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299 lines
11 KiB
299 lines
11 KiB
3 years ago
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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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import collections
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import os
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from typing import List
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from typing import Tuple
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from ..utils.download import download_and_decompress
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from ..utils.env import DATA_HOME
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from .dataset import AudioClassificationDataset
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__all__ = ['UrbanAcousticScenes', 'UrbanAudioVisualScenes']
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class UrbanAcousticScenes(AudioClassificationDataset):
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"""
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TAU Urban Acoustic Scenes 2020 Mobile Development dataset contains recordings from
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12 European cities in 10 different acoustic scenes using 4 different devices.
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Additionally, synthetic data for 11 mobile devices was created based on the original
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recordings. Of the 12 cities, two are present only in the evaluation set.
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Reference:
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A multi-device dataset for urban acoustic scene classification
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https://arxiv.org/abs/1807.09840
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"""
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source_url = 'https://zenodo.org/record/3819968/files/'
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base_name = 'TAU-urban-acoustic-scenes-2020-mobile-development'
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archieves = [
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{
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'url': source_url + base_name + '.meta.zip',
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'md5': '6eae9db553ce48e4ea246e34e50a3cf5',
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},
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{
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'url': source_url + base_name + '.audio.1.zip',
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'md5': 'b1e85b8a908d3d6a6ab73268f385d5c8',
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},
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{
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'url': source_url + base_name + '.audio.2.zip',
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'md5': '4310a13cc2943d6ce3f70eba7ba4c784',
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},
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{
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'url': source_url + base_name + '.audio.3.zip',
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'md5': 'ed38956c4246abb56190c1e9b602b7b8',
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},
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{
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'url': source_url + base_name + '.audio.4.zip',
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'md5': '97ab8560056b6816808dedc044dcc023',
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},
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{
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'url': source_url + base_name + '.audio.5.zip',
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'md5': 'b50f5e0bfed33cd8e52cb3e7f815c6cb',
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},
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{
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'url': source_url + base_name + '.audio.6.zip',
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'md5': 'fbf856a3a86fff7520549c899dc94372',
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},
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{
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'url': source_url + base_name + '.audio.7.zip',
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'md5': '0dbffe7b6e45564da649378723284062',
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},
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{
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'url': source_url + base_name + '.audio.8.zip',
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'md5': 'bb6f77832bf0bd9f786f965beb251b2e',
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},
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{
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'url': source_url + base_name + '.audio.9.zip',
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'md5': 'a65596a5372eab10c78e08a0de797c9e',
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},
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{
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'url': source_url + base_name + '.audio.10.zip',
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'md5': '2ad595819ffa1d56d2de4c7ed43205a6',
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},
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{
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'url': source_url + base_name + '.audio.11.zip',
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'md5': '0ad29f7040a4e6a22cfd639b3a6738e5',
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},
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{
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'url': source_url + base_name + '.audio.12.zip',
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'md5': 'e5f4400c6b9697295fab4cf507155a2f',
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},
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{
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'url': source_url + base_name + '.audio.13.zip',
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'md5': '8855ab9f9896422746ab4c5d89d8da2f',
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},
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{
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'url': source_url + base_name + '.audio.14.zip',
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'md5': '092ad744452cd3e7de78f988a3d13020',
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},
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{
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'url': source_url + base_name + '.audio.15.zip',
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'md5': '4b5eb85f6592aebf846088d9df76b420',
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},
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{
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'url': source_url + base_name + '.audio.16.zip',
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'md5': '2e0a89723e58a3836be019e6996ae460',
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},
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]
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label_list = [
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'airport', 'shopping_mall', 'metro_station', 'street_pedestrian',
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'public_square', 'street_traffic', 'tram', 'bus', 'metro', 'park'
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]
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meta = os.path.join(base_name, 'meta.csv')
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meta_info = collections.namedtuple('META_INFO', (
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'filename', 'scene_label', 'identifier', 'source_label'))
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subset_meta = {
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'train': os.path.join(base_name, 'evaluation_setup', 'fold1_train.csv'),
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'dev':
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os.path.join(base_name, 'evaluation_setup', 'fold1_evaluate.csv'),
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'test': os.path.join(base_name, 'evaluation_setup', 'fold1_test.csv'),
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}
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subset_meta_info = collections.namedtuple('SUBSET_META_INFO',
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('filename', 'scene_label'))
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audio_path = os.path.join(base_name, 'audio')
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def __init__(self, mode: str='train', feat_type: str='raw', **kwargs):
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"""
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Ags:
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mode (:obj:`str`, `optional`, defaults to `train`):
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It identifies the dataset mode (train or dev).
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feat_type (:obj:`str`, `optional`, defaults to `raw`):
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It identifies the feature type that user wants to extrace of an audio file.
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"""
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files, labels = self._get_data(mode)
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super(UrbanAcousticScenes, self).__init__(
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files=files, labels=labels, feat_type=feat_type, **kwargs)
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def _get_meta_info(self, subset: str=None,
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skip_header: bool=True) -> List[collections.namedtuple]:
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if subset is None:
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meta_file = self.meta
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meta_info = self.meta_info
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else:
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assert subset in self.subset_meta, f'Subset must be one in {list(self.subset_meta.keys())}, but got {subset}.'
