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PaddleSpeech/audio/paddleaudio/datasets/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 ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['ESC50']
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
"""
archieves = [
{
'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',
**kwargs):
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
split (:obj:`int`, `optional`, defaults to 1):
It specify the fold of dev dataset.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
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_and_decompress(self.archieves, DATA_HOME)
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