Datasets
Dataset classes are provided in the library to create uniform interface for many differently organized audio datasets. The datasets are downloaded, extracted and prepared for usage when they are first time used.
Four type of datasets are provided:
Acoustic scene datasets, classes inherited from
dcase_util.datasets.AcousticSceneDatasetclass.Sound event datasets, classes inherited from
dcase_util.datasets.SoundEventDatasetclass.Sound event datasets with synthetic data creation, classes inherited from
dcase_util.datasets.SyntheticSoundEventDatasetclass.Audio tagging datasets, classes inherited from
dcase_util.datasets.AudioTaggingDatasetclass.
To get list of all datasets available:
print(dcase_util.datasets.dataset_list())
# Dataset list
# Class Name | Group | Remote | Local | Audio | Scenes | Events | Tags
# ------------------------------------------ | ----- | ------ | ------ | ----- | ------ | ------ | ----
# DCASE2013_Scenes_DevelopmentSet | scene | 344 MB | 849 MB | 100 | 10 | |
# TUTAcousticScenes_2016_DevelopmentSet | scene | 7 GB | 23 GB | 1170 | 15 | |
# TUTAcousticScenes_2016_EvaluationSet | scene | 2 GB | 5 GB | 390 | 15 | |
# TUTAcousticScenes_2017_DevelopmentSet | scene | 9 GB | 21 GB | 4680 | 15 | |
# TUTAcousticScenes_2017_EvaluationSet | scene | 3 GB | 7 GB | | | |
# DCASE2017_Task4tagging_DevelopmentSet | event | 5 MB | 24 GB | 56700 | 1 | 17 |
# DCASE2017_Task4tagging_EvaluationSet | event | 823 MB | 1 GB | | | |
# TUTRareSoundEvents_2017_DevelopmentSet | event | 7 GB | 28 GB | | | 3 |
# TUTRareSoundEvents_2017_EvaluationSet | event | 4 GB | 4 GB | | | 3 |
# TUTSoundEvents_2016_DevelopmentSet | event | 967 MB | 2 GB | 954 | 2 | 17 |
# TUTSoundEvents_2016_EvaluationSet | event | 449 MB | 989 MB | 511 | 2 | 17 |
# TUTSoundEvents_2017_DevelopmentSet | event | 1 GB | 2 GB | 659 | 1 | 6 |
# TUTSoundEvents_2017_EvaluationSet | event | 370 MB | 798 MB | | | |
# TUT_SED_Synthetic_2016 | event | 4 GB | 5 GB | | | |
# CHiMEHome_DomesticAudioTag_DevelopmentSet | tag | 3 GB | 9 GB | 1946 | 1 | | 7
Initializing dataset
To download, extract and prepare dataset (in this case dataset will be place in the temp dir, and only files related to meta data are downloaded):
import tempfile
db = dcase_util.datasets.TUTAcousticScenes_2016_DevelopmentSet(
data_path=tempfile.gettempdir(),
included_content_types=['meta']
)
db.initialize()
db.show()
# DictContainer :: Class
# audio_source : Field recording
# audio_type : Natural
# authors : Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen
# licence : free non-commercial
# microphone_model : Soundman OKM II Klassik/studio A3 electret microphone
# recording_device_model : Roland Edirol R-09
# title : TUT Acoustic Scenes 2016, development dataset
# url : https://zenodo.org/record/45739
#
# MetaDataContainer :: Class
# Filename : /tmp/TUT-acoustic-scenes-2016-development/meta.txt
# Items : 1170
# Unique
# Files : 1170
# Scene labels : 15
# Event labels : 0
# Tags : 0
#
# Scene statistics
# Scene label Count
# -------------------- ------
# beach 78
# bus 78
# cafe/restaurant 78
# car 78
# city_center 78
# forest_path 78
# grocery_store 78
# home 78
# library 78
# metro_station 78
# office 78
# park 78
# residential_area 78
# train 78
# tram 78
Cross-validation setup
Usually dataset are delivered with cross-validation setup.
To get training material for the fold 1:
training_material = db.train(fold=1)
training_material.show()
# MetaDataContainer :: Class
# Filename : /tmp/TUT-acoustic-scenes-2016-development/evaluation_setup/fold1_train.txt
# Items : 880
# Unique
# Files : 880
# Scene labels : 15
# Event labels : 0
# Tags : 0
#
# Scene statistics
# Scene label Count
# -------------------- ------
# beach 59
# bus 59
# cafe/restaurant 60
# car 58
# city_center 60
# forest_path 57
# grocery_store 59
# home 56
# library 57
# metro_station 59
# office 59
# park 58
# residential_area 59
# train 60
# tram 60
To get testing material for the fold 1 (material without reference data):
testing_material = db.test(fold=1)
testing_material.show()
# MetaDataContainer :: Class
# Filename : /tmp/TUT-acoustic-scenes-2016-development/evaluation_setup/fold1_test.txt
# Items : 290
# Unique
# Files : 290
# Scene labels : 0
# Event labels : 0
# Tags : 0
To get testing material for the fold 1 with full reference data:
eval_material = db.eval(fold=1)
eval_material.show()
# MetaDataContainer :: Class
# Filename : /tmp/TUT-acoustic-scenes-2016-development/evaluation_setup/fold1_evaluate.txt
# Items : 290
# Unique
# Files : 290
# Scene labels : 15
# Event labels : 0
# Tags : 0
#
# Scene statistics
# Scene label Count
# -------------------- ------
# beach 19
# bus 19
# cafe/restaurant 18
# car 20
# city_center 18
# forest_path 21
# grocery_store 19
# home 22
# library 21
# metro_station 19
# office 19
# park 20
# residential_area 19
# train 18
# tram 18
To get all data set fold to None:
all_material = db.train(fold=None)
all_material.show()
# MetaDataContainer :: Class
# Filename : /tmp/TUT-acoustic-scenes-2016-development/meta.txt
# Items : 1170
# Unique
# Files : 1170
# Scene labels : 15
# Event labels : 0
# Tags : 0
#
# Scene statistics
# Scene label Count
# -------------------- ------
# beach 78
# bus 78
# cafe/restaurant 78
# car 78
# city_center 78
# forest_path 78
# grocery_store 78
# home 78
# library 78
# metro_station 78
# office 78
# park 78
# residential_area 78
# train 78
# tram 78
Iterating over all folds:
for fold in db.folds:
train_material = db.train(fold=fold)
Most of the datasets do not provide validation set split. However, dataset class provides a few way to generate it from the training set while maintaining the data statistics and making sure there will not be data from same source in both training and validation set.
Generating balanced validation set (balancing done so that recording from same location are assigned to same set) for fold 1:
training_files, validation_files = db.validation_split(
fold=1,
split_type='balanced',
validation_amount=0.3
)
Generating random validation set (without balancing) for fold 1:
training_files, validation_files = db.validation_split(
fold=1,
split_type='random',
validation_amount=0.3
)
Getting validation set provided with dataset (dataset used in the example does not provide it, and this will raise an error.):
training_files, validation_files = db.validation_split(
fold=1,
split_type='dataset'
)
Meta data
Getting meta data associated to the file:
items = db.file_meta(filename='audio/b086_150_180.wav')
print(items)
# MetaDataContainer :: Class
# Items : 1
# Unique
# Files : 1
# Scene labels : 1
# Event labels : 0
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# audio/b086_150_180.. - - grocery_store - - -
#
# Scene statistics
# Scene label Count
# -------------------- ------
# grocery_store 1