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meta_file = self.subset_meta[subset]
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meta_info = self.subset_meta_info
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ret = []
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with open(os.path.join(DATA_HOME, meta_file), 'r') as rf:
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lines = rf.readlines()[1:] if skip_header else rf.readlines()
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for line in lines:
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ret.append(meta_info(*line.strip().split('\t')))
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return ret
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def _get_data(self, mode: str) -> Tuple[List[str], List[int]]:
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if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
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not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
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download_and_decompress(self.archieves, DATA_HOME)
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meta_info = self._get_meta_info(subset=mode, skip_header=True)
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files = []
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labels = []
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for sample in meta_info:
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filename, label = sample[:2]
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filename = os.path.basename(filename)
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target = self.label_list.index(label)
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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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class UrbanAudioVisualScenes(AudioClassificationDataset):
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"""
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TAU Urban Audio Visual Scenes 2021 Development dataset contains synchronized audio
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and video recordings from 12 European cities in 10 different scenes.
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This dataset consists of 10-seconds audio and video segments from 10
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acoustic scenes. The total amount of audio in the development set is 34 hours.
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Reference:
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A Curated Dataset of Urban Scenes for Audio-Visual Scene Analysis
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https://arxiv.org/abs/2011.00030
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"""
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source_url = 'https://zenodo.org/record/4477542/files/'
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base_name = 'TAU-urban-audio-visual-scenes-2021-development'
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archieves = [
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{
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'url': source_url + base_name + '.meta.zip',
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'md5': '76e3d7ed5291b118372e06379cb2b490',
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},
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{
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'url': source_url + base_name + '.audio.1.zip',
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'md5': '186f6273f8f69ed9dbdc18ad65ac234f',
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},
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{
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'url': source_url + base_name + '.audio.2.zip',
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'md5': '7fd6bb63127f5785874a55aba4e77aa5',
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},
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{
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'url': source_url + base_name + '.audio.3.zip',
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'md5': '61396bede29d7c8c89729a01a6f6b2e2',
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},
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{
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'url': source_url + base_name + '.audio.4.zip',
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'md5': '6ddac89717fcf9c92c451868eed77fe1',
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},
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{
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'url': source_url + base_name + '.audio.5.zip',
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'md5': 'af4820756cdf1a7d4bd6037dc034d384',
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},
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{
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'url': source_url + base_name + '.audio.6.zip',
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'md5': 'ebd11ec24411f2a17a64723bd4aa7fff',
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},
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{
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'url': source_url + base_name + '.audio.7.zip',
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'md5': '2be39a76aeed704d5929d020a2909efd',
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},
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{
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'url': source_url + base_name + '.audio.8.zip',
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'md5': '972d8afe0874720fc2f28086e7cb22a9',
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},
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]
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label_list = [
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'airport', 'shopping_mall', 'metro_station', 'street_pedestrian',
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'public_square', 'street_traffic', 'tram', 'bus', 'metro', 'park'
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]
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meta_base_path = os.path.join(base_name, base_name + '.meta')
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meta = os.path.join(meta_base_path, 'meta.csv')
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meta_info = collections.namedtuple('META_INFO', (
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'filename_audio', 'filename_video', 'scene_label', 'identifier'))
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subset_meta = {
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'train':
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os.path.join(meta_base_path, 'evaluation_setup', 'fold1_train.csv'),
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'dev':
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os.path.join(meta_base_path, 'evaluation_setup', 'fold1_evaluate.csv'),
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'test':
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os.path.join(meta_base_path, 'evaluation_setup', 'fold1_test.csv'),
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}
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subset_meta_info = collections.namedtuple('SUBSET_META_INFO', (
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'filename_audio', 'filename_video', 'scene_label'))
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audio_path = os.path.join(base_name, 'audio')
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def __init__(self, mode: str='train', feat_type: str='raw', **kwargs):
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"""
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Ags:
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mode (:obj:`str`, `optional`, defaults to `train`):
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It identifies the dataset mode (train or dev).
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feat_type (:obj:`str`, `optional`, defaults to `raw`):
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It identifies the feature type that user wants to extrace of an audio file.
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"""
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files, labels = self._get_data(mode)
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super(UrbanAudioVisualScenes, self).__init__(
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files=files, labels=labels, feat_type=feat_type, **kwargs)
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def _get_meta_info(self, subset: str=None,
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skip_header: bool=True) -> List[collections.namedtuple]:
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if subset is None:
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meta_file = self.meta
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meta_info = self.meta_info
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else:
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assert subset in self.subset_meta, f'Subset must be one in {list(self.subset_meta.keys())}, but got {subset}.'
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meta_file = self.subset_meta[subset]
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meta_info = self.subset_meta_info
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ret = []
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with open(os.path.join(DATA_HOME, meta_file), 'r') as rf:
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lines = rf.readlines()[1:] if skip_header else rf.readlines()
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for line in lines:
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ret.append(meta_info(*line.strip().split('\t')))
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return ret
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def _get_data(self, mode: str) -> Tuple[List[str], List[int]]:
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if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
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not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
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download_and_decompress(self.archieves,
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os.path.join(DATA_HOME, self.base_name))
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meta_info = self._get_meta_info(subset=mode, skip_header=True)
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files = []
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labels = []
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for sample in meta_info:
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filename, _, label = sample[:3]
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filename = os.path.basename(filename)
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target = self.label_list.index(label)
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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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