93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
import pickle
|
|
from collections import defaultdict
|
|
from pathlib import Path
|
|
import random
|
|
|
|
import librosa as librosa
|
|
from torch.utils.data import Dataset
|
|
import torch
|
|
|
|
import variables as V
|
|
from ml_lib.modules.utils import F_x
|
|
|
|
|
|
class BinaryMasksDataset(Dataset):
|
|
_to_label = defaultdict(lambda: -1)
|
|
_to_label.update(dict(clear=V.CLEAR, mask=V.MASK))
|
|
|
|
@property
|
|
def sample_shape(self):
|
|
return self[0][0].shape
|
|
|
|
def __init__(self, data_root, setting, mel_transforms, transforms=None, mixup=False, stretch_dataset=True):
|
|
self.stretch = stretch_dataset
|
|
assert isinstance(setting, str), f'Setting has to be a string, but was: {type(setting)}.'
|
|
assert setting in V.DATA_OPTIONS, f'Setting must match one of: {V.DATA_OPTIONS}.'
|
|
super(BinaryMasksDataset, self).__init__()
|
|
|
|
self.data_root = Path(data_root)
|
|
self.setting = setting
|
|
self.mixup = mixup
|
|
self.container_ext = '.pik'
|
|
self._mel_transform = mel_transforms
|
|
self._labels = self._build_labels()
|
|
self._wav_folder = self.data_root / 'wav'
|
|
self._wav_files = list(sorted(self._labels.keys()))
|
|
self._mel_folder = self.data_root / 'mel'
|
|
self._transforms = transforms or F_x(in_shape=None)
|
|
|
|
def _build_labels(self):
|
|
with open(Path(self.data_root) / 'lab' / 'labels.csv', mode='r') as f:
|
|
# Exclude the header
|
|
_ = next(f)
|
|
labeldict = dict()
|
|
for row in f:
|
|
if self.setting not in row:
|
|
continue
|
|
filename, label = row.strip().split(',')
|
|
labeldict[filename] = self._to_label[label.lower()] if not self.setting == 'test' else filename
|
|
if self.stretch:
|
|
labeldict.update({f'X_{key}': val for key, val in labeldict.items()})
|
|
return labeldict
|
|
|
|
def __len__(self):
|
|
return len(self._labels) * 2 if self.mixup else len(self._labels)
|
|
|
|
def _compute_or_retrieve(self, filename):
|
|
if not (self._mel_folder / (filename + self.container_ext)).exists():
|
|
raw_sample, sr = librosa.core.load(self._wav_folder / (filename.replace('X_', '') + '.wav'))
|
|
mel_sample = self._mel_transform(raw_sample)
|
|
self._mel_folder.mkdir(exist_ok=True, parents=True)
|
|
with (self._mel_folder / (filename + self.container_ext)).open(mode='wb') as f:
|
|
pickle.dump(mel_sample, f, protocol=pickle.HIGHEST_PROTOCOL)
|
|
with (self._mel_folder / (filename + self.container_ext)).open(mode='rb') as f:
|
|
mel_sample = pickle.load(f, fix_imports=True)
|
|
return mel_sample
|
|
|
|
def __getitem__(self, item):
|
|
is_mixed = item >= len(self._labels)
|
|
if is_mixed:
|
|
item = item - len(self._labels)
|
|
|
|
key: str = list(self._labels.keys())[item]
|
|
filename = key.replace('.wav', '')
|
|
mel_sample = self._compute_or_retrieve(filename)
|
|
label = self._labels[key]
|
|
|
|
if is_mixed:
|
|
label_sec = -1
|
|
while label_sec != self._labels[key]:
|
|
key_sec = random.choice(list(self._labels.keys()))
|
|
label_sec = self._labels[key_sec]
|
|
# noinspection PyUnboundLocalVariable
|
|
filename_sec = key_sec[:-4]
|
|
mel_sample_sec = self._compute_or_retrieve(filename_sec)
|
|
mix_in_border = int(random.random() * mel_sample.shape[-1]) * random.choice([1, -1])
|
|
mel_sample[:, :mix_in_border] = mel_sample_sec[:, :mix_in_border]
|
|
|
|
transformed_samples = self._transforms(mel_sample)
|
|
if not self.setting == 'test':
|
|
label = torch.as_tensor(label, dtype=torch.float)
|
|
|
|
return transformed_samples, label
|