Source code for mirdata.datasets.salami

"""SALAMI Dataset Loader

.. admonition:: Dataset Info
    :class: dropdown

    The SALAMI dataset contains Structural Annotations of a Large Amount of Music
    Information: the public portion contains over 2200 annotations of over 1300
    unique tracks.

    NB: mirdata relies on the **corrected** version of the 2.0 annotations:
    Details can be found at and

    For more details, please visit:

import csv
import logging
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

BIBTEX = """@inproceedings{smith2011salami,
    title={Design and creation of a large-scale database of structural annotations.},
    author={Smith, Jordan Bennett Louis and Burgoyne, John Ashley and
          Fujinaga, Ichiro and De Roure, David and Downie, J Stephen},
    booktitle={12th International Society for Music Information Retrieval Conference},
    series = {ISMIR},
    "annotations": download_utils.RemoteFileMetadata(
    Unfortunately the audio files of the Salami dataset are not available
    for download. If you have the Salami dataset, place the contents into a
    folder called Salami with the following structure:
        > Salami/
            > salami-data-public-hierarchy-corrections/
            > audio/
    and copy the Salami folder to {}

This data is released under a Creative Commons 0 license, effectively dedicating it to
the public domain. More information about this dedication and your rights, please see the
details here: and

[docs]class Track(core.Track): """salami Track class Args: track_id (str): track id of the track Attributes: annotator_1_id (str): number that identifies annotator 1 annotator_1_time (str): time that the annotator 1 took to complete the annotation annotator_2_id (str): number that identifies annotator 1 annotator_2_time (str): time that the annotator 1 took to complete the annotation artist (str): song artist audio_path (str): path to the audio file broad_genre (str): broad genre of the song duration (float): duration of song in seconds genre (str): genre of the song sections_annotator1_lowercase_path (str): path to annotations in hierarchy level 1 from annotator 1 sections_annotator1_uppercase_path (str): path to annotations in hierarchy level 0 from annotator 1 sections_annotator2_lowercase_path (str): path to annotations in hierarchy level 1 from annotator 2 sections_annotator2_uppercase_path (str): path to annotations in hierarchy level 0 from annotator 2 source (str): dataset or source of song title (str): title of the song Cached Properties: sections_annotator_1_uppercase (SectionData): annotations in hierarchy level 0 from annotator 1 sections_annotator_1_lowercase (SectionData): annotations in hierarchy level 1 from annotator 1 sections_annotator_2_uppercase (SectionData): annotations in hierarchy level 0 from annotator 2 sections_annotator_2_lowercase (SectionData): annotations in hierarchy level 1 from annotator 2 """ def __init__( self, track_id, data_home, dataset_name, index, metadata, ): super().__init__( track_id, data_home, dataset_name, index, metadata, ) self.sections_annotator1_uppercase_path = self.get_path("annotator_1_uppercase") self.sections_annotator1_lowercase_path = self.get_path("annotator_1_lowercase") self.sections_annotator2_uppercase_path = self.get_path("annotator_2_uppercase") self.sections_annotator2_lowercase_path = self.get_path("annotator_2_lowercase") self.audio_path = self.get_path("audio") @property def source(self): return self._track_metadata.get("source") @property def annotator_1_id(self): return self._track_metadata.get("annotator_1_id") @property def annotator_2_id(self): return self._track_metadata.get("annotator_2_id") @property def duration(self): return self._track_metadata.get("duration") @property def title(self): return self._track_metadata.get("title") @property def artist(self): return self._track_metadata.get("artist") @property def annotator_1_time(self): return self._track_metadata.get("annotator_1_time") @property def annotator_2_time(self): return self._track_metadata.get("annotator_2_time") @property def broad_genre(self): return self._track_metadata.get("class") @property def genre(self): return self._track_metadata.get("genre") @core.cached_property def sections_annotator_1_uppercase(self) -> Optional[annotations.SectionData]: return load_sections(self.sections_annotator1_uppercase_path) @core.cached_property def sections_annotator_1_lowercase(self) -> Optional[annotations.SectionData]: return load_sections(self.sections_annotator1_lowercase_path) @core.cached_property def sections_annotator_2_uppercase(self) -> Optional[annotations.SectionData]: return load_sections(self.sections_annotator2_uppercase_path) @core.cached_property def sections_annotator_2_lowercase(self) -> Optional[annotations.SectionData]: return load_sections(self.sections_annotator2_lowercase_path) @property def audio(self) -> 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): """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, multi_section_data=[ ( [ (self.sections_annotator_1_uppercase, 0), (self.sections_annotator_1_lowercase, 1), ], "annotator_1", ), ( [ (self.sections_annotator_2_uppercase, 0), (self.sections_annotator_2_lowercase, 1), ], "annotator_2", ), ], metadata=self._track_metadata, )
[docs]def load_audio(fhandle: str) -> Tuple[np.ndarray, float]: """Load a Salami audio file. Args: fhandle (str or file-like): path to 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_sections(fhandle: TextIO) -> annotations.SectionData: """Load salami sections data from a file Args: fhandle (str or file-like): File-like object or path to sectin annotation file Returns: SectionData: section data """ times = [] secs = [] reader = csv.reader(fhandle, delimiter="\t") for line in reader: times.append(float(line[0])) secs.append(line[1]) times = np.array(times) # type: ignore secs = np.array(secs) # type: ignore # remove sections with length == 0 times_revised = np.delete(times, np.where(np.diff(times) == 0)) secs_revised = np.delete(secs, np.where(np.diff(times) == 0)) return annotations.SectionData( np.array([times_revised[:-1], times_revised[1:]]).T, list(secs_revised[:-1]) )
[docs]@core.docstring_inherit(core.Dataset) class Dataset(core.Dataset): """ The salami dataset """ def __init__(self, data_home=None): super().__init__( data_home, name="salami", track_class=Track, bibtex=BIBTEX, remotes=REMOTES, download_info=DOWNLOAD_INFO, license_info=LICENSE_INFO, ) @core.cached_property def _metadata(self): metadata_path = os.path.join( self.data_home, os.path.join( "salami-data-public-hierarchy-corrections", "metadata", "metadata.csv" ), ) if not os.path.exists(metadata_path): raise FileNotFoundError("Metadata not found. Did you run .download()?") with open(metadata_path, "r") as fhandle: reader = csv.reader(fhandle, delimiter=",") raw_data = [] for line in reader: if line != []: if line[0] == "SONG_ID": continue raw_data.append(line) metadata_index = {} for line in raw_data: track_id = line[0] duration = None if line[5] != "": duration = float(line[5]) metadata_index[track_id] = { "source": line[1], "annotator_1_id": line[2], "annotator_2_id": line[3], "duration": duration, "title": line[7], "artist": line[8], "annotator_1_time": line[10], "annotator_2_time": line[11], "class": line[14], "genre": line[15], } return metadata_index
[docs] @core.copy_docs(load_audio) def load_audio(self, *args, **kwargs): return load_audio(*args, **kwargs)
[docs] @core.copy_docs(load_sections) def load_sections(self, *args, **kwargs): return load_sections(*args, **kwargs)