Initializing

mirdata.initialize(dataset_name, data_home=None)[source]

Load a mirdata dataset by name

Example

orchset = mirdata.initialize('orchset')  # get the orchset dataset
orchset.download()  # download orchset
orchset.validate()  # validate orchset
track = orchset.choice_track()  # load a random track
print(track)  # see what data a track contains
orchset.track_ids()  # load all track ids
Parameters
  • dataset_name (str) – the dataset’s name see mirdata.DATASETS for a complete list of possibilities

  • data_home (str or None) – path where the data lives. If None uses the default location.

Returns

Dataset – a mirdata.core.Dataset object

mirdata.list_datasets()[source]

Get a list of all mirdata dataset names

Returns

list – list of dataset names as strings

Dataset Loaders

acousticbrainz_genre

Acoustic Brainz Genre dataset

class mirdata.datasets.acousticbrainz_genre.Dataset(data_home=None)[source]

The acousticbrainz genre dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

filter_index(search_key)[source]

Load from AcousticBrainz genre dataset the indexes that match with search_key.

Parameters

search_key (str) – regex to match with folds, mbid or genres

Returns

dict – {track_id: track data}

license()[source]

Print the license

load_all_train()[source]

Load from AcousticBrainz genre dataset the tracks that are used for training across the four different datasets.

Returns

dict – {track_id: track data}

load_all_validation()[source]

Load from AcousticBrainz genre dataset the tracks that are used for validating across the four different datasets.

Returns

dict – {track_id: track data}

load_allmusic_train()[source]

Load from AcousticBrainz genre dataset the tracks that are used for validation in allmusic dataset.

Returns

dict – {track_id: track data}

load_allmusic_validation()[source]

Load from AcousticBrainz genre dataset the tracks that are used for validation in allmusic dataset.

Returns

dict – {track_id: track data}

load_discogs_train()[source]

Load from AcousticBrainz genre dataset the tracks that are used for training in discogs dataset.

Returns

dict – {track_id: track data}

load_discogs_validation()[source]

Load from AcousticBrainz genre dataset the tracks that are used for validation in tagtraum dataset.

Returns

dict – {track_id: track data}

load_extractor(*args, **kwargs)[source]

Load a AcousticBrainz Dataset json file with all the features and metadata.

Parameters

fhandle (str or file-like) – path or file-like object pointing to a json file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_lastfm_train()[source]

Load from AcousticBrainz genre dataset the tracks that are used for training in lastfm dataset.

Returns

dict – {track_id: track data}

load_lastfm_validation()[source]

Load from AcousticBrainz genre dataset the tracks that are used for validation in lastfm dataset.

Returns

dict – {track_id: track data}

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tagtraum_train()[source]

Load from AcousticBrainz genre dataset the tracks that are used for training in tagtraum dataset.

Returns

dict – {track_id: track data}

load_tagtraum_validation()[source]

Load from AcousticBrainz genre dataset the tracks that are used for validating in tagtraum dataset.

Returns

dict – {track_id: track data}

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.acousticbrainz_genre.Track(track_id, data_home, dataset_name, index, metadata)[source]

AcousticBrainz Genre Dataset track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored. If None, looks for the data in the default directory, ~/mir_datasets

Variables
  • track_id (str) – track id

  • genre (list) – human-labeled genre and subgenres list

  • mbid (str) – musicbrainz id

  • mbid_group (str) – musicbrainz id group

  • artist (list) – the track’s artist/s

  • title (list) – the track’s title

  • date (list) – the track’s release date/s

  • filename (str) – the track’s filename

  • album (list) – the track’s album/s

  • track_number (list) – the track number/s

  • tonal (dict) – dictionary of acousticbrainz tonal features

  • low_level (dict) – dictionary of acousticbrainz low-level features

  • rhythm (dict) – dictionary of acousticbrainz rhythm features

Other Parameters

acousticbrainz_metadata (dict) – dictionary of metadata provided by AcousticBrainz

property album

metadata album annotation

Returns

list – album

property artist

metadata artist annotation

Returns

list – artist

property date

metadata date annotation

Returns

list – date

property file_name

metadata file_name annotation

Returns

str – file name

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

property low_level

low_level track descriptors.

Returns

dict

  • ‘average_loudness’: dynamic range descriptor. It rescales average loudness, computed on 2sec windows with 1 sec overlap, into the [0,1] interval. The value of 0 corresponds to signals with large dynamic range, 1 corresponds to signal with little dynamic range. Algorithms: Loudness

  • ’dynamic_complexity’: dynamic complexity computed on 2sec windows with 1sec overlap. Algorithms: DynamicComplexity

  • ’silence_rate_20dB’, ‘silence_rate_30dB’, ‘silence_rate_60dB’: rate of silent frames in a signal for thresholds of 20, 30, and 60 dBs. Algorithms: SilenceRate

  • ’spectral_rms’: spectral RMS. Algorithms: RMS

  • ’spectral_flux’: spectral flux of a signal computed using L2-norm. Algorithms: Flux

  • ’spectral_centroid’, ‘spectral_kurtosis’, ‘spectral_spread’, ‘spectral_skewness’: centroid and central moments statistics describing the spectral shape. Algorithms: Centroid, CentralMoments

  • ’spectral_rolloff’: the roll-off frequency of a spectrum. Algorithms: RollOff

  • ’spectral_decrease’: spectral decrease. Algorithms: Decrease

  • ’hfc’: high frequency content descriptor as proposed by Masri. Algorithms: HFC

  • ’zerocrossingrate’ zero-crossing rate. Algorithms: ZeroCrossingRate

  • ’spectral_energy’: spectral energy. Algorithms: Energy

  • ’spectral_energyband_low’, ‘spectral_energyband_middle_low’, ‘spectral_energyband_middle_high’,

  • ’spectral_energyband_high’: spectral energy in frequency bands [20Hz, 150Hz], [150Hz, 800Hz], [800Hz, 4kHz], and [4kHz, 20kHz]. Algorithms EnergyBand

  • ’barkbands’: spectral energy in 27 Bark bands. Algorithms: BarkBands

  • ’melbands’: spectral energy in 40 mel bands. Algorithms: MFCC

  • ’erbbands’: spectral energy in 40 ERB bands. Algorithms: ERBBands

  • ’mfcc’: the first 13 mel frequency cepstrum coefficients. See algorithm: MFCC

  • ’gfcc’: the first 13 gammatone feature cepstrum coefficients. Algorithms: GFCC

  • ’barkbands_crest’, ‘barkbands_flatness_db’: crest and flatness computed over energies in Bark bands. Algorithms: Crest, FlatnessDB

