript"]),
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torch.tensor(len(s["transcript"])).int())
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def __len__(self):
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return len(self.samples)
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def _load_json_manifest(self, fpath):
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for s in json.load(open(fpath, "r", encoding="utf-8")):
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if self.pad_to_max_duration and not self.ignore_offline_speed_perturbation:
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# require all perturbed samples to be < self.max_duration
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s_max_duration = max(f['duration'] for f in s['files'])
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else:
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# otherwise we allow perturbances to be > self.max_duration
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s_max_duration = s['original_duration']
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s['duration'] = s.pop('original_duration')
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if not (self.min_duration <= s_max_duration <= self.max_duration):
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self.duration_filtered += s['duration']
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continue
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# Prune and normalize according to transcript
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tr = (s.get('transcript', None) or
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self.load_transcript(s['text_filepath']))
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if not isinstance(tr, str):
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print(f'WARNING: Skipped sample (transcript not a str): {tr}.')
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self.duration_filtered += s['duration']
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continue
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if self.normalize_transcripts:
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tr = normalize_string(tr, self.labels, self.punctuation_map)
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s["transcript"] = self.to_vocab_inds(tr)
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files = s.pop('files')
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if self.ignore_offline_speed_perturbation:
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files = [f for f in files if f['speed'] == 1.0]
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s['audio_duration'] = [f['duration'] for f in files]
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s['audio_filepath'] = [str(Path(self.data_dir, f['fname']))
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for f in files]
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self.samples.append(s)
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self.duration += s['duration']
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if self.max_utts > 0 and len(self.samples) >= self.max_utts:
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print(f'Reached max_utts={self.max_utts}. Finished parsing {fpath}.')
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break
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def load_transcript(self, transcript_path):
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with open(transcript_path, 'r', encoding="utf-8") as transcript_file:
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transcript = transcript_file.read().replace('\n', '')
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return transcript
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def to_vocab_inds(self, transcript):
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chars = [self.labels_map.get(x, self.blank_index) for x in list(transcript)]
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transcript = list(filter(lambda x: x != self.blank_index, chars))
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return transcript
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def collate_fn(batch):
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bs = len(batch)
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max_len = lambda l, idx: max(el[idx].size(0) for el in l)
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audio = torch.zeros(bs, max_len(batch, 0))
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audio_lens = torch.zeros(bs, dtype=torch.int32)
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transcript = torch.zeros(bs, max_len(batch, 2))
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transcript_lens = torch.zeros(bs, dtype=torch.int32)
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for i, sample in enumerate(batch):
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audio[i].narrow(0, 0, sample[0].size(0)).copy_(sample[0])
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audio_lens[i] = sample[1]
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transcript[i].narrow(0, 0, sample[2].size(0)).copy_(sample[2])
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transcript_lens[i] = sample[3]
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return audio, audio_lens, transcript, transcript_lens
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def get_data_loader(dataset, batch_size, multi_gpu=True, shuffle=True,
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drop_last=True, num_workers=4):
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kw = {'dataset': dataset, 'collate_fn': collate_fn,
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'num_workers': num_workers, 'pin_memory': True}
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if multi_gpu:
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loader_shuffle = False
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sampler = DistributedSampler(dataset, shuffle=shuffle)
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else:
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loader_shuffle = shuffle
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sampler = None
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return DataLoader(batch_size=batch_size, drop_last=drop_last,
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sampler=sampler, shuffle=loader_shuffle, **kw)
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