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esc50.py
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esc50.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 collections
import os
from typing import List
from typing import Tuple
from paddle.utils import download
from paddle.dataset.common import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = []
class ESC50(AudioClassificationDataset):
"""
The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings
suitable for benchmarking methods of environmental sound classification. The dataset
consists of 5-second-long recordings organized into 50 semantical classes (with
40 examples per class)
Reference:
ESC: Dataset for Environmental Sound Classification
http://dx.doi.org/10.1145/2733373.2806390
Args:
mode (str, optional): It identifies the dataset mode (train or dev). Default:train.
split (int, optional): It specify the fold of dev dataset. Default:1.
feat_type (str, optional): It identifies the feature type that user wants to extrace of an audio file. Default:raw.
archive(dict, optional): it tells where to download the audio archive. Default:None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of ESC50 dataset.
Examples:
.. code-block:: python
import paddle
mode = 'dev'
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='raw')
for idx in range(5):
audio, label = esc50_dataset[idx]
# do something with audio, label
print(audio.shape, label)
# [audio_data_length] , label_id
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='mfcc',
n_mfcc=40)
for idx in range(5):
audio, label = esc50_dataset[idx]
# do something with mfcc feature, label
print(audio.shape, label)
# [feature_dim, length] , label_id
"""
archive = {
'url': 'https://paddleaudio.bj.bcebos.com/datasets/ESC-50-master.zip',
'md5': '7771e4b9d86d0945acce719c7a59305a',
}
label_list = [
# Animals
'Dog',
'Rooster',
'Pig',
'Cow',
'Frog',
'Cat',
'Hen',
'Insects (flying)',
'Sheep',
'Crow',
# Natural soundscapes & water sounds
'Rain',
'Sea waves',
'Crackling fire',
'Crickets',
'Chirping birds',
'Water drops',
'Wind',
'Pouring water',
'Toilet flush',
'Thunderstorm',
# Human, non-speech sounds
'Crying baby',
'Sneezing',
'Clapping',
'Breathing',
'Coughing',
'Footsteps',
'Laughing',
'Brushing teeth',
'Snoring',
'Drinking, sipping',
# Interior/domestic sounds
'Door knock',
'Mouse click',
'Keyboard typing',
'Door, wood creaks',
'Can opening',
'Washing machine',
'Vacuum cleaner',
'Clock alarm',
'Clock tick',
'Glass breaking',
# Exterior/urban noises
'Helicopter',
'Chainsaw',
'Siren',
'Car horn',
'Engine',
'Train',
'Church bells',
'Airplane',
'Fireworks',
'Hand saw',
]
meta = os.path.join('ESC-50-master', 'meta', 'esc50.csv')
meta_info = collections.namedtuple(
'META_INFO',
('filename', 'fold', 'target', 'category', 'esc10', 'src_file', 'take'),
)
audio_path = os.path.join('ESC-50-master', 'audio')
def __init__(
self,
mode: str = 'train',
split: int = 1,
feat_type: str = 'raw',
archive=None,
**kwargs,
):
assert split in range(
1, 6
), f'The selected split should be integer, and 1 <= split <= 5, but got {split}'
if archive is not None:
self.archive = archive
files, labels = self._get_data(mode, split)
super(ESC50, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs
)
def _get_meta_info(self) -> List[collections.namedtuple]:
ret = []
with open(os.path.join(DATA_HOME, self.meta), 'r') as rf:
for line in rf.readlines()[1:]:
ret.append(self.meta_info(*line.strip().split(',')))
return ret
def _get_data(self, mode: str, split: int) -> Tuple[List[str], List[int]]:
if not os.path.isdir(
os.path.join(DATA_HOME, self.audio_path)
) or not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download.get_path_from_url(
self.archive['url'],
DATA_HOME,
self.archive['md5'],
decompress=True,
)
meta_info = self._get_meta_info()
files = []
labels = []
for sample in meta_info:
filename, fold, target, _, _, _, _ = sample
if mode == 'train' and int(fold) != split:
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
if mode != 'train' and int(fold) == split:
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
return files, labels