# -*- coding: utf-8 -*-
"""giantsteps_tempo Dataset Loader
name: GiantSteps (tempo+genre)
contact: Richard Vogl <richard.vogl@tuwien.ac.at>
Peter Knees <peter.knees@tuwien.ac.at>
description: collection of annotations for 664 2min(1) audio previews from
www.beatport.com
references: [1] Peter Knees, Ángel Faraldo, Perfecto Herrera, Richard Vogl,
Sebastian Böck, Florian Hörschläger, Mickael Le Goff: "Two data
sets for tempo estimation and key detection in electronic dance
music annotated from user corrections", Proc. of the 16th
Conference of the International Society for Music Information
Retrieval (ISMIR'15), Oct. 2015, Malaga, Spain.
[2] Hendrik Schreiber, Meinard Müller: "A Crowdsourced Experiment
for Tempo Estimation of Electronic Dance Music", Proc. of the
19th Conference of the International Society for Music
Information Retrieval (ISMIR'18), Sept. 2018, Paris, France.
annotations: tempo (bpm), genre
notes:
=========================================================================
The audio files (664 files, size ~1gb) can be downloaded from http://www.beatport.com/
using the bash script:
https://github.com/GiantSteps/giantsteps-tempo-dataset/blob/master/audio_dl.sh
To download the files manually use links of the following form:
http://geo-samples.beatport.com/lofi/<name of mp3 file>
e.g.:
http://geo-samples.beatport.com/lofi/5377710.LOFI.mp3
To convert the audio files to .wav use (bash + sox):
./convert_audio.sh
To retrieve the genre information, the JSON contained within the website was parsed.
The tempo annotation was extracted from forum entries of people correcting the bpm values (i.e. manual annotation of tempo).
For more information please contact creators.
[2] found some files without tempo. There are:
3041381.LOFI.mp3
3041383.LOFI.mp3
1327052.LOFI.mp3
Their v2 tempo is denoted as 0.0 in tempo and mirex and has no annotation in the JAMS format.
(1): Most of the audio files are 120 seconds long. Exceptions are:
name length
906760.LOFI.mp3 62
1327052.LOFI.mp3 70
4416506.LOFI.mp3 80
1855660.LOFI.mp3 119
3419452.LOFI.mp3 119
3577631.LOFI.mp3 119
"""
import librosa
import os
from mirdata import download_utils
from mirdata import track
from mirdata import utils
import numpy as np
import jams
DATASET_DIR = 'GiantSteps_tempo'
DATA = utils.LargeData('giantsteps_tempo_index.json')
REMOTES = {
'annotations': download_utils.RemoteFileMetadata(
filename='giantsteps-tempo-dataset-0b7d47ba8cae59d3535a02e3db69e2cf6d0af5bb.zip',
url='https://github.com/GiantSteps/giantsteps-tempo-dataset/archive/0b7d47ba8cae59d3535a02e3db69e2cf6d0af5bb.zip',
checksum='8fdafbaf505fe3f293bd912c92b72ac8',
destination_dir='',
)
}
[docs]class Track(track.Track):
"""giantsteps_tempo track class
Args:
track_id (str): track id of the track
data_home (str): Local path where the dataset is stored.
If `None`, looks for the data in the default directory, `~/mir_datasets`
Attributes:
audio_path (str): track audio path
title (str): title of the track
track_id (str): track id
annotation_v1_path (str): track annotation v1 path
annotation_v2_path (str): track annotation v2 path
"""
def __init__(self, track_id, data_home=None):
if track_id not in DATA.index:
raise ValueError(
'{} is not a valid track ID in giantsteps_tempo'.format(track_id)
)
self.track_id = track_id
if data_home is None:
data_home = utils.get_default_dataset_path(DATASET_DIR)
self._data_home = data_home
self._track_paths = DATA.index[track_id]
self.audio_path = os.path.join(self._data_home, self._track_paths['audio'][0])
self.annotation_v1_path = os.path.join(
self._data_home, self._track_paths['annotation_v1'][0]
)
self.annotation_v2_path = os.path.join(
self._data_home, self._track_paths['annotation_v2'][0]
)
self.title = self.audio_path.replace(".mp3", '').split('/')[-1].split('.')[0]
@utils.cached_property
def genre(self):
"""genre: human-labeled metadata annotation"""
return load_genre(self.annotation_v1_path)
@utils.cached_property
def tempo(self):
"""TempoData: tempo annotation ordered by confidence"""
return load_tempo(self.annotation_v1_path)
@utils.cached_property
def tempo_v2(self):
"""TempoData: tempos annotation ordered by confidence"""
return load_tempo(self.annotation_v2_path)
@property
def audio(self):
"""(np.ndarray, float): audio signal, sample rate"""
return load_audio(self.audio_path)
[docs] def to_jams(self):
"""Jams: the track's data in jams format"""
return jams.load(self.annotation_v1_path)
[docs] def to_jams_v2(self):
"""Jams: the track's data in jams format"""
return jams.load(self.annotation_v2_path)
[docs]def load_audio(audio_path):
"""Load a giantsteps_tempo audio file.
Args:
audio_path (str): path to audio file
Returns:
y (np.ndarray): the mono audio signal
sr (float): The sample rate of the audio file
"""
if not os.path.exists(audio_path):
raise IOError("audio_path {} does not exist".format(audio_path))
return librosa.load(audio_path, sr=None, mono=True)
[docs]def download(data_home=None, force_overwrite=False, cleanup=True):
"""Download the giantsteps_tempo Dataset (annotations).
