"""Core mirdata classes
"""
import json
import os
import random
import types
from typing import Any
import numpy as np
from mirdata import download_utils
from mirdata import validate
MAX_STR_LEN = 100
DOCS_URL = "https://mirdata.readthedocs.io/en/stable/source/mirdata.html"
DISCLAIMER = """
******************************************************************************************
DISCLAIMER: mirdata is a software package with its own license which is independent from
this dataset's license. We don not take responsibility for possible inaccuracies in the
license information provided in mirdata. It is the user's responsibility to be informed
and respect the dataset's license.
******************************************************************************************
"""
##### decorators ######
[docs]class cached_property(object):
"""Cached propery decorator
A property that is only computed once per instance and then replaces
itself with an ordinary attribute. Deleting the attribute resets the
property.
Source: https://github.com/bottlepy/bottle/commit/fa7733e075da0d790d809aa3d2f53071897e6f76
"""
def __init__(self, func):
self.__doc__ = getattr(func, "__doc__")
self.func = func
def __get__(self, obj: Any, cls: type) -> Any:
if obj is None:
return self
value = obj.__dict__[self.func.__name__] = self.func(obj)
return value
[docs]def docstring_inherit(parent):
"""Decorator function to inherit docstrings from the parent class.
Adds documented Attributes from the parent to the child docs.
"""
def inherit(obj):
spaces = " "
if not str(obj.__doc__).__contains__("Attributes:"):
obj.__doc__ += "\n" + spaces + "Attributes:\n"
obj.__doc__ = str(obj.__doc__).rstrip() + "\n"
for attribute in parent.__doc__.split("Attributes:\n")[-1].lstrip().split("\n"):
obj.__doc__ += spaces * 2 + str(attribute).lstrip().rstrip() + "\n"
return obj
return inherit
[docs]def copy_docs(original):
"""
Decorator function to copy docs from one function to another
"""
def wrapper(target):
target.__doc__ = original.__doc__
return target
return wrapper
##### Core Classes #####
[docs]class Dataset(object):
"""mirdata Dataset class
Attributes:
data_home (str): path where mirdata will look for the dataset
name (str): the identifier of the dataset
bibtex (str or None): dataset citation/s in bibtex format
remotes (dict or None): data to be downloaded
readme (str): information about the dataset
track (function): a function mapping a track_id to a mirdata.core.Track
multitrack (function): a function mapping a mtrack_id to a mirdata.core.Multitrack
"""
[docs] def __init__(
self,
data_home=None,
name=None,
track_class=None,
multitrack_class=None,
bibtex=None,
remotes=None,
download_info=None,
license_info=None,
custom_index_path=None,
):
"""Dataset init method
Args:
data_home (str or None): path where mirdata will look for the dataset
name (str or None): the identifier of the dataset
track_class (mirdata.core.Track or None): a Track class
multitrack_class (mirdata.core.Multitrack or None): a Multitrack class
bibtex (str or None): dataset citation/s in bibtex format
remotes (dict or None): data to be downloaded
download_info (str or None): download instructions or caveats
license_info (str or None): license of the dataset
custom_index_path (str or None): overwrites the default index path for remote indexes
"""
self.name = name
self.data_home = self.default_path if data_home is None else data_home
if custom_index_path:
self.index_path = os.path.join(self.data_home, custom_index_path)
self.remote_index = True
else:
self.index_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"datasets/indexes",
"{}_index.json".format(self.name),
)
self.remote_index = False
self._track_class = track_class
self._multitrack_class = multitrack_class
self.bibtex = bibtex
self.remotes = remotes
self._download_info = download_info
self._license_info = license_info
