torchaudio testing
This commit is contained in:
Si11ium
104
datasets/base_dataset.py
Normal file
104
datasets/base_dataset.py
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@@ -0,0 +1,104 @@
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import pickle
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from pathlib import Path
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from typing import Union
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from abc import ABC
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import variables as V
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from torch.utils.data import Dataset
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class BaseAudioToMelDataset(Dataset, ABC):
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@property
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def task_type(self):
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return self._task_type
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@property
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def classes(self):
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return V.multi_classes
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@property
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def n_classes(self):
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return V.N_CLASS_binary if self.task_type == V.TASK_OPTION_binary else V.N_CLASS_multi
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@property
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def sample_shape(self):
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return self[0][0].shape
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@property
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def _fingerprint(self):
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raise NotImplementedError
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return str(self._mel_transform)
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# Data Structures
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@property
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def mel_folder(self):
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return self.data_root / 'mel'
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@property
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def wav_folder(self):
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return self.data_root / self._wav_folder_name
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@property
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def _container_ext(self):
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return '.mel'
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def __init__(self, data_root: Union[str, Path], task_type, mel_kwargs,
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mel_augmentations=None, audio_augmentations=None, reset=False,
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wav_folder_name='wav', **_):
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super(BaseAudioToMelDataset, self).__init__()
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# Dataset Parameters
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self.data_root = Path(data_root)
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self._wav_folder_name = wav_folder_name
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self.reset = reset
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self.mel_kwargs = mel_kwargs
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# Transformations
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self.mel_augmentations = mel_augmentations
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self.audio_augmentations = audio_augmentations
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self._task_type = task_type
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# Find all raw files and turn generator to persistent list:
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self._wav_files = list(self.wav_folder.rglob('*.wav'))
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# Build the Dataset
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self._dataset = self._build_dataset()
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def __len__(self):
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raise NotImplementedError
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def __getitem__(self, item):
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raise NotImplementedError
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def _build_dataset(self):
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raise NotImplementedError
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def _check_reset_and_clean_up(self, reset):
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all_mel_folders = set([str(x.parent).replace(self._wav_folder_name, 'mel') for x in self._wav_files])
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for mel_folder in all_mel_folders:
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param_storage = Path(mel_folder) / 'data_params.pik'
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param_storage.parent.mkdir(parents=True, exist_ok=True)
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try:
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pik_data = param_storage.read_bytes()
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fingerprint = pickle.loads(pik_data)
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if fingerprint == self._fingerprint:
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this_reset = reset
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else:
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print('Diverging parameters were found; Refreshing...')
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param_storage.unlink()
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pik_data = pickle.dumps(self._fingerprint)
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param_storage.write_bytes(pik_data)
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this_reset = True
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except FileNotFoundError:
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pik_data = pickle.dumps(self._fingerprint)
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param_storage.write_bytes(pik_data)
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this_reset = True
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if this_reset:
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all_mel_files = self.mel_folder.rglob(f'*{self._container_ext}')
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for mel_file in all_mel_files:
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mel_file.unlink()
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@@ -20,17 +20,20 @@ class BinaryMasksDataset(Dataset):
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@property
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def _fingerprint(self):
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return dict(**self._mel_kwargs, normalize=self.normalize)
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return dict(**self._mel_kwargs if self._mel_kwargs else dict())
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def __init__(self, data_root, setting, mel_transforms, transforms=None, stretch_dataset=False,
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use_preprocessed=True):
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use_preprocessed=True, mel_kwargs=None):
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self.stretch = stretch_dataset
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assert isinstance(setting, str), f'Setting has to be a string, but was: {type(setting)}.'
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assert setting in V.DATA_OPTIONS, f'Setting must match one of: {V.DATA_OPTIONS}.'