  • ’barkbands_kurtosis’, ‘barkbands_skewness’, ‘barkbands_spread’: central moments statistics over energies in Bark bands. Algorithms: CentralMoments

  • ’melbands_crest’, ‘melbands_flatness_db’: crest and flatness computed over energies in mel bands. Algorithms: Crest, FlatnessDB

  • ’melbands_kurtosis’, ‘melbands_skewness’, ‘melbands_spread’: central moments statistics over energies in mel bands. Algorithms: CentralMoments

  • ’erbbands_crest’, ‘erbbands_flatness_db’: crest and flatness computed over energies in ERB bands. Algorithms: Crest, FlatnessDB

  • ’erbbands_kurtosis’, ‘erbbands_skewness’, ‘erbbands_spread’: central moments statistics over energies in ERB bands. Algorithms: CentralMoments

  • ’dissonance’: sensory dissonance of a spectrum. Algorithms: Dissonance

  • ’spectral_entropy’: Shannon entropy of a spectrum. Algorithms: Entropy

  • ’pitch_salience’: pitch salience of a spectrum. Algorithms: PitchSalience

  • ’spectral_complexity’: spectral complexity. Algorithms: SpectralComplexity

  • ’spectral_contrast_coeffs’, ‘spectral_contrast_valleys’: spectral contrast features. Algorithms: SpectralContrast

property rhythm

rhythm essentia extractor descriptors

Returns

dict

  • ‘beats_position’: time positions [sec] of detected beats using beat tracking algorithm by Degara et al., 2012. Algorithms: RhythmExtractor2013, BeatTrackerDegara

  • ’beats_count’: number of detected beats

  • ’bpm’: BPM value according to detected beats

  • ’bpm_histogram_first_peak_bpm’, ‘bpm_histogram_first_peak_spread’, ‘bpm_histogram_first_peak_weight’,

  • ’bpm_histogram_second_peak_bpm’, ‘bpm_histogram_second_peak_spread’, ‘bpm_histogram_second_peak_weight’: descriptors characterizing highest and second highest peak of the BPM histogram. Algorithms: BpmHistogramDescriptors

  • ’beats_loudness’, ‘beats_loudness_band_ratio’: spectral energy computed on beats segments of audio across the whole spectrum, and ratios of energy in 6 frequency bands. Algorithms: BeatsLoudness, SingleBeatLoudness

  • ’onset_rate’: number of detected onsets per second. Algorithms: OnsetRate

  • ’danceability’: danceability estimate. Algorithms: Danceability

property title

metadata title annotation

Returns

list – title

to_jams()[source]

the track’s data in jams format

Returns

jams.JAMS – return track data in jam format

property tonal

tonal features

Returns

dict

  • ‘tuning_frequency’: estimated tuning frequency [Hz]. Algorithms: TuningFrequency

  • ’tuning_nontempered_energy_ratio’ and ‘tuning_equal_tempered_deviation’

  • ’hpcp’, ‘thpcp’: 32-dimensional harmonic pitch class profile (HPCP) and its transposed version. Algorithms: HPCP

  • ’hpcp_entropy’: Shannon entropy of a HPCP vector. Algorithms: Entropy

  • ’key_key’, ‘key_scale’: Global key feature. Algorithms: Key

  • ’chords_key’, ‘chords_scale’: Global key extracted from chords detection.

  • ’chords_strength’, ‘chords_histogram’: : strength of estimated chords and normalized histogram of their progression; Algorithms: ChordsDetection, ChordsDescriptors

  • ’chords_changes_rate’, ‘chords_number_rate’: chords change rate in the progression; ratio of different chords from the total number of chords in the progression; Algorithms: ChordsDetection, ChordsDescriptors

property tracknumber

metadata tracknumber annotation

Returns

list – tracknumber

mirdata.datasets.acousticbrainz_genre.load_extractor(fhandle)[source]

Load a AcousticBrainz Dataset json file with all the features and metadata.

Parameters

fhandle (str or file-like) – path or file-like object pointing to a json file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

beatles

Beatles Dataset Loader

class mirdata.datasets.beatles.Dataset(data_home=None)[source]

The beatles dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Beatles audio file.

Parameters

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

load_beats(*args, **kwargs)[source]

Load Beatles format beat data from a file

Parameters

fhandle (str or file-like) – path or file-like object pointing to a beat annotation file

Returns

BeatData – loaded beat data

load_chords(*args, **kwargs)[source]

Load Beatles format chord data from a file

Parameters

fhandle (str or file-like) – path or file-like object pointing to a chord annotation file

Returns

ChordData – loaded chord data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_sections(*args, **kwargs)[source]

Load Beatles format section data from a file

Parameters

fhandle (str or file-like) – path or file-like object pointing to a section annotation file

Returns

SectionData – loaded section data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.beatles.Track(track_id, data_home, dataset_name, index, metadata)[source]

Beatles track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – path where the data lives

Variables
  • 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

Other Parameters
  • beats (BeatData) – human-labeled beat annotations

  • chords (ChordData) – human-labeled chord annotations

  • key (KeyData) – local key annotations

  • sections (SectionData) – section annotations

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

the track’s data in jams format

Returns

jams.JAMS – return track data in jam format

mirdata.datasets.beatles.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Beatles audio file.

Parameters

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

mirdata.datasets.beatles.load_beats(fhandle: TextIO)mirdata.annotations.BeatData[source]

Load Beatles format beat data from a file

Parameters

fhandle (str or file-like) – path or file-like object pointing to a beat annotation file

Returns

BeatData – loaded beat data

mirdata.datasets.beatles.load_chords(fhandle: TextIO)mirdata.annotations.ChordData[source]

Load Beatles format chord data from a file

Parameters

fhandle (str or file-like) – path or file-like object pointing to a chord annotation file

Returns

ChordData – loaded chord data

mirdata.datasets.beatles.load_key(fhandle: TextIO)mirdata.annotations.KeyData[source]

Load Beatles format key data from a file

Parameters

fhandle (str or file-like) – path or file-like object pointing to a key annotation file

Returns

KeyData – loaded key data

mirdata.datasets.beatles.load_sections(fhandle: TextIO)mirdata.annotations.SectionData[source]

Load Beatles format section data from a file

Parameters

fhandle (str or file-like) – path or file-like object pointing to a section annotation file

Returns

SectionData – loaded section data

beatport_key

beatport_key Dataset Loader

class mirdata.datasets.beatport_key.Dataset(data_home=None)[source]

The beatport_key dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download the dataset

Parameters
  • 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

license()[source]

Print the license

load_artist(*args, **kwargs)[source]

Load beatport_key tempo data from a file

Parameters

metadata_path (str) – path to metadata annotation file

Returns

list – list of artists involved in the track.

load_audio(*args, **kwargs)[source]

Load a beatport_key audio file.