self.readme = "{}#module-mirdata.datasets.{}".format(DOCS_URL, self.name)
# this is a hack to be able to have dataset-specific docstrings
self.track = lambda track_id: self._track(track_id)
self.track.__doc__ = self._track_class.__doc__ # set the docstring
self.multitrack = lambda mtrack_id: self._multitrack(mtrack_id)
self.multitrack.__doc__ = self._multitrack_class.__doc__ # set the docstring
def __repr__(self):
repr_string = "The {} dataset\n".format(self.name)
repr_string += "-" * MAX_STR_LEN
repr_string += "\n\n\n"
repr_string += "Call the .cite method for bibtex citations.\n"
repr_string += "-" * MAX_STR_LEN
repr_string += "\n\n\n"
if self._track_class is not None:
repr_string += self.track.__doc__
repr_string += "-" * MAX_STR_LEN
repr_string += "\n"
if self._multitrack_class is not None:
repr_string += self.multitrack.__doc__
repr_string += "-" * MAX_STR_LEN
repr_string += "\n"
return repr_string
@cached_property
def _index(self):
if self.remote_index and not os.path.exists(self.index_path):
raise FileNotFoundError(
"This dataset's index is not available locally. You may need to first run .download()"
)
with open(self.index_path) as fhandle:
index = json.load(fhandle)
return index
@cached_property
def _metadata(self):
return None
@property
def default_path(self):
"""Get the default path for the dataset
Returns:
str: Local path to the dataset
"""
mir_datasets_dir = os.path.join(os.getenv("HOME", "/tmp"), "mir_datasets")
return os.path.join(mir_datasets_dir, self.name)
def _track(self, track_id):
"""Load a track by track_id.
Hidden helper function that gets called as a lambda.
Args:
track_id (str): track id of the track
Returns:
Track: a Track object
"""
if self._track_class is None:
raise AttributeError("This dataset does not have tracks")
else:
return self._track_class(
track_id,
self.data_home,
self.name,
self._index,
lambda: self._metadata,
)
def _multitrack(self, mtrack_id):
"""Load a multitrack by mtrack_id.
Hidden helper function that gets called as a lambda.
Args:
mtrack_id (str): mtrack id of the multitrack
Returns:
MultiTrack: an instance of this dataset's MultiTrack object
"""
if self._multitrack_class is None:
raise AttributeError("This dataset does not have multitracks")
else:
return self._multitrack_class(
mtrack_id,
self.data_home,
self.name,
self._index,
self._track_class,
lambda: self._metadata,
)
[docs] def load_tracks(self):
"""Load all tracks in the dataset
Returns:
dict:
{`track_id`: track data}
Raises:
NotImplementedError: If the dataset does not support Tracks
"""
return {track_id: self.track(track_id) for track_id in self.track_ids}
[docs] def load_multitracks(self):
"""Load all multitracks in the dataset
Returns:
dict:
{`mtrack_id`: multitrack data}
Raises:
NotImplementedError: If the dataset does not support Multitracks
"""
return {mtrack_id: self.multitrack(mtrack_id) for mtrack_id in self.mtrack_ids}
[docs] def choice_track(self):
"""Choose a random track
Returns:
Track: a Track object instantiated by a random track_id
"""
return self.track(random.choice(self.track_ids))
[docs] def choice_multitrack(self):
"""Choose a random multitrack
Returns:
Multitrack: a Multitrack object instantiated by a random mtrack_id
"""
return self.multitrack(random.choice(self.mtrack_ids))
[docs] def cite(self):
"""
Print the reference
"""
print("========== BibTeX ==========")
print(self.bibtex)
[docs] def license(self):
"""
Print the license
"""
print("========== License ==========")
print(self._license_info)
print(DISCLAIMER)
[docs] def download(self, partial_download=None, force_overwrite=False, cleanup=False):
"""Download data to `save_dir` and optionally print a message.
Args:
partial_download (list or None):
A list of keys of remotes to partially download.
If None, all data is downloaded
force_overwrite (bool):
If True, existing files are overwritten by the downloaded files.
cleanup (bool):
Whether to delete any zip/tar files after extracting.