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super(BinaryMasksDataset, self).__init__()
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self.task = V.TASK_OPTION_binary
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self.data_root = Path(data_root) / 'ComParE2020_Mask'
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self.setting = setting
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self._mel_kwargs = mel_kwargs
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self._wav_folder = self.data_root / 'wav'
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self._mel_folder = self.data_root / 'mel'
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self.container_ext = '.pik'
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@@ -1,140 +1,78 @@
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import pickle
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from pathlib import Path
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import multiprocessing as mp
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from typing import Union, List
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import librosa as librosa
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from torch.utils.data import Dataset, ConcatDataset
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import multiprocessing as mp
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from torch.utils.data import ConcatDataset
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import torch
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from tqdm import tqdm
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import variables as V
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from ml_lib.audio_toolset.mel_dataset import TorchMelDataset
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from ml_lib.modules.util import F_x
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from datasets.base_dataset import BaseAudioToMelDataset
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from ml_lib.audio_toolset.audio_to_mel_dataset import LibrosaAudioToMelDataset, PyTorchAudioToMelDataset
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class Urban8K(Dataset):
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try:
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torch.multiprocessing.set_sharing_strategy('file_system')
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except AttributeError:
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pass
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@property
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def sample_shape(self):
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return self[0][0].shape
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class Urban8K(BaseAudioToMelDataset):
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@property
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def _fingerprint(self):
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return str(self._mel_transform)
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def __init__(self, data_root, setting, mel_transforms, fold=1, transforms=None,
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use_preprocessed=True, audio_segment_len=62, audio_hop_len=30, num_worker=mp.cpu_count(),
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**_):
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def __init__(self,
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data_root, setting, fold: Union[int, List]=1, num_worker=mp.cpu_count(),
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reset=False, sample_segment_len=50, sample_hop_len=20,
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**kwargs):
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self.num_worker = num_worker
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assert isinstance(setting, str), f'Setting has to be a string, but was: {type(setting)}.'
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assert setting in V.DATA_OPTIONS, f'Setting must match one of: {V.DATA_OPTIONS}.'
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assert fold in range(1, 11)
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super(Urban8K, self).__init__()
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assert fold in range(1, 11) if isinstance(fold, int) else all([f in range(1, 11) for f in fold])
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self.data_root = Path(data_root) / 'UrbanSound8K'
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#Dataset Paramters
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self.setting = setting
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self.num_worker = num_worker
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self.fold = fold if self.setting == V.DATA_OPTIONS.train else 10
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self.use_preprocessed = use_preprocessed
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self._wav_folder = self.data_root / 'audio' / f'fold{self.fold}'
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self._mel_folder = self.data_root / 'mel' / f'fold{self.fold}'
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self.container_ext = '.pik'
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self._mel_transform = mel_transforms
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fold = fold if self.setting != V.DATA_OPTION_test else 10
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self.fold = fold if isinstance(fold, list) else [fold]
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self._labels = self._build_labels()
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self._wav_files = list(sorted(self._labels.keys()))
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transforms = transforms or F_x(in_shape=None)
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self.sample_segment_len = sample_segment_len
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self.sample_hop_len = sample_hop_len
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param_storage = self._mel_folder / 'data_params.pik'
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self._mel_folder.mkdir(parents=True, exist_ok=True)
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try:
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pik_data = param_storage.read_bytes()
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fingerprint = pickle.loads(pik_data)
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if fingerprint == self._fingerprint:
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self.use_preprocessed = use_preprocessed
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else:
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print('Diverging parameters were found; Refreshing...')
