You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/paddlespeech/audio/datasets/urban_sound.py

105 lines
3.9 KiB

# 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 import DATA_HOME
from ..utils.download import download_and_decompress
from .dataset import AudioClassificationDataset
__all__ = ['UrbanSound8K']
class UrbanSound8K(AudioClassificationDataset):
"""
UrbanSound8K dataset contains 8732 labeled sound excerpts (<=4s) of urban
sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark,
drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. The
classes are drawn from the urban sound taxonomy.
Reference:
A Dataset and Taxonomy for Urban Sound Research
https://dl.acm.org/doi/10.1145/2647868.2655045
"""
archieves = [
{
'url':
'https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz',
'md5': '9aa69802bbf37fb986f71ec1483a196e',
},
]
label_list = [
"air_conditioner", "car_horn", "children_playing", "dog_bark",
"drilling", "engine_idling", "gun_shot", "jackhammer", "siren",
"street_music"
]
meta = os.path.join('UrbanSound8K', 'metadata', 'UrbanSound8K.csv')
meta_info = collections.namedtuple(
'META_INFO', ('filename', 'fsid', 'start', 'end', 'salience', 'fold',
'class_id', 'label'))
audio_path = os.path.join('UrbanSound8K', 'audio')
def __init__(self,
mode: str='train',
split: int=1,
feat_type: str='raw',
**kwargs):
files, labels = self._get_data(mode, split)
super(UrbanSound8K, self).__init__(
files=files, labels=labels, feat_type=feat_type, **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.
"""
def _get_meta_info(self):
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, f'fold{fold}',
filename))
labels.append(int(target))
if mode != 'train' and int(fold) == split:
files.append(
os.path.join(DATA_HOME, self.audio_path, f'fold{fold}',
filename))
labels.append(int(target))
return files, labels