Raises:
ValueError: if invalid keys are passed to partial_download
IOError: if a downloaded file's checksum is different from expected
"""
download_utils.downloader(
self.data_home,
remotes=self.remotes,
partial_download=partial_download,
info_message=self._download_info,
force_overwrite=force_overwrite,
cleanup=cleanup,
)
[docs] @cached_property
def track_ids(self):
"""Return track ids
Returns:
list: A list of track ids
"""
if "tracks" not in self._index:
raise AttributeError("This dataset does not have tracks")
return list(self._index["tracks"].keys())
[docs] @cached_property
def mtrack_ids(self):
"""Return track ids
Returns:
list: A list of track ids
"""
if "multitracks" not in self._index:
raise AttributeError("This dataset does not have multitracks")
return list(self._index["multitracks"].keys())
[docs] def validate(self, verbose=True):
"""Validate if the stored dataset is a valid version
Args:
verbose (bool): If False, don't print output
Returns:
* list - files in the index but are missing locally
* list - files which have an invalid checksum
"""
missing_files, invalid_checksums = validate.validator(
self._index, self.data_home, verbose=verbose
)
return missing_files, invalid_checksums
[docs]class Track(object):
"""Track base class
See the docs for each dataset loader's Track class for details
"""
[docs] def __init__(
self,
track_id,
data_home,
dataset_name,
index,
metadata,
):
"""Track init method. Sets boilerplate attributes, including:
- ``track_id``
- ``_dataset_name``
- ``_data_home``
- ``_track_paths``
- ``_track_metadata``
Args:
track_id (str): track id
data_home (str): path where mirdata will look for the dataset
dataset_name (str): the identifier of the dataset
index (dict): the dataset's file index
metadata (function or None): a function returning a dictionary of metadata or None
"""
if track_id not in index["tracks"]:
raise ValueError(
"{} is not a valid track_id in {}".format(track_id, dataset_name)
)
self.track_id = track_id
self._dataset_name = dataset_name
self._data_home = data_home
self._track_paths = index["tracks"][track_id]
self._metadata = metadata
@property
def _track_metadata(self):
metadata = self._metadata()
if metadata and self.track_id in metadata:
return metadata[self.track_id]
elif metadata:
return metadata
raise AttributeError("This Track does not have metadata.")
def __repr__(self):
properties = [v for v in dir(self.__class__) if not v.startswith("_")]
attributes = [
v for v in dir(self) if not v.startswith("_") and v not in properties
]
repr_str = "Track(\n"
for attr in attributes:
val = getattr(self, attr)
if isinstance(val, str):
if len(val) > MAX_STR_LEN:
val = "...{}".format(val[-MAX_STR_LEN:])
val = '"{}"'.format(val)
repr_str += " {}={},\n".format(attr, val)
for prop in properties:
val = getattr(self.__class__, prop)
if isinstance(val, types.FunctionType):
continue
if val.__doc__ is None:
doc = ""
else:
doc = val.__doc__
val_type_str = doc.split(":")[0]
repr_str += " {}: {},\n".format(prop, val_type_str)
repr_str += ")"
return repr_str
def to_jams(self):
raise NotImplementedError
[docs] def get_path(self, key):
"""Get absolute path to track audio and annotations. Returns None if
the path in the index is None
Args:
key (string): Index key of the audio or annotation type
Returns:
str or None: joined path string or None
"""
if self._track_paths[key][0] is None:
return None
else:
return os.path.join(self._data_home, self._track_paths[key][0])
[docs]class MultiTrack(Track):
"""MultiTrack class.
A multitrack class is a collection of track objects and their associated audio
that can be mixed together.
A multitrack is itself a Track, and can have its own associated audio (such as
a mastered mix), its own metadata and its own annotations.