Parameters

audio_path (str) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_genre(*args, **kwargs)[source]

Load beatport_key genre data from a file

Parameters

metadata_path (str) – path to metadata annotation file

Returns

dict – with the list with genres [‘genres’] and list with sub-genres [‘sub_genres’]

load_key(*args, **kwargs)[source]

Load beatport_key format key data from a file

Parameters

keys_path (str) – path to key annotation file

Returns

list – list of annotated keys

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tempo(*args, **kwargs)[source]

Load beatport_key tempo data from a file

Parameters

metadata_path (str) – path to metadata annotation file

Returns

str – tempo in beats per minute

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.beatport_key.Track(track_id, data_home, dataset_name, index, metadata)[source]

beatport_key track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored.

Variables
  • audio_path (str) – track audio path

  • keys_path (str) – key annotation path

  • metadata_path (str) – sections annotation path

  • title (str) – title of the track

  • track_id (str) – track id

Other Parameters
  • key (list) – list of annotated musical keys

  • artists (list) – artists involved in the track

  • genre (dict) – genres and subgenres

  • tempo (int) – tempo in beats per minute

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.beatport_key.load_artist(metadata_path)[source]

Load beatport_key tempo data from a file

Parameters

metadata_path (str) – path to metadata annotation file

Returns

list – list of artists involved in the track.

mirdata.datasets.beatport_key.load_audio(audio_path)[source]

Load a beatport_key audio file.

Parameters

audio_path (str) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.beatport_key.load_genre(metadata_path)[source]

Load beatport_key genre data from a file

Parameters

metadata_path (str) – path to metadata annotation file

Returns

dict – with the list with genres [‘genres’] and list with sub-genres [‘sub_genres’]

mirdata.datasets.beatport_key.load_key(keys_path)[source]

Load beatport_key format key data from a file

Parameters

keys_path (str) – path to key annotation file

Returns

list – list of annotated keys

mirdata.datasets.beatport_key.load_tempo(metadata_path)[source]

Load beatport_key tempo data from a file

Parameters

metadata_path (str) – path to metadata annotation file

Returns

str – tempo in beats per minute

billboard

McGill Billboard Dataset Loader

class mirdata.datasets.billboard.Dataset(data_home=None)[source]

The McGill Billboard dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Billboard audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_chords(*args, **kwargs)[source]

Load chords from a Salami LAB file.

Parameters

fhandle (str or file-like) – path to audio file

Returns

ChordData – chord data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_named_sections(*args, **kwargs)[source]

Load name-annotated sections from a Salami LAB file.

Parameters

fpath (str) – path to audio file

Returns

SectionData – section data

load_sections(*args, **kwargs)[source]

Load letter-annotated sections from a Salami LAB file.

Parameters

fpath (str) – path to audio file

Returns

SectionData – section data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.billboard.Track(track_id, data_home, dataset_name, index, metadata)[source]

McGill Billboard Dataset Track class

Parameters

track_id (str) – track id of the track

Variables
  • track_id (str) – the index for the sample entry

  • audio_path (str) – audio path of the track

  • date (chart) – the date of the chart for the entry

  • rank (peak) – the desired rank on that chart

  • rank – the rank of the song actually annotated, which may be up to 2 ranks higher or lower than the target rank

  • title (str) – the title of the song annotated

  • artist (str) – the name of the artist performing the song annotated

  • rank – the highest rank the song annotated ever achieved on the Billboard Hot 100

  • on chart (weeks) – the number of weeks the song annotated spent on the Billboard Hot 100 chart in total

Other Parameters
  • chords_full (ChordData) – HTK-style LAB files for the chord annotations (full)

  • chords_majmin7 (ChordData) – HTK-style LAB files for the chord annotations (majmin7)

  • chords_majmin7inv (ChordData) – HTK-style LAB files for the chord annotations (majmin7inv)

  • chords_majmin (ChordData) – HTK-style LAB files for the chord annotations (majmin)

  • chords_majmininv (ChordData) – HTK-style LAB files for the chord annotations(majmininv)

  • chroma (np.array) – Array containing the non-negative-least-squares chroma vectors

  • tuning (list) – List containing the tuning estimates

  • sections (SectionData) – Letter-annotated section data (A,B,A’)

  • named_sections (SectionData) – Name-annotated section data (intro, verse, chorus)

  • salami_metadata (dict) – Metadata of the Salami LAB file

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

chroma

Non-negative-least-squares (NNLS) chroma vectors from the Chordino Vamp plug-in

Returns

np.ndarray - NNLS chroma vector

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

tuning

Tuning estimates from the Chordino Vamp plug-in

Returns

list - list of of tuning estimates []

mirdata.datasets.billboard.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Billboard audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.billboard.load_chords(fhandle: TextIO)[source]

Load chords from a Salami LAB file.

Parameters

fhandle (str or file-like) – path to audio file

Returns

ChordData – chord data

mirdata.datasets.billboard.load_named_sections(fpath: str)[source]

Load name-annotated sections from a Salami LAB file.

Parameters

fpath (str) – path to audio file

Returns

SectionData – section data

mirdata.datasets.billboard.load_sections(fpath: str)[source]

Load letter-annotated sections from a Salami LAB file.

Parameters

fpath (str) – path to audio file

Returns

SectionData – section data

cante100

cante100 Loader

class mirdata.datasets.cante100.Dataset(data_home=None)[source]

The cante100 dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a cante100 audio file.

Parameters

fhandle (str) – path to an audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_melody(*args, **kwargs)[source]

Load cante100 f0 annotations

Parameters

fhandle (str or file-like) – path or file-like object pointing to melody annotation file

Returns

F0Data – predominant melody

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_notes(*args, **kwargs)[source]

Load note data from the annotation files

Parameters

fhandle (str or file-like) – path or file-like object pointing to a notes annotation file

Returns

NoteData – note annotations

load_spectrogram(*args, **kwargs)[source]

Load a cante100 dataset spectrogram file.