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param_storage.unlink()
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pik_data = pickle.dumps(self._fingerprint)
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param_storage.write_bytes(pik_data)
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self.use_preprocessed = False
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# Dataset specific super init
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super(Urban8K, self).__init__(Path(data_root) / 'UrbanSound8K',
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V.TASK_OPTION_multiclass, reset=reset, wav_folder_name='audio', **kwargs
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)
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except FileNotFoundError:
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pik_data = pickle.dumps(self._fingerprint)
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param_storage.write_bytes(pik_data)
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self.use_preprocessed = False
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def _build_subdataset(self, row):
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slice_file_name, fs_id, start, end, salience, fold, class_id, class_name = row.strip().split(',')
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fold, class_id = (int(x) for x in (fold, class_id))
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if int(fold) in self.fold:
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audio_file_path = self.wav_folder / f'fold{fold}' / slice_file_name
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return PyTorchAudioToMelDataset(audio_file_path, class_id, **self.__dict__)
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else:
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return None
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while True:
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if not self.use_preprocessed:
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self._pre_process()
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try:
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self._dataset = ConcatDataset(
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[TorchMelDataset(identifier=key, mel_path=self._mel_folder, transform=transforms,
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segment_len=audio_segment_len, hop_len=audio_hop_len,
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label=self._labels[key]['label']
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) for key in self._labels.keys()]
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)
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break
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except IOError:
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self.use_preprocessed = False
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pass
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def _build_labels(self):
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labeldict = dict()
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def _build_dataset(self):
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dataset= list()
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with open(Path(self.data_root) / 'metadata' / 'UrbanSound8K.csv', mode='r') as f:
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# Exclude the header
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_ = next(f)
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for row in f:
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slice_file_name, fs_id, start, end, salience, fold, class_id, class_name = row.strip().split(',')
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if int(fold) == self.fold:
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key = slice_file_name.replace('.wav', '')
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labeldict[key] = dict(label=int(class_id), fold=int(fold))
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all_rows = list(f)
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chunksize = len(all_rows) // max(self.num_worker, 1)
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with mp.Pool(processes=self.num_worker) as pool:
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with tqdm(total=len(all_rows)) as pbar:
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for i, sub_dataset in enumerate(
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pool.imap_unordered(self._build_subdataset, all_rows, chunksize=chunksize)):
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pbar.update()
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dataset.append(sub_dataset)
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# Delete File if one exists.
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if not self.use_preprocessed:
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for key in labeldict.keys():
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for mel_file in self._mel_folder.rglob(f'{key}_*'):
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try:
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mel_file.unlink(missing_ok=True)
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except FileNotFoundError:
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pass
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return labeldict
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dataset = ConcatDataset([x for x in dataset if x is not None])
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return dataset
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def __len__(self):
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return len(self._dataset)
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def _pre_process(self):
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print('Preprocessing Mel Files....')
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with mp.Pool(processes=self.num_worker) as pool:
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with tqdm(total=len(self._labels)) as pbar:
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for i, _ in enumerate(pool.imap_unordered(self._build_mel, self._labels.keys())):
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pbar.update()
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def _build_mel(self, filename):
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wav_file = self._wav_folder / (filename.replace('X', '') + '.wav')
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mel_file = list(self._mel_folder.glob(f'{filename}_*'))
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if not mel_file:
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raw_sample, sr = librosa.core.load(wav_file)
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mel_sample = self._mel_transform(raw_sample)
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m, n = mel_sample.shape
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mel_file = self._mel_folder / f'{filename}_{m}_{n}'
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self._mel_folder.mkdir(exist_ok=True, parents=True)
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with mel_file.open(mode='wb') as f:
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pickle.dump(mel_sample, f, protocol=pickle.HIGHEST_PROTOCOL)
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else:
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# print(f"Already existed.. Skipping {filename}")
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mel_file = mel_file[0]
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with mel_file.open(mode='rb') as f:
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mel_sample = pickle.load(f, fix_imports=True)
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return mel_sample, mel_file
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def __getitem__(self, item):
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transformed_samples, label = self._dataset[item]
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label = torch.as_tensor(label, dtype=torch.float)
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label = torch.as_tensor(label, dtype=torch.int)
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return transformed_samples, label
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@@ -1,140 +0,0 @@
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import pickle
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from pathlib import Path
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import multiprocessing as mp
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import librosa as librosa
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from torch.utils.data import Dataset, ConcatDataset
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import torch
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from tqdm import tqdm
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import variables as V
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from ml_lib.audio_toolset.mel_dataset import TorchMelDataset
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from ml_lib.modules.util import F_x
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class Urban8K_TO(Dataset):
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@property
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def sample_shape(self):
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return self[0][0].shape
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@property
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def _fingerprint(self):
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return str(self._mel_transform)
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def __init__(self, data_root, setting, mel_transforms, fold=1, transforms=None,
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use_preprocessed=True, audio_segment_len=1, audio_hop_len=1, num_worker=mp.cpu_count(),
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**_):
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assert isinstance(setting, str), f'Setting has to be a string, but was: {type(setting)}.'
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assert setting in V.DATA_OPTIONS, f'Setting must match one of: {V.DATA_OPTIONS}.'