"""
[docs] def __init__(
self,
mtrack_id,
data_home,
dataset_name,
index,
track_class,
metadata,
):
"""Multitrack init method. Sets boilerplate attributes, including:
- ``mtrack_id``
- ``_dataset_name``
- ``_data_home``
- ``_multitrack_paths``
- ``_multitrack_metadata``
Args:
mtrack_id (str): multitrack id
data_home (str): path where mirdata will look for the dataset
dataset_name (str): the identifier of the dataset
index (dict): the dataset's file index
metadata (function or None): a function returning a dictionary of metadata or None
"""
if mtrack_id not in index["multitracks"]:
raise ValueError(
"{} is not a valid mtrack_id in {}".format(mtrack_id, dataset_name)
)
self.mtrack_id = mtrack_id
self._dataset_name = dataset_name
self._data_home = data_home
self._multitrack_paths = index["multitracks"][self.mtrack_id]
self._metadata = metadata
self._track_class = track_class
self._index = index
self.track_ids = self._index["multitracks"][self.mtrack_id]["tracks"]
@property
def tracks(self):
return {
t: self._track_class(
t, self._data_home, self._dataset_name, self._index, self._metadata
)
for t in self.track_ids
}
@property
def track_audio_property(self):
raise NotImplementedError("Mixing is not supported for this dataset")
@property
def _multitrack_metadata(self):
metadata = self._metadata()
if metadata and self.mtrack_id in metadata:
return metadata[self.mtrack_id]
elif metadata:
return metadata
raise AttributeError("This MultiTrack does not have metadata")
[docs] def get_path(self, key):
"""Get absolute path to multitrack audio and annotations. Returns None if
the path in the index is None
Args:
key (string): Index key of the audio or annotation type
Returns:
str or None: joined path string or None
"""
if self._multitrack_paths[key][0] is None:
return None
else:
return os.path.join(self._data_home, self._multitrack_paths[key][0])
[docs] def get_target(self, track_keys, weights=None, average=True, enforce_length=True):
"""Get target which is a linear mixture of tracks
Args:
track_keys (list): list of track keys to mix together
weights (list or None): list of positive scalars to be used in the average
average (bool): if True, computes a weighted average of the tracks
if False, computes a weighted sum of the tracks
enforce_length (bool): If True, raises ValueError if the tracks are
not the same length. If False, pads audio with zeros to match the length
of the longest track
Returns:
np.ndarray: target audio with shape (n_channels, n_samples)
Raises:
ValueError:
if sample rates of the tracks are not equal
if enforce_length=True and lengths are not equal
"""
signals = []
lengths = []
sample_rates = []
for k in track_keys:
audio, sample_rate = getattr(self.tracks[k], self.track_audio_property)
# ensure all signals are shape (n_channels, n_samples)
if len(audio.shape) == 1:
audio = audio[np.newaxis, :]
signals.append(audio)
lengths.append(audio.shape[1])
sample_rates.append(sample_rate)
if len(set(sample_rates)) > 1:
raise ValueError(
"Sample rates for tracks {} are not equal: {}".format(
track_keys, sample_rates
)
)
max_length = np.max(lengths)
if any([l != max_length for l in lengths]):
if enforce_length:
raise ValueError(
"Track's {} audio are not the same length {}. Use enforce_length=False to pad with zeros.".format(
track_keys, lengths
)
)
else:
# pad signals to the max length
signals = [
np.pad(signal, ((0, 0), (0, max_length - signal.shape[1])))
for signal in signals
]
if weights is None:
weights = np.ones((len(track_keys),))
target = np.average(signals, axis=0, weights=weights)
if not average:
target *= np.sum(weights)
return target
[docs] def get_random_target(self, n_tracks=None, min_weight=0.3, max_weight=1.0):
"""Get a random target by combining a random selection of tracks with random weights
Args:
n_tracks (int or None): number of tracks to randomly mix. If None, uses all tracks
min_weight (float): minimum possible weight when mixing
max_weight (float): maximum possible weight when mixing
Returns:
* np.ndarray - mixture audio with shape (n_samples, n_channels)
* list - list of keys of included tracks
* list - list of weights used to mix tracks
"""
tracks = list(self.tracks.keys())
assert len(tracks) > 0
if n_tracks is not None and n_tracks < len(tracks):
tracks = np.random.choice(tracks, n_tracks, replace=False)
weights = np.random.uniform(low=min_weight, high=max_weight, size=len(tracks))
target = self.get_target(tracks, weights=weights)
return target, tracks, weights
[docs] def get_mix(self):
"""Create a linear mixture given a subset of tracks.
Args:
track_keys (list): list of track keys to mix together
Returns:
np.ndarray: mixture audio with shape (n_samples, n_channels)
"""
tracks = list(self.tracks.keys())
assert len(tracks) > 0
return self.get_target(tracks)