Parameters

fhandle (str or file-like) – path or file-like object pointing to an audio file

Returns

np.ndarray – spectrogram

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.cante100.Track(track_id, data_home, dataset_name, index, metadata)[source]

cante100 track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored. If None, looks for the data in the default directory, ~/mir_datasets/cante100

Variables
  • track_id (str) – track id

  • identifier (str) – musicbrainz id of the track

  • artist (str) – performing artists

  • title (str) – title of the track song

  • release (str) – release where the track can be found

  • duration (str) – duration in seconds of the track

Other Parameters
  • melody (F0Data) – annotated melody

  • notes (NoteData) – annotated notes

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

property spectrogram

spectrogram of The track’s audio

Returns

np.ndarray – spectrogram

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.cante100.load_audio(fhandle: str) → Tuple[numpy.ndarray, float][source]

Load a cante100 audio file.

Parameters

fhandle (str) – path to an audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.cante100.load_melody(fhandle: TextIO) → Optional[mirdata.annotations.F0Data][source]

Load cante100 f0 annotations

Parameters

fhandle (str or file-like) – path or file-like object pointing to melody annotation file

Returns

F0Data – predominant melody

mirdata.datasets.cante100.load_notes(fhandle: TextIO)mirdata.annotations.NoteData[source]

Load note data from the annotation files

Parameters

fhandle (str or file-like) – path or file-like object pointing to a notes annotation file

Returns

NoteData – note annotations

mirdata.datasets.cante100.load_spectrogram(fhandle: TextIO)numpy.ndarray[source]

Load a cante100 dataset spectrogram file.

Parameters

fhandle (str or file-like) – path or file-like object pointing to an audio file

Returns

np.ndarray – spectrogram

compmusic_otmm_makam

OTMM Makam Recognition Dataset Loader

class mirdata.datasets.compmusic_otmm_makam.Dataset(data_home=None)[source]

The compmusic_otmm_makam dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_mb_tags(*args, **kwargs)[source]

Load track metadata

Parameters

fhandle (str or file-like) – path or file-like object pointing to musicbrainz metadata file

Returns

Dict – metadata of the track

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_pitch(*args, **kwargs)[source]

Load pitch

Parameters

fhandle (str or file-like) – path or file-like object pointing to a pitch annotation file

Returns

F0Data – pitch annotation

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.compmusic_otmm_makam.Track(track_id, data_home, dataset_name, index, metadata)[source]

OTMM Makam Track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored. default=None If None, looks for the data in the default directory, ~/mir_datasets

Variables
  • pitch_path (str) – local path where the pitch annotation is stored

  • mb_tags_path (str) – local path where the MusicBrainz tags annotation is stored

Properties:

makam (str): string referring to the makam represented in the track tonic (float): tonic annotation mbid (str): MusicBrainz ID of the track

Other Parameters
  • pitch (F0Data) – pitch annotation

  • mb_tags (dict) – dictionary containing the raw editorial track metadata from MusicBrainz

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.compmusic_otmm_makam.load_mb_tags(fhandle: TextIO) → dict[source]

Load track metadata

Parameters

fhandle (str or file-like) – path or file-like object pointing to musicbrainz metadata file

Returns

Dict – metadata of the track

mirdata.datasets.compmusic_otmm_makam.load_pitch(fhandle: TextIO)mirdata.annotations.F0Data[source]

Load pitch

Parameters

fhandle (str or file-like) – path or file-like object pointing to a pitch annotation file

Returns

F0Data – pitch annotation

dali

DALI Dataset Loader

class mirdata.datasets.dali.Dataset(data_home=None)[source]

The dali dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_annotations_class(*args, **kwargs)[source]

Load full annotations into the DALI class object

Parameters

annotations_path (str) – path to a DALI annotation file

Returns

DALI.annotations – DALI annotations object

load_annotations_granularity(*args, **kwargs)[source]

Load annotations at the specified level of granularity

Parameters
  • annotations_path (str) – path to a DALI annotation file

  • granularity (str) – one of ‘notes’, ‘words’, ‘lines’, ‘paragraphs’

Returns

NoteData for granularity=’notes’ or LyricData otherwise

load_audio(*args, **kwargs)[source]

Load a DALI audio file.

Parameters

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

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.dali.Track(track_id, data_home, dataset_name, index, metadata)[source]

DALI melody Track class

Parameters

track_id (str) – track id of the track

Variables
  • album (str) – the track’s album

  • annotation_path (str) – path to the track’s annotation file

  • artist (str) – the track’s artist

  • audio_path (str) – path to the track’s audio file

  • audio_url (str) – youtube ID

  • dataset_version (int) – dataset annotation version

  • ground_truth (bool) – True if the annotation is verified

  • language (str) – sung language

  • release_date (str) – year the track was released

  • scores_manual (int) – manual score annotations

  • scores_ncc (float) – ncc score annotations

  • title (str) – the track’s title

  • track_id (str) – the unique track id

  • url_working (bool) – True if the youtube url was valid

Other Parameters
  • notes (NoteData) – vocal notes

  • words (LyricData) – word-level lyrics

  • lines (LyricData) – line-level lyrics

  • paragraphs (LyricData) – paragraph-level lyrics

  • annotation-object (DALI.Annotations) – DALI annotation object

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.dali.load_annotations_class(annotations_path)[source]

Load full annotations into the DALI class object

Parameters

annotations_path (str) – path to a DALI annotation file

Returns

DALI.annotations – DALI annotations object

mirdata.datasets.dali.load_annotations_granularity(annotations_path, granularity)[source]

Load annotations at the specified level of granularity

Parameters
  • annotations_path (str) – path to a DALI annotation file

  • granularity (str) – one of ‘notes’, ‘words’, ‘lines’, ‘paragraphs’

Returns

NoteData for granularity=’notes’ or LyricData otherwise

mirdata.datasets.dali.load_audio(fhandle: BinaryIO) → Optional[Tuple[numpy.ndarray, float]][source]

Load a DALI audio file.

Parameters

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

giantsteps_key

giantsteps_key Dataset Loader

class mirdata.datasets.giantsteps_key.Dataset(data_home=None)[source]

The giantsteps_key dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_artist(*args, **kwargs)[source]

Load giantsteps_key tempo data from a file

Parameters

fhandle (str or file-like) – File-like object or path pointing to metadata annotation file

Returns

list – list of artists involved in the track.

load_audio(*args, **kwargs)[source]

Load a giantsteps_key audio file.

