# -*- coding: utf-8 -*-
"""Medley-solos-DB Dataset Loader.
.. admonition:: Dataset Info
:class: dropdown
Medley-solos-DB is a cross-collection dataset for automatic musical instrument
recognition in solo recordings. It consists of a training set of 3-second audio
clips, which are extracted from the MedleyDB dataset (Bittner et al., ISMIR 2014)
as well as a test set of 3-second clips, which are extracted from the solosDB
dataset (Essid et al., IEEE TASLP 2009).
Each of these clips contains a single instrument among a taxonomy of eight:
0. clarinet,
1. distorted electric guitar,
2. female singer,
3. flute,
4. piano,
5. tenor saxophone,
6. trumpet, and
7. violin.
The Medley-solos-DB dataset is the dataset that is used in the benchmarks of
musical instrument recognition in the publications of Lostanlen and Cella
(ISMIR 2016) and Andén et al. (IEEE TSP 2019).
"""
import csv
import librosa
import logging
import os
from mirdata import download_utils
from mirdata import jams_utils
from mirdata import core
BIBTEX = """@inproceedings{lostanlen2019ismir,
title={Deep Convolutional Networks in the Pitch Spiral for Musical Instrument Recognition},
author={Lostanlen, Vincent and Cella, Carmine Emanuele},
booktitle={International Society of Music Information Retrieval (ISMIR)},
year={2016}
}"""
REMOTES = {
"annotations": download_utils.RemoteFileMetadata(
filename="Medley-solos-DB_metadata.csv",
url="https://zenodo.org/record/3464194/files/Medley-solos-DB_metadata.csv?download=1",
checksum="fda6a589c56785f2195c9227809c521a",
destination_dir="annotation",
),
"audio": download_utils.RemoteFileMetadata(
filename="Medley-solos-DB.tar.gz",
url="https://zenodo.org/record/3464194/files/Medley-solos-DB.tar.gz?download=1",
checksum="f5facf398793ef5c1f80c013afdf3e5f",
destination_dir="audio",
),
}
LICENSE_INFO = "Creative Commons Attribution 4.0 International."
def _load_metadata(data_home):
metadata_path = os.path.join(
data_home, "annotation", "Medley-solos-DB_metadata.csv"
)
if not os.path.exists(metadata_path):
logging.info("Metadata file {} not found.".format(metadata_path))
return None
metadata_index = {}
with open(metadata_path, "r") as fhandle:
csv_reader = csv.reader(fhandle, delimiter=",")
next(csv_reader)
for row in csv_reader:
subset, instrument_str, instrument_id, song_id, track_id = row
metadata_index[str(track_id)] = {
"subset": str(subset),
"instrument": str(instrument_str),
"instrument_id": int(instrument_id),
"song_id": int(song_id),
}
metadata_index["data_home"] = data_home
return metadata_index
DATA = core.LargeData("medley_solos_db_index.json", _load_metadata)
[docs]class Track(core.Track):
"""medley_solos_db Track class
Args:
track_id (str): track id of the track
Attributes:
audio_path (str): path to the track's audio file
instrument (str): instrument encoded by its English name
instrument_id (int): instrument encoded as an integer
song_id (int): song encoded as an integer
subset (str): either equal to 'train', 'validation', or 'test'
track_id (str): track id
"""
def __init__(self, track_id, data_home):
if track_id not in DATA.index["tracks"]:
raise ValueError(
"{} is not a valid track ID in Medley-solos-DB".format(track_id)
)
self.track_id = track_id
self._data_home = data_home
self._track_paths = DATA.index["tracks"][track_id]
metadata = DATA.metadata(data_home)
if metadata is not None and track_id in metadata:
self._track_metadata = metadata[track_id]
else:
self._track_metadata = {
"instrument": None,
"instrument_id": None,
"song_id": None,
"subset": None,
"track_id": None,
}
self.audio_path = os.path.join(self._data_home, self._track_paths["audio"][0])
self.instrument = self._track_metadata["instrument"]
self.instrument_id = self._track_metadata["instrument_id"]
self.song_id = self._track_metadata["song_id"]
self.subset = self._track_metadata["subset"]
@property
def audio(self):
"""The track's audio
Returns:
* np.ndarray - audio signal
* float - sample rate
"""
return load_audio(self.audio_path)
[docs] def to_jams(self):
"""Get the track's data in jams format
Returns:
jams.JAMS: the track's data in jams format
"""
return jams_utils.jams_converter(
audio_path=self.audio_path, metadata=self._track_metadata
)
[docs]def load_audio(audio_path):
"""Load a Medley Solos DB audio file.
Args:
audio_path (str): path to audio file
Returns:
* np.ndarray - the mono audio signal
* 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=22050, mono=True)
[docs]@core.docstring_inherit(core.Dataset)
class Dataset(core.Dataset):
"""
The medley_solos_db dataset
"""
def __init__(self, data_home=None):
super().__init__(
data_home,
index=DATA.index,
name="medley_solos_db",
track_object=Track,
bibtex=BIBTEX,
remotes=REMOTES,
license_info=LICENSE_INFO,
)
[docs] @core.copy_docs(load_audio)
def load_audio(self, *args, **kwargs):
return load_audio(*args, **kwargs)