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assert fold in range(1, 11)
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super(Urban8K_TO, self).__init__()
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self.data_root = Path(data_root) / 'UrbanSound8K'
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self.setting = setting
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self.num_worker = num_worker
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self.fold = fold if self.setting == V.DATA_OPTIONS.train else 10
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self.use_preprocessed = use_preprocessed
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self._wav_folder = self.data_root / 'audio' / f'fold{self.fold}'
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self._mel_folder = self.data_root / 'mel' / f'fold{self.fold}'
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self.container_ext = '.pik'
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self._mel_transform = mel_transforms
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self._labels = self._build_labels()
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self._wav_files = list(sorted(self._labels.keys()))
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transforms = transforms or F_x(in_shape=None)
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param_storage = self._mel_folder / 'data_params.pik'
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self._mel_folder.mkdir(parents=True, exist_ok=True)
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try:
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pik_data = param_storage.read_bytes()
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fingerprint = pickle.loads(pik_data)
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if fingerprint == self._fingerprint:
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self.use_preprocessed = use_preprocessed
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else:
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print('Diverging parameters were found; Refreshing...')
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param_storage.unlink()
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pik_data = pickle.dumps(self._fingerprint)
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param_storage.write_bytes(pik_data)
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self.use_preprocessed = False
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except FileNotFoundError:
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pik_data = pickle.dumps(self._fingerprint)
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param_storage.write_bytes(pik_data)
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self.use_preprocessed = False
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while True:
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if not self.use_preprocessed:
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self._pre_process()
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try:
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self._dataset = ConcatDataset(
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[TorchMelDataset(identifier=key, mel_path=self._mel_folder, transform=transforms,
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segment_len=audio_segment_len, hop_len=audio_hop_len,
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label=self._labels[key]['label']
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) for key in self._labels.keys()]
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)
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break
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except IOError:
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self.use_preprocessed = False
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pass
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def _build_labels(self):
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labeldict = dict()
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with open(Path(self.data_root) / 'metadata' / 'UrbanSound8K.csv', mode='r') as f:
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# Exclude the header
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_ = next(f)
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for row in f:
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slice_file_name, fs_id, start, end, salience, fold, class_id, class_name = row.strip().split(',')
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if int(fold) == self.fold:
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key = slice_file_name.replace('.wav', '')
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labeldict[key] = dict(label=int(class_id), fold=int(fold))
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# Delete File if one exists.
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if not self.use_preprocessed:
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for key in labeldict.keys():
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for mel_file in self._mel_folder.rglob(f'{key}_*'):
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try:
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mel_file.unlink(missing_ok=True)
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except FileNotFoundError:
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pass
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return labeldict
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def __len__(self):
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return len(self._dataset)
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def _pre_process(self):
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print('Preprocessing Mel Files....')
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with mp.Pool(processes=self.num_worker) as pool:
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with tqdm(total=len(self._labels)) as pbar:
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for i, _ in enumerate(pool.imap_unordered(self._build_mel, self._labels.keys())):
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pbar.update()
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def _build_mel(self, filename):
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wav_file = self._wav_folder / (filename.replace('X', '') + '.wav')
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mel_file = list(self._mel_folder.glob(f'{filename}_*'))
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if not mel_file:
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raw_sample, sr = librosa.core.load(wav_file)
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mel_sample = self._mel_transform(raw_sample)
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m, n = mel_sample.shape
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mel_file = self._mel_folder / f'{filename}_{m}_{n}'
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self._mel_folder.mkdir(exist_ok=True, parents=True)
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with mel_file.open(mode='wb') as f:
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pickle.dump(mel_sample, f, protocol=pickle.HIGHEST_PROTOCOL)
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else:
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# print(f"Already existed.. Skipping {filename}")
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mel_file = mel_file[0]
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|
||||
with mel_file.open(mode='rb') as f:
|
||||
mel_sample = pickle.load(f, fix_imports=True)
|
||||
return mel_sample, mel_file
|
||||
|
||||
def __getitem__(self, item):
|
||||
transformed_samples, label = self._dataset[item]
|
||||
|
||||
label = torch.as_tensor(label, dtype=torch.float)
|
||||
|
||||
return transformed_samples, label
|
||||
Reference in New Issue
Block a user