Parameters

fhandle (str or file-like) – path pointing to an audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_genre(*args, **kwargs)[source]

Load giantsteps_key genre data from a file

Parameters

fhandle (str or file-like) – File-like object or path pointing to metadata annotation file

Returns

dict{‘genres’: […], ‘subgenres’: […]}

load_key(*args, **kwargs)[source]

Load giantsteps_key format key data from a file

Parameters

fhandle (str or file-like) – File like object or string pointing to key annotation file

Returns

str – loaded key data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tempo(*args, **kwargs)[source]

Load giantsteps_key tempo data from a file

Parameters

fhandle (str or file-like) – File-like object or string pointing to metadata annotation file

Returns

str – loaded tempo data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.giantsteps_key.Track(track_id, data_home, dataset_name, index, metadata)[source]

giantsteps_key track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (str) – track audio path

  • keys_path (str) – key annotation path

  • metadata_path (str) – sections annotation path

  • title (str) – title of the track

  • track_id (str) – track id

Other Parameters
  • key (str) – musical key annotation

  • artists (list) – list of artists involved

  • genres (dict) – genres and subgenres

  • tempo (int) – crowdsourced tempo annotations in beats per minute

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.giantsteps_key.load_artist(fhandle: TextIO) → List[str][source]

Load giantsteps_key tempo data from a file

Parameters

fhandle (str or file-like) – File-like object or path pointing to metadata annotation file

Returns

list – list of artists involved in the track.

mirdata.datasets.giantsteps_key.load_audio(fhandle: str) → Tuple[numpy.ndarray, float][source]

Load a giantsteps_key audio file.

Parameters

fhandle (str or file-like) – path pointing to an audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.giantsteps_key.load_genre(fhandle: TextIO) → Dict[str, List[str]][source]

Load giantsteps_key genre data from a file

Parameters

fhandle (str or file-like) – File-like object or path pointing to metadata annotation file

Returns

dict{‘genres’: […], ‘subgenres’: […]}

mirdata.datasets.giantsteps_key.load_key(fhandle: TextIO) → str[source]

Load giantsteps_key format key data from a file

Parameters

fhandle (str or file-like) – File like object or string pointing to key annotation file

Returns

str – loaded key data

mirdata.datasets.giantsteps_key.load_tempo(fhandle: TextIO) → str[source]

Load giantsteps_key tempo data from a file

Parameters

fhandle (str or file-like) – File-like object or string pointing to metadata annotation file

Returns

str – loaded tempo data

giantsteps_tempo

giantsteps_tempo Dataset Loader

class mirdata.datasets.giantsteps_tempo.Dataset(data_home=None)[source]

The giantsteps_tempo dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a giantsteps_tempo audio file.

Parameters

fhandle (str or file-like) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_genre(*args, **kwargs)[source]

Load genre data from a file

Parameters

path (str) – path to metadata annotation file

Returns

str – loaded genre data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tempo(*args, **kwargs)[source]

Load giantsteps_tempo tempo data from a file ordered by confidence

Parameters

fhandle (str or file-like) – File-like object or path to tempo annotation file

Returns

annotations.TempoData – Tempo data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.giantsteps_tempo.Track(track_id, data_home, dataset_name, index, metadata)[source]

giantsteps_tempo track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (str) – track audio path

  • title (str) – title of the track

  • track_id (str) – track id

  • annotation_v1_path (str) – track annotation v1 path

  • annotation_v2_path (str) – track annotation v2 path

Other Parameters
  • genre (dict) – Human-labeled metadata annotation

  • tempo (list) – List of annotations.TempoData, ordered by confidence

  • tempo_v2 (list) – List of annotations.TempoData for version 2, ordered by confidence

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

to_jams_v2()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.giantsteps_tempo.load_audio(fhandle: str) → Tuple[numpy.ndarray, float][source]

Load a giantsteps_tempo audio file.

Parameters

fhandle (str or file-like) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.giantsteps_tempo.load_genre(fhandle: TextIO) → str[source]

Load genre data from a file

Parameters

path (str) – path to metadata annotation file

Returns

str – loaded genre data

mirdata.datasets.giantsteps_tempo.load_tempo(fhandle: TextIO)mirdata.annotations.TempoData[source]

Load giantsteps_tempo tempo data from a file ordered by confidence

Parameters

fhandle (str or file-like) – File-like object or path to tempo annotation file

Returns

annotations.TempoData – Tempo data

groove_midi

Groove MIDI Loader

class mirdata.datasets.groove_midi.Dataset(data_home=None)[source]

The groove_midi dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Groove MIDI audio file.

Parameters

path – path to an audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_beats(*args, **kwargs)[source]

Load beat data from the midi file.

Parameters
  • midi_path (str) – path to midi file

  • midi (pretty_midi.PrettyMIDI) – pre-loaded midi object or None if None, the midi object is loaded using midi_path

Returns

annotations.BeatData – machine generated beat data

load_drum_events(*args, **kwargs)[source]

Load drum events from the midi file.

Parameters
  • midi_path (str) – path to midi file

  • midi (pretty_midi.PrettyMIDI) – pre-loaded midi object or None if None, the midi object is loaded using midi_path

Returns

annotations.EventData – drum event data

load_midi(*args, **kwargs)[source]

Load a Groove MIDI midi file.

Parameters

fhandle (str or file-like) – File-like object or path to midi file

Returns

midi_data (pretty_midi.PrettyMIDI) – pretty_midi object

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.groove_midi.Track(track_id, data_home, dataset_name, index, metadata)[source]

Groove MIDI Track class

Parameters

track_id (str) – track id of the track

Variables
  • drummer (str) – Drummer id of the track (ex. ‘drummer1’)

  • session (str) – Type of session (ex. ‘session1’, ‘eval_session’)

  • track_id (str) – track id of the track (ex. ‘drummer1/eval_session/1’)

  • style (str) – Style (genre, groove type) of the track (ex. ‘funk/groove1’)

  • tempo (int) – track tempo in beats per minute (ex. 138)

  • beat_type (str) – Whether the track is a beat or a fill (ex. ‘beat’)

  • time_signature (str) – Time signature of the track (ex. ‘4-4’, ‘6-8’)

  • midi_path (str) – Path to the midi file

  • audio_path (str) – Path to the audio file

  • duration (float) – Duration of the midi file in seconds

  • split (str) – Whether the track is for a train/valid/test set. One of ‘train’, ‘valid’ or ‘test’.

Other Parameters
  • beats (BeatData) – Machine-generated beat annotations

  • drum_events (EventData) – Annotated drum kit events

  • midi (pretty_midi.PrettyMIDI) – object containing MIDI information

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.groove_midi.load_audio(path: str) → Tuple[Optional[numpy.ndarray], Optional[float]][source]

Load a Groove MIDI audio file.

Parameters

path – path to an audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.groove_midi.load_beats(midi_path, midi=None)[source]

Load beat data from the midi file.

