# -*- coding: utf-8 -*-
"""Beatles Dataset Loader
.. admonition:: Dataset Info
:class: dropdown
The Beatles Dataset includes beat and metric position, chord, key, and segmentation
annotations for 179 Beatles songs. Details can be found in http://matthiasmauch.net/_pdf/mauch_omp_2009.pdf and
http://isophonics.net/content/reference-annotations-beatles.
"""
import csv
import os
from typing import BinaryIO, Optional, TextIO, Tuple
import librosa
import numpy as np
from mirdata import download_utils
from mirdata import jams_utils
from mirdata import core
from mirdata import annotations
from mirdata import io
DATA = core.LargeData("beatles_index.json")
BIBTEX = """@inproceedings{mauch2009beatles,
title={OMRAS2 metadata project 2009},
author={Mauch, Matthias and Cannam, Chris and Davies, Matthew and Dixon, Simon and Harte,
Christopher and Kolozali, Sefki and Tidhar, Dan and Sandler, Mark},
booktitle={12th International Society for Music Information Retrieval Conference},
year={2009},
series = {ISMIR}
}"""
REMOTES = {
"annotations": download_utils.RemoteFileMetadata(
filename="The Beatles Annotations.tar.gz",
url="http://isophonics.net/files/annotations/The%20Beatles%20Annotations.tar.gz",
checksum="62425c552d37c6bb655a78e4603828cc",
destination_dir="annotations",
)
}
DOWNLOAD_INFO = """
Unfortunately the audio files of the Beatles dataset are not available
for download. If you have the Beatles dataset, place the contents into
a folder called Beatles with the following structure:
> Beatles/
> annotations/
> audio/
and copy the Beatles folder to {}
"""
LICENSE_INFO = (
"Unfortunately we couldn't find the license information for the Beatles dataset."
)
[docs]class Track(core.Track):
"""Beatles track class
Args:
track_id (str): track id of the track
data_home (str): path where the data lives
Attributes:
audio_path (str): track audio path
beats_path (str): beat annotation path
chords_path (str): chord annotation path
keys_path (str): key annotation path
sections_path (str): sections annotation path
title (str): title of the track
track_id (str): track id
Cached Properties:
beats (BeatData): human-labeled beat annotations
chords (ChordData): human-labeled chord annotations
key (KeyData): local key annotations
sections (SectionData): section annotations
"""
def __init__(
self,
track_id,
data_home,
dataset_name,
index,
metadata,
):
super().__init__(
track_id,
data_home,
dataset_name,
index,
metadata,
)
self.beats_path = core.none_path_join(
[self._data_home, self._track_paths["beat"][0]]
)
self.chords_path = os.path.join(self._data_home, self._track_paths["chords"][0])
self.keys_path = core.none_path_join(
[self._data_home, self._track_paths["keys"][0]]
)
self.sections_path = os.path.join(
self._data_home, self._track_paths["sections"][0]
)
self.audio_path = os.path.join(self._data_home, self._track_paths["audio"][0])
self.title = os.path.basename(self._track_paths["sections"][0]).split(".")[0]
@core.cached_property
def beats(self) -> Optional[annotations.BeatData]:
return load_beats(self.beats_path)
@core.cached_property
def chords(self) -> Optional[annotations.ChordData]:
return load_chords(self.chords_path)
@core.cached_property
def key(self) -> Optional[annotations.KeyData]:
return load_key(self.keys_path)
@core.cached_property
def sections(self) -> Optional[annotations.SectionData]:
return load_sections(self.sections_path)
@property
def audio(self) -> Optional[Tuple[np.ndarray, float]]:
"""The track's audio
Returns:
* np.ndarray - audio signal
* float - sample rate
"""
return load_audio(self.audio_path)
[docs] def to_jams(self):
"""the track's data in jams format
Returns:
jams.JAMS: return track data in jam format
"""
return jams_utils.jams_converter(
audio_path=self.audio_path,
beat_data=[(self.beats, None)],
section_data=[(self.sections, None)],
chord_data=[(self.chords, None)],
key_data=[(self.key, None)],
metadata={"artist": "The Beatles", "title": self.title},
)
[docs]@io.coerce_to_bytes_io
def load_audio(fhandle: BinaryIO) -> Tuple[np.ndarray, float]:
"""Load a Beatles audio file.
