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c7e47b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Utilities to navigate MedleyDB.
"""
from __future__ import print_function
from . import multitrack as M
from . import TRACK_LIST_V1
from . import TRACK_LIST_V2
from . import TRACK_LIST_EXTRA
from . import TRACK_LIST_BACH10
from . import ARTIST_INDEX
import numpy as np
from sklearn.model_selection import GroupShuffleSplit
def load_melody_multitracks(dataset_version=None):
"""Load all multitracks that have melody annotations.
Returns
-------
melody_multitracks : list
List of multitrack objects.
dataset_version : list or None, default=None
List of dataset version ids. If None, uses version 1.
Examples
--------
>>> melody_multitracks = load_melody_multitracks()
>>> multitracks = load_melody_multitracks(dataset_version=['V2'])
"""
multitracks = load_all_multitracks(dataset_version=dataset_version)
for track in multitracks:
if track.has_melody:
yield track
def load_all_multitracks(dataset_version=None):
"""Load all multitracks in MEDLEYDB_PATH.
Parameters
----------
dataset_version : list or None, default=None
List of dataset version ids. If None, uses version 1.
Returns
-------
multitracks : list
List of multitrack objects.
Examples
--------
>>> multitracks = load_all_multitracks()
>>> multitracks = load_all_multitracks(dataset_version=['V1', 'V2'])
"""
if dataset_version is None:
dataset_version = ['V1']
track_list = []
if 'V1' in dataset_version:
track_list.extend(TRACK_LIST_V1)
if 'V2' in dataset_version:
track_list.extend(TRACK_LIST_V2)
if 'EXTRA' in dataset_version:
track_list.extend(TRACK_LIST_EXTRA)
if 'BACH10' in dataset_version:
track_list.extend(TRACK_LIST_BACH10)
multitracks = load_multitracks(track_list)
return multitracks
def load_multitracks(track_list):
"""Load a list of multitracks.
Parameters
----------
track_list : list
List of track ids in format 'Artist_Title'
Returns
-------
multitracks : dict
List of multitrack objects.
Examples
--------
>>> track_list = ['ArtistName1_TrackName1', \
'ArtistName2_TrackName2', \
'ArtistName3_TrackName3']
>>> multitracks = load_multitracks(track_list)
"""
for track_id in track_list:
yield M.MultiTrack(track_id)
def get_files_for_instrument(instrument, multitrack_list=None):
"""Get all (stem) files for a particular instrument from the dataset.
Parameters
----------
instrument : str
Instrument files to extract.
multitrack_list : list of MultiTrack objects or None, default=None
List of MultiTrack objects.
If None, uses all multitracks.
Returns
-------
inst_list : list
List of filepaths corresponding to instrument label.
Examples
--------
# load drum set files from the full dataset:
>>> drumset_files = get_files_for_instrument('drum set')
# load violin files from a subset of the dataset:
>>> track_list = ['ArtistName1_TrackName1', \
'ArtistName2_TrackName2', \
'ArtistName3_TrackName3']
>>> multitrack_subset = load_multitracks(track_list)
>>> violin_files = get_files_for_instrument(
'violin', multitrack_subset
)
"""
if not M.is_valid_instrument(instrument):
raise ValueError("%s is not in the instrument taxonomy." % instrument)
if not multitrack_list:
multitrack_list = load_all_multitracks()
for multitrack in multitrack_list:
for stem in multitrack.stems.values():
if instrument in stem.instrument:
yield stem.audio_path
def artist_conditional_split(trackid_list=None, test_size=0.15, num_splits=5,
random_state=None, artist_index=None):
"""Create artist-conditional train-test splits.
The same artist (as defined by the artist_index) cannot appear
in both the training and testing set.
Parameters
----------
trackid_list : list or None, default=None
List of trackids to use in train-test split. If None, uses all tracks
test_size : float, default=0.15
Fraction of tracks to use in test set. The test set will be as close
as possible in size to this value, but it may not be exact due to the
artist-conditional constraint.
num_splits : int, default=5
Number of random splits to create
random_state : int or None, default=None
A random state to optionally reproduce the same random split.
artist_index : dict or None, default=None
Dictionary mapping each track id in trackid_list to a string that
uniquely identifies each artist.
If None, uses the predefined index ARTIST_INDEX.
Returns
-------
splits : list of dicts
List of length num_splits of train/test split dictionaries. Each
dictionary has the keys 'train' and 'test', each which map to lists of
trackids.
"""
if trackid_list is None:
trackid_list = TRACK_LIST_V1
if artist_index is None:
artist_index = ARTIST_INDEX
artists = np.asarray([ARTIST_INDEX[trackid] for trackid in trackid_list])
splitter = GroupShuffleSplit(n_splits=num_splits,
random_state=random_state,
test_size=test_size)
trackid_array = np.array(trackid_list)
splits = []
for train, test in splitter.split(trackid_array, groups=artists):
splits.append({
'train': list(trackid_array[train]),
'test': list(trackid_array[test])
})
return splits
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