Parameters
  • midi_path (str) – path to midi file

  • midi (pretty_midi.PrettyMIDI) – pre-loaded midi object or None if None, the midi object is loaded using midi_path

Returns

annotations.BeatData – machine generated beat data

mirdata.datasets.groove_midi.load_drum_events(midi_path, midi=None)[source]

Load drum events from the midi file.

Parameters
  • midi_path (str) – path to midi file

  • midi (pretty_midi.PrettyMIDI) – pre-loaded midi object or None if None, the midi object is loaded using midi_path

Returns

annotations.EventData – drum event data

mirdata.datasets.groove_midi.load_midi(fhandle: BinaryIO) → Optional[pretty_midi.PrettyMIDI][source]

Load a Groove MIDI midi file.

Parameters

fhandle (str or file-like) – File-like object or path to midi file

Returns

midi_data (pretty_midi.PrettyMIDI) – pretty_midi object

gtzan_genre

GTZAN-Genre Dataset Loader

class mirdata.datasets.gtzan_genre.Dataset(data_home=None)[source]

The gtzan_genre dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a GTZAN audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.gtzan_genre.Track(track_id, data_home, dataset_name, index, metadata)[source]

gtzan_genre Track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (str) – path to the audio file

  • genre (str) – annotated genre

  • track_id (str) – track id

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.gtzan_genre.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a GTZAN audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

guitarset

GuitarSet Loader

class mirdata.datasets.guitarset.Dataset(data_home=None)[source]

The guitarset dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Guitarset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_beats(*args, **kwargs)[source]

Load a Guitarset beats annotation.

Parameters

fhandle (str or file-like) – File-like object or path of the jams annotation file

Returns

BeatData – Beat data

load_chords(*args, **kwargs)[source]

Load a guitarset chord annotation.

Parameters
  • jams_path (str) – Path of the jams annotation file

  • leadsheet_version (Bool) – Whether or not to load the leadsheet version of the chord annotation If False, load the infered version.

Returns

ChordData – Chord data

load_key_mode(*args, **kwargs)[source]

Load a Guitarset key-mode annotation.

Parameters

fhandle (str or file-like) – File-like object or path of the jams annotation file

Returns

KeyData – Key data

load_multitrack_audio(*args, **kwargs)[source]

Load a Guitarset multitrack audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_notes(*args, **kwargs)[source]

Load a guitarset note annotation for a given string

Parameters
  • jams_path (str) – Path of the jams annotation file

  • string_num (int), in range(6) – Which string to load. 0 is the Low E string, 5 is the high e string.

Returns

NoteData – Note data for the given string

load_pitch_contour(*args, **kwargs)[source]

Load a guitarset pitch contour annotation for a given string

Parameters
  • jams_path (str) – Path of the jams annotation file

  • string_num (int), in range(6) – Which string to load. 0 is the Low E string, 5 is the high e string.

Returns

F0Data – Pitch contour data for the given string

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.guitarset.Track(track_id, data_home, dataset_name, index, metadata)[source]

guitarset Track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_hex_cln_path (str) – path to the debleeded hex wave file

  • audio_hex_path (str) – path to the original hex wave file

  • audio_mic_path (str) – path to the mono wave via microphone

  • audio_mix_path (str) – path to the mono wave via downmixing hex pickup

  • jams_path (str) – path to the jams file

  • mode (str) – one of [‘solo’, ‘comp’] For each excerpt, players are asked to first play in ‘comp’ mode and later play a ‘solo’ version on top of the already recorded comp.

  • player_id (str) – ID of the different players. one of [‘00’, ‘01’, … , ‘05’]

  • style (str) – one of [‘Jazz’, ‘Bossa Nova’, ‘Rock’, ‘Singer-Songwriter’, ‘Funk’]

  • tempo (float) – BPM of the track

  • track_id (str) – track id

Other Parameters
  • beats (BeatData) – beat positions

  • leadsheet_chords (ChordData) – chords as written in the leadsheet

  • inferred_chords (ChordData) – chords inferred from played transcription

  • key_mode (KeyData) – key and mode

  • pitch_contours (dict) – Pitch contours per string - ‘E’: F0Data(…) - ‘A’: F0Data(…) - ‘D’: F0Data(…) - ‘G’: F0Data(…) - ‘B’: F0Data(…) - ‘e’: F0Data(…)

  • notes (dict) – Notes per string - ‘E’: NoteData(…) - ‘A’: NoteData(…) - ‘D’: NoteData(…) - ‘G’: NoteData(…) - ‘B’: NoteData(…) - ‘e’: NoteData(…)

property audio_hex

Hexaphonic audio (6-channels) with one channel per string

Returns

  • np.ndarray - audio signal

  • float - sample rate

property audio_hex_cln
Hexaphonic audio (6-channels) with one channel per string

after bleed removal

Returns

  • np.ndarray - audio signal

  • float - sample rate

property audio_mic

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

property audio_mix

Mixture audio (mono)

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.guitarset.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Guitarset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.guitarset.load_beats(fhandle: TextIO)mirdata.annotations.BeatData[source]

Load a Guitarset beats annotation.

Parameters

fhandle (str or file-like) – File-like object or path of the jams annotation file

Returns

BeatData – Beat data

mirdata.datasets.guitarset.load_chords(jams_path, leadsheet_version=True)[source]

Load a guitarset chord annotation.

Parameters
  • jams_path (str) – Path of the jams annotation file

  • leadsheet_version (Bool) – Whether or not to load the leadsheet version of the chord annotation If False, load the infered version.

Returns

ChordData – Chord data

mirdata.datasets.guitarset.load_key_mode(fhandle: TextIO)mirdata.annotations.KeyData[source]

Load a Guitarset key-mode annotation.

Parameters

fhandle (str or file-like) – File-like object or path of the jams annotation file

Returns

KeyData – Key data

mirdata.datasets.guitarset.load_multitrack_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Guitarset multitrack audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.guitarset.load_notes(jams_path, string_num)[source]

Load a guitarset note annotation for a given string

Parameters
  • jams_path (str) – Path of the jams annotation file

  • string_num (int), in range(6) – Which string to load. 0 is the Low E string, 5 is the high e string.

Returns

NoteData – Note data for the given string

mirdata.datasets.guitarset.load_pitch_contour(jams_path, string_num)[source]

Load a guitarset pitch contour annotation for a given string

Parameters
  • jams_path (str) – Path of the jams annotation file

  • string_num (int), in range(6) – Which string to load. 0 is the Low E string, 5 is the high e string.