Args:
fhandle (str or file-like): path or file-like object pointing to an audio file
Returns:
* np.ndarray - the mono audio signal
* float - The sample rate of the audio file
"""
return librosa.load(fhandle, sr=None, mono=True)
[docs]@io.coerce_to_string_io
def load_beats(fhandle: TextIO) -> annotations.BeatData:
"""Load Beatles format beat data from a file
Args:
fhandle (str or file-like): path or file-like object pointing to a beat annotation file
Returns:
BeatData: loaded beat data
"""
beat_times, beat_positions = [], []
dialect = csv.Sniffer().sniff(fhandle.read(1024))
fhandle.seek(0)
reader = csv.reader(fhandle, dialect)
for line in reader:
beat_times.append(float(line[0]))
beat_positions.append(line[-1])
beat_positions = _fix_newpoint(np.array(beat_positions))
# After fixing New Point labels convert positions to int
beat_data = annotations.BeatData(
np.array(beat_times), np.array([int(b) for b in beat_positions])
)
return beat_data
[docs]@io.coerce_to_string_io
def load_chords(fhandle: TextIO) -> annotations.ChordData:
"""Load Beatles format chord data from a file
Args:
fhandle (str or file-like): path or file-like object pointing to a chord annotation file
Returns:
ChordData: loaded chord data
"""
start_times, end_times, chords = [], [], []
dialect = csv.Sniffer().sniff(fhandle.read(1024))
fhandle.seek(0)
reader = csv.reader(fhandle, dialect)
for line in reader:
start_times.append(float(line[0]))
end_times.append(float(line[1]))
chords.append(line[2])
return annotations.ChordData(np.array([start_times, end_times]).T, chords)
[docs]@io.coerce_to_string_io
def load_key(fhandle: TextIO) -> annotations.KeyData:
"""Load Beatles format key data from a file
Args:
fhandle (str or file-like): path or file-like object pointing to a key annotation file
Returns:
KeyData: loaded key data
"""
start_times, end_times, keys = [], [], []
reader = csv.reader(fhandle, delimiter="\t")
for line in reader:
if line[2] == "Key":
start_times.append(float(line[0]))
end_times.append(float(line[1]))
keys.append(line[3])
return annotations.KeyData(np.array([start_times, end_times]).T, keys)
[docs]@io.coerce_to_string_io
def load_sections(fhandle: TextIO) -> annotations.SectionData:
"""Load Beatles format section data from a file
Args:
fhandle (str or file-like): path or file-like object pointing to a section annotation file
Returns:
SectionData: loaded section data
"""
start_times, end_times, sections = [], [], []
reader = csv.reader(fhandle, delimiter="\t")
for line in reader:
start_times.append(float(line[0]))
end_times.append(float(line[1]))
sections.append(line[3])
return annotations.SectionData(np.array([start_times, end_times]).T, sections)
def _fix_newpoint(beat_positions: np.ndarray) -> np.ndarray:
"""Fills in missing beat position labels by inferring the beat position
from neighboring beats.
"""
while np.any(beat_positions == "New Point"):
idxs = np.where(beat_positions == "New Point")[0]
for i in idxs:
if i < len(beat_positions) - 1:
if not beat_positions[i + 1] == "New Point":
beat_positions[i] = str(np.mod(int(beat_positions[i + 1]) - 1, 4))
if i == len(beat_positions) - 1:
if not beat_positions[i - 1] == "New Point":
beat_positions[i] = str(np.mod(int(beat_positions[i - 1]) + 1, 4))
beat_positions[beat_positions == "0"] = "4"
return beat_positions
[docs]@core.docstring_inherit(core.Dataset)
class Dataset(core.Dataset):
"""
The beatles dataset
"""
def __init__(self, data_home=None):
super().__init__(
data_home,
index=DATA.index,
name="beatles",
track_class=Track,
bibtex=BIBTEX,
remotes=REMOTES,
download_info=DOWNLOAD_INFO,
license_info=LICENSE_INFO,
)
[docs] @core.copy_docs(load_audio)
def load_audio(self, *args, **kwargs):
return load_audio(*args, **kwargs)
[docs] @core.copy_docs(load_beats)
def load_beats(self, *args, **kwargs):
return load_beats(*args, **kwargs)
[docs] @core.copy_docs(load_chords)
def load_chords(self, *args, **kwargs):
return load_chords(*args, **kwargs)
[docs] @core.copy_docs(load_sections)
def load_sections(self, *args, **kwargs):
return load_sections(*args, **kwargs)