File size: 4,461 Bytes
3447959 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
# Copyright (2024) Tsinghua University, Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import time
import torch
from torch.utils.data import DataLoader, DistributedSampler
import soundfile as sf
import numpy as np
from dist_utils import is_main_process, get_world_size, get_rank
def now():
from datetime import datetime
return datetime.now().strftime("%Y%m%d%H%M")
def setup_logger():
logging.basicConfig(
level=logging.INFO if is_main_process() else logging.WARN,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[logging.StreamHandler()],
)
def get_dataloader(dataset, config, is_train=True, use_distributed=True):
if use_distributed:
sampler = DistributedSampler(
dataset,
shuffle=is_train,
num_replicas=get_world_size(),
rank=get_rank()
)
else:
sampler = None
loader = DataLoader(
dataset,
batch_size=config.batch_size_train if is_train else config.batch_size_eval,
num_workers=config.num_workers,
pin_memory=True,
sampler=sampler,
shuffle=sampler is None and is_train,
collate_fn=dataset.collater,
drop_last=is_train,
)
if is_train:
loader = IterLoader(loader, use_distributed=use_distributed)
return loader
def apply_to_sample(f, sample):
if len(sample) == 0:
return {}
def _apply(x):
if torch.is_tensor(x):
return f(x)
elif isinstance(x, dict):
return {key: _apply(value) for key, value in x.items()}
elif isinstance(x, list):
return [_apply(x) for x in x]
else:
return x
return _apply(sample)
def move_to_cuda(sample):
def _move_to_cuda(tensor):
return tensor.cuda()
return apply_to_sample(_move_to_cuda, sample)
def prepare_sample(samples, cuda_enabled=True):
if cuda_enabled:
samples = move_to_cuda(samples)
# TODO fp16 support
return samples
class IterLoader:
"""
A wrapper to convert DataLoader as an infinite iterator.
Modified from:
https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/iter_based_runner.py
"""
def __init__(self, dataloader: DataLoader, use_distributed: bool = False):
self._dataloader = dataloader
self.iter_loader = iter(self._dataloader)
self._use_distributed = use_distributed
self._epoch = 0
@property
def epoch(self) -> int:
return self._epoch
def __next__(self):
try:
data = next(self.iter_loader)
except StopIteration:
self._epoch += 1
if hasattr(self._dataloader.sampler, "set_epoch") and self._use_distributed:
self._dataloader.sampler.set_epoch(self._epoch)
time.sleep(2) # Prevent possible deadlock during epoch transition
self.iter_loader = iter(self._dataloader)
data = next(self.iter_loader)
return data
def __iter__(self):
return self
def __len__(self):
return len(self._dataloader)
def prepare_one_sample(wav_path, wav_processor, cuda_enabled=True):
audio, sr = sf.read(wav_path)
if len(audio.shape) == 2: # stereo to mono
audio = audio[:, 0]
if len(audio) < sr: # pad audio to at least 1s
sil = np.zeros(sr - len(audio), dtype=float)
audio = np.concatenate((audio, sil), axis=0)
audio = audio[: sr * 30] # truncate audio to at most 30s
spectrogram = wav_processor(audio, sampling_rate=sr, return_tensors="pt")["input_features"]
samples = {
"spectrogram": spectrogram,
"raw_wav": torch.from_numpy(audio).unsqueeze(0),
"padding_mask": torch.zeros(len(audio), dtype=torch.bool).unsqueeze(0),
}
if cuda_enabled:
samples = move_to_cuda(samples)
return samples |