Returns

F0Data – Pitch contour data for the given string

ikala

iKala Dataset Loader

class mirdata.datasets.ikala.Dataset(data_home=None)[source]

The ikala dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_f0(*args, **kwargs)[source]

Load an ikala f0 annotation

Parameters

fhandle (str or file-like) – File-like object or path to f0 annotation file

Raises

IOError – If f0_path does not exist

Returns

F0Data – the f0 annotation data

load_instrumental_audio(*args, **kwargs)[source]

Load ikala instrumental audio

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - audio signal

  • float - sample rate

load_lyrics(*args, **kwargs)[source]

Load an ikala lyrics annotation

Parameters

fhandle (str or file-like) – File-like object or path to lyric annotation file

Raises

IOError – if lyrics_path does not exist

Returns

LyricData – lyric annotation data

load_mix_audio(*args, **kwargs)[source]

Load an ikala mix.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - audio signal

  • float - sample rate

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

load_vocal_audio(*args, **kwargs)[source]

Load ikala vocal audio

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - audio signal

  • float - sample rate

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.ikala.Track(track_id, data_home, dataset_name, index, metadata)[source]

ikala Track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (str) – path to the track’s audio file

  • f0_path (str) – path to the track’s f0 annotation file

  • lyrics_path (str) – path to the track’s lyric annotation file

  • section (str) – section. Either ‘verse’ or ‘chorus’

  • singer_id (str) – singer id

  • song_id (str) – song id of the track

  • track_id (str) – track id

Other Parameters
  • f0 (F0Data) – human-annotated singing voice pitch

  • lyrics (LyricsData) – human-annotated lyrics

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

property instrumental_audio

instrumental audio (mono)

Returns

  • np.ndarray - audio signal

  • float - sample rate

property mix_audio

mixture audio (mono)

Returns

  • np.ndarray - audio signal

  • float - sample rate

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

property vocal_audio

solo vocal audio (mono)

Returns

  • np.ndarray - audio signal

  • float - sample rate

mirdata.datasets.ikala.load_f0(fhandle: TextIO)mirdata.annotations.F0Data[source]

Load an ikala f0 annotation

Parameters

fhandle (str or file-like) – File-like object or path to f0 annotation file

Raises

IOError – If f0_path does not exist

Returns

F0Data – the f0 annotation data

mirdata.datasets.ikala.load_instrumental_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load ikala instrumental audio

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - audio signal

  • float - sample rate

mirdata.datasets.ikala.load_lyrics(fhandle: TextIO)mirdata.annotations.LyricData[source]

Load an ikala lyrics annotation

Parameters

fhandle (str or file-like) – File-like object or path to lyric annotation file

Raises

IOError – if lyrics_path does not exist

Returns

LyricData – lyric annotation data

mirdata.datasets.ikala.load_mix_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load an ikala mix.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - audio signal

  • float - sample rate

mirdata.datasets.ikala.load_vocal_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load ikala vocal audio

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - audio signal

  • float - sample rate

irmas

IRMAS Loader

class mirdata.datasets.irmas.Dataset(data_home=None)[source]

The irmas dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a IRMAS dataset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_pred_inst(*args, **kwargs)[source]

Load predominant instrument of track

Parameters

fhandle (str or file-like) – File-like object or path where the test annotations are stored.

Returns

list(str) – test track predominant instrument(s) annotations

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.irmas.Track(track_id, data_home, dataset_name, index, metadata)[source]

IRMAS track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored. If None, looks for the data in the default directory, ~/mir_datasets/Mridangam-Stroke

Variables
  • track_id (str) – track id

  • predominant_instrument (list) – Training tracks predominant instrument

  • train (bool) – flag to identify if the track is from the training of the testing dataset

  • genre (str) – string containing the namecode of the genre of the track.

  • drum (bool) – flag to identify if the track contains drums or not.

Other Parameters

instrument (list) – list of predominant instruments as str

property audio

The track’s audio signal

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

the track’s data in jams format

Returns

jams.JAMS – return track data in jam format

mirdata.datasets.irmas.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a IRMAS dataset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.irmas.load_pred_inst(fhandle: TextIO) → List[str][source]

Load predominant instrument of track

Parameters

fhandle (str or file-like) – File-like object or path where the test annotations are stored.

Returns

list(str) – test track predominant instrument(s) annotations

maestro

MAESTRO Dataset Loader

class mirdata.datasets.maestro.Dataset(data_home=None)[source]

The maestro dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download the dataset

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a MAESTRO audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_midi(*args, **kwargs)[source]

Load a MAESTRO midi file.

Parameters

fhandle (str or file-like) – File-like object or path to midi file

Returns

pretty_midi.PrettyMIDI – pretty_midi object

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_notes(*args, **kwargs)[source]

Load note data from the midi file.

Parameters
  • midi_path (str) – path to midi file

  • midi (pretty_midi.PrettyMIDI) – pre-loaded midi object or None if None, the midi object is loaded using midi_path

Returns

NoteData – note annotations

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.maestro.Track(track_id, data_home, dataset_name, index, metadata)[source]

MAESTRO Track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (str) – Path to the track’s audio file

  • canonical_composer (str) – Composer of the piece, standardized on a single spelling for a given name.

  • canonical_title (str) – Title of the piece. Not guaranteed to be standardized to a single representation.

  • duration (float) – Duration in seconds, based on the MIDI file.

  • midi_path (str) – Path to the track’s MIDI file

  • split (str) – Suggested train/validation/test split.

  • track_id (str) – track id

  • year (int) – Year of performance.

Cached Property:

midi (pretty_midi.PrettyMIDI): object containing MIDI annotations notes (NoteData): annotated piano notes

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.maestro.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a MAESTRO audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.maestro.load_midi(fhandle: BinaryIO)pretty_midi.PrettyMIDI[source]

Load a MAESTRO midi file.

Parameters

fhandle (str or file-like) – File-like object or path to midi file

Returns

pretty_midi.PrettyMIDI – pretty_midi object

mirdata.datasets.maestro.load_notes(midi_path, midi=None)[source]

Load note data from the midi file.

