chore: import upstream snapshot with attribution
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wehub-resource-sync
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# 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 __future__ import annotations
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import os
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from typing import TYPE_CHECKING, Any, Literal, NamedTuple, TypeAlias
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from paddle.dataset.common import DATA_HOME
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from paddle.utils import download
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from .dataset import AudioClassificationDataset
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if TYPE_CHECKING:
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_ModeLiteral: TypeAlias = Literal[
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'train',
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'dev',
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]
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_FeatTypeLiteral: TypeAlias = Literal[
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'raw',
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'melspectrogram',
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'mfcc',
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'logmelspectrogram',
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'spectrogram',
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]
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__all__ = []
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class ESC50(AudioClassificationDataset):
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"""
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The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings
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suitable for benchmarking methods of environmental sound classification. The dataset
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consists of 5-second-long recordings organized into 50 semantical classes (with
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40 examples per class)
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Reference:
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ESC: Dataset for Environmental Sound Classification
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http://dx.doi.org/10.1145/2733373.2806390
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Args:
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mode (str, optional): It identifies the dataset mode (train or dev). Default:train.
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split (int, optional): It specify the fold of dev dataset. Default:1.
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feat_type (str, optional): It identifies the feature type that user wants to extract of an audio file. Default:raw.
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archive(dict, optional): it tells where to download the audio archive. Default:None.
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Returns:
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:ref:`api_paddle_io_Dataset`. An instance of ESC50 dataset.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +TIMEOUT(60)
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>>> import paddle
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>>> esc50_dataset = paddle.audio.datasets.ESC50(
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... mode='dev',
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... feat_type='raw',
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... )
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>>> for idx in range(5):
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... audio, label = esc50_dataset[idx]
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... # do something with audio, label
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... print(audio.shape, label)
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... # [audio_data_length] , label_id
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paddle.Size([220500]) 0
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paddle.Size([220500]) 14
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paddle.Size([220500]) 36
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paddle.Size([220500]) 36
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paddle.Size([220500]) 19
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>>> esc50_dataset = paddle.audio.datasets.ESC50(
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... mode='dev',
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... feat_type='mfcc',
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... n_mfcc=40,
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... )
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>>> for idx in range(5):
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... audio, label = esc50_dataset[idx]
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... # do something with mfcc feature, label
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... print(audio.shape, label)
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... # [feature_dim, length] , label_id
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paddle.Size([40, 1723]) 0
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paddle.Size([40, 1723]) 14
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paddle.Size([40, 1723]) 36
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paddle.Size([40, 1723]) 36
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paddle.Size([40, 1723]) 19
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"""
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archive: dict[str, str] = {
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'url': 'https://paddleaudio.bj.bcebos.com/datasets/ESC-50-master.zip',
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'md5': '7771e4b9d86d0945acce719c7a59305a',
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}
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label_list: list[str] = [
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# Animals
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'Dog',
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'Rooster',
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'Pig',
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'Cow',
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'Frog',
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'Cat',
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'Hen',
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'Insects (flying)',
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'Sheep',
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'Crow',
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# Natural soundscapes & water sounds
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'Rain',
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'Sea waves',
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'Crackling fire',
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'Crickets',
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'Chirping birds',
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'Water drops',
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'Wind',
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'Pouring water',
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'Toilet flush',
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'Thunderstorm',
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# Human, non-speech sounds
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'Crying baby',
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'Sneezing',
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'Clapping',
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'Breathing',
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'Coughing',
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'Footsteps',
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'Laughing',
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'Brushing teeth',
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'Snoring',
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'Drinking, sipping',
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# Interior/domestic sounds
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'Door knock',
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'Mouse click',
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'Keyboard typing',
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'Door, wood creaks',
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'Can opening',
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'Washing machine',
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'Vacuum cleaner',
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'Clock alarm',
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'Clock tick',
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'Glass breaking',
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# Exterior/urban noises
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'Helicopter',
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'Chainsaw',
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'Siren',
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'Car horn',
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'Engine',
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'Train',
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'Church bells',
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'Airplane',
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'Fireworks',
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'Hand saw',
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]
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meta: str = os.path.join('ESC-50-master', 'meta', 'esc50.csv')
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audio_path: str = os.path.join('ESC-50-master', 'audio')
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class meta_info(NamedTuple):
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filename: str
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fold: str
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target: str
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category: str
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esc10: str
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src_file: str
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take: str
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def __init__(
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self,
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mode: _ModeLiteral = 'train',
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split: int = 1,
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feat_type: _FeatTypeLiteral = 'raw',
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archive: dict[str, str] | None = None,
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**kwargs: Any,
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) -> None:
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assert split in range(1, 6), (
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f'The selected split should be integer, and 1 <= split <= 5, but got {split}'
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)
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if archive is not None:
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self.archive = archive
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files, labels = self._get_data(mode, split)
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super().__init__(
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files=files, labels=labels, feat_type=feat_type, **kwargs
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)
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def _get_meta_info(self) -> list[meta_info]:
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ret = []
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with open(os.path.join(DATA_HOME, self.meta), 'r') as rf:
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for line in rf.readlines()[1:]:
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ret.append(self.meta_info(*line.strip().split(',')))
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return ret
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def _get_data(
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self, mode: _ModeLiteral, split: int
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) -> tuple[list[str], list[int]]:
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if not os.path.isdir(
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os.path.join(DATA_HOME, self.audio_path)
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) or not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
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download.get_path_from_url(
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self.archive['url'],
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DATA_HOME,
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self.archive['md5'],
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decompress=True,
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)
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meta_info = self._get_meta_info()
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files = []
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labels = []
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for sample in meta_info:
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filename, fold, target, _, _, _, _ = sample
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if mode == 'train' and int(fold) != split:
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files.append(os.path.join(DATA_HOME, self.audio_path, filename))
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labels.append(int(target))
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if mode != 'train' and int(fold) == split:
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files.append(os.path.join(DATA_HOME, self.audio_path, filename))
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labels.append(int(target))
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return files, labels
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