The audio files are not provided due to copyright issues.
Args:
data_home (str):
Local path where the dataset is stored.
If `None`, looks for the data in the default directory, `~/mir_datasets`
force_overwrite (bool):
Whether to overwrite the existing downloaded data
cleanup (bool):
Whether to delete the zip/tar file after extracting.
partial_download(list of str)
arguments can be 'audio' 'metadata' or/and 'tempos'
"""
# use the default location: ~/mir_datasets/giantsteps_tempo
if data_home is None:
data_home = utils.get_default_dataset_path(DATASET_DIR)
download_message = """
Unfortunately the audio files of the Giant Steps Tempo dataset are not available
for download. If you have the Giant Steps audio dataset, place the contents into
a folder called GiantSteps_tempo with the following structure:
> GiantSteps_tempo/
> giantsteps-tempo-dataset-0b7d47ba8cae59d3535a02e3db69e2cf6d0af5bb/
> audio/
and copy the folder to {}
""".format(
data_home
)
download_utils.downloader(
data_home,
remotes=REMOTES,
info_message=download_message,
force_overwrite=force_overwrite,
cleanup=cleanup,
)
[docs]def validate(data_home=None, silence=False):
"""Validate if a local version of this dataset is consistent
Args:
data_home (str): Local path where the dataset is stored.
If `None`, looks for the data in the default directory, `~/mir_datasets`
Returns:
missing_files (list): List of file paths that are in the dataset index
but missing locally
invalid_checksums (list): List of file paths where the expected file exists locally
but has a different checksum than the reference
"""
if data_home is None:
data_home = utils.get_default_dataset_path(DATASET_DIR)
missing_files, invalid_checksums = utils.validator(
DATA.index, data_home, silence=silence
)
return missing_files, invalid_checksums
[docs]def track_ids():
"""Get the list of track IDs for this dataset
Returns:
(list): A list of track ids
"""
return list(DATA.index.keys())
[docs]def load(data_home=None):
"""Load giantsteps_tempo dataset
Args:
data_home (str): Local path where the dataset is stored.
If `None`, looks for the data in the default directory, `~/mir_datasets`
Returns:
(dict): {`track_id`: track data}
"""
if data_home is None:
data_home = utils.get_default_dataset_path(DATASET_DIR)
giantsteps_key_data = {}
for tempo in track_ids():
giantsteps_key_data[tempo] = Track(tempo, data_home=data_home)
return giantsteps_key_data
[docs]def load_genre(path):
"""Load genre data from a file
Args:
path (str): path to metadata annotation file
Returns:
(str): loaded genre data
"""
if path is None:
return None
with open(path) as json_file:
annotation = jams.load(json_file)
return annotation.search(namespace='tag_open')[0]['data'][0].value
[docs]def load_tempo(tempo_path):
"""Load giantsteps_tempo tempo data from a file ordered by confidence
Args:
tempo_path (str): path to tempo annotation file
Returns:
(list of utils.TempoData): loaded tempo data
"""
if tempo_path is None:
return None
if not os.path.exists(tempo_path):
raise IOError("tempo_path {} does not exist".format(tempo_path))
with open(tempo_path) as json_file:
annotation = jams.load(json_file)
tempo = annotation.search(namespace='tempo')[0]['data']
return utils.TempoData(
np.array([t.time for t in tempo]),
np.array([t.duration for t in tempo]),
np.array([t.value for t in tempo]),
np.array([t.confidence for t in tempo]),
)
[docs]def cite():
"""Print the reference"""
cite_data = """
=========== MLA ===========
Peter Knees, Ángel Faraldo, Perfecto Herrera, Richard Vogl,
Sebastian Böck, Florian Hörschläger, Mickael Le Goff: "Two data
sets for tempo estimation and key detection in electronic dance
music annotated from user corrections," Proc. of the 16th
Conference of the International Society for Music Information
Retrieval (ISMIR'15), Oct. 2015, Malaga, Spain.
========== Bibtex ==========
@inproceedings{knees2015two,
title={Two data sets for tempo estimation and key detection in electronic dance music annotated from user corrections},
author={Knees, Peter and Faraldo P{\'e}rez, {\'A}ngel and Boyer, Herrera and Vogl, Richard and B{\"o}ck, Sebastian and H{\"o}rschl{\"a}ger, Florian and Le Goff, Mickael and others},
booktitle={Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR); 2015 Oct 26-30; M{\'a}laga, Spain.[M{\'a}laga]: International Society for Music Information Retrieval, 2015. p. 364-70.},
year={2015},
organization={International Society for Music Information Retrieval (ISMIR)}
}
=========== MLA ===========
Hendrik Schreiber, Meinard Müller: "A Crowdsourced Experiment
for Tempo Estimation of Electronic Dance Music", Proc. of the
19th Conference of the International Society for Music
Information Retrieval (ISMIR'18), Sept. 2018, Paris, France.
========== Bibtex ==========
@inproceedings{SchreiberM18a_Tempo_ISMIR,
author = {Hendrik Schreiber and Meinard M{\"u}ller},
title = {A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music},
booktitle = {Proceedings of the International Conference on Music Information Retrieval ({ISMIR})},
address = {Paris, France},
year = {2018},
url-pdf = {http://www.tagtraum.com/download/2018_schreiber_tempo_giantsteps.pdf}
}
"""
print(cite_data)