Parameters
  • midi_path (str) – path to midi file

  • midi (pretty_midi.PrettyMIDI) – pre-loaded midi object or None if None, the midi object is loaded using midi_path

Returns

NoteData – note annotations

medley_solos_db

Medley-solos-DB Dataset Loader.

class mirdata.datasets.medley_solos_db.Dataset(data_home=None)[source]

The medley_solos_db dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Medley Solos DB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.medley_solos_db.Track(track_id, data_home, dataset_name, index, metadata)[source]

medley_solos_db Track class

Parameters

track_id (str) – track id of the track

Variables
  • 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

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.medley_solos_db.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Medley Solos DB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

medleydb_melody

MedleyDB melody Dataset Loader

class mirdata.datasets.medleydb_melody.Dataset(data_home=None)[source]

The medleydb_melody dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a MedleyDB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_melody(*args, **kwargs)[source]

Load a MedleyDB melody1 or melody2 annotation file

Parameters

fhandle (str or file-like) – File-like object or path to a melody annotation file

Raises

IOError – if melody_path does not exist

Returns

F0Data – melody data

load_melody3(*args, **kwargs)[source]

Load a MedleyDB melody3 annotation file

Parameters

fhandle (str or file-like) – File-like object or melody 3 melody annotation path

Raises

IOError – if melody_path does not exist

Returns

MultiF0Data – melody 3 annotation data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.medleydb_melody.Track(track_id, data_home, dataset_name, index, metadata)[source]

medleydb_melody Track class

Parameters

track_id (str) – track id of the track

Variables
  • artist (str) – artist

  • audio_path (str) – path to the audio file

  • genre (str) – genre

  • is_excerpt (bool) – True if the track is an excerpt

  • is_instrumental (bool) – True of the track does not contain vocals

  • melody1_path (str) – path to the melody1 annotation file

  • melody2_path (str) – path to the melody2 annotation file

  • melody3_path (str) – path to the melody3 annotation file

  • n_sources (int) – Number of instruments in the track

  • title (str) – title

  • track_id (str) – track id

Other Parameters
  • melody1 (F0Data) – the pitch of the single most predominant source (often the voice)

  • melody2 (F0Data) – the pitch of the predominant source for each point in time

  • melody3 (MultiF0Data) – the pitch of any melodic source. Allows for more than one f0 value at a time

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.medleydb_melody.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a MedleyDB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.medleydb_melody.load_melody(fhandle: TextIO)mirdata.annotations.F0Data[source]

Load a MedleyDB melody1 or melody2 annotation file

Parameters

fhandle (str or file-like) – File-like object or path to a melody annotation file

Raises

IOError – if melody_path does not exist

Returns

F0Data – melody data

mirdata.datasets.medleydb_melody.load_melody3(fhandle: TextIO)mirdata.annotations.MultiF0Data[source]

Load a MedleyDB melody3 annotation file

Parameters

fhandle (str or file-like) – File-like object or melody 3 melody annotation path

Raises

IOError – if melody_path does not exist

Returns

MultiF0Data – melody 3 annotation data

medleydb_pitch

MedleyDB pitch Dataset Loader

class mirdata.datasets.medleydb_pitch.Dataset(data_home=None)[source]

The medleydb_pitch dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a MedleyDB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_pitch(*args, **kwargs)[source]

load a MedleyDB pitch annotation file

Parameters

pitch_path (str) – path to pitch annotation file

Raises

IOError – if pitch_path doesn’t exist

Returns

F0Data – pitch annotation

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.medleydb_pitch.Track(track_id, data_home, dataset_name, index, metadata)[source]

medleydb_pitch Track class

Parameters

track_id (str) – track id of the track

Variables
  • artist (str) – artist

  • audio_path (str) – path to the audio file

  • genre (str) – genre

  • instrument (str) – instrument of the track

  • pitch_path (str) – path to the pitch annotation file

  • title (str) – title

  • track_id (str) – track id

Other Parameters

pitch (F0Data) – human annotated pitch

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.medleydb_pitch.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a MedleyDB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.medleydb_pitch.load_pitch(fhandle: TextIO)mirdata.annotations.F0Data[source]

load a MedleyDB pitch annotation file

Parameters

pitch_path (str) – path to pitch annotation file

Raises

IOError – if pitch_path doesn’t exist

Returns

F0Data – pitch annotation

mridangam_stroke

Mridangam Stroke Dataset Loader

class mirdata.datasets.mridangam_stroke.Dataset(data_home=None)[source]

The mridangam_stroke dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Mridangam Stroke Dataset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.mridangam_stroke.Track(track_id, data_home, dataset_name, index, metadata)[source]

Mridangam Stroke track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored.

Variables
  • track_id (str) – track id

  • audio_path (str) – audio path

  • stroke_name (str) – name of the Mridangam stroke present in Track

  • tonic (str) – tonic of the stroke in the Track

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.mridangam_stroke.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Mridangam Stroke Dataset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

orchset

ORCHSET Dataset Loader

class mirdata.datasets.orchset.Dataset(data_home=None)[source]

The orchset dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio_mono(*args, **kwargs)[source]

Load an Orchset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_audio_stereo(*args, **kwargs)[source]

Load an Orchset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the stereo audio signal

  • float - The sample rate of the audio file

load_melody(*args, **kwargs)[source]

Load an Orchset melody annotation file

Parameters

fhandle (str or file-like) – File-like object or path to melody annotation file

Raises

IOError – if melody_path doesn’t exist

Returns

F0Data – melody annotation data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.orchset.Track(track_id, data_home, dataset_name, index, metadata)[source]

orchset Track class

Parameters

track_id (str) – track id of the track

Variables
  • alternating_melody (bool) – True if the melody alternates between instruments

  • audio_path_mono (str) – path to the mono audio file

  • audio_path_stereo (str) – path to the stereo audio file

  • composer (str) – the work’s composer

  • contains_brass (bool) – True if the track contains any brass instrument

  • contains_strings (bool) – True if the track contains any string instrument

  • contains_winds (bool) – True if the track contains any wind instrument

  • excerpt (str) – True if the track is an excerpt

  • melody_path (str) – path to the melody annotation file

  • only_brass (bool) – True if the track contains brass instruments only

  • only_strings (bool) – True if the track contains string instruments only

  • only_winds (bool) – True if the track contains wind instruments only

  • predominant_melodic_instruments (list) – List of instruments which play the melody

  • track_id (str) – track id

  • work (str) – The musical work

Other Parameters

melody (F0Data) – melody annotation

property audio_mono

the track’s audio (mono)

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

property audio_stereo

the track’s audio (stereo)

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.orchset.load_audio_mono(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load an Orchset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.orchset.load_audio_stereo(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load an Orchset audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the stereo audio signal

  • float - The sample rate of the audio file

mirdata.datasets.orchset.load_melody(fhandle: TextIO)mirdata.annotations.F0Data[source]

Load an Orchset melody annotation file

Parameters

fhandle (str or file-like) – File-like object or path to melody annotation file

Raises

IOError – if melody_path doesn’t exist

Returns

F0Data – melody annotation data

phenicx_anechoic

PHENICX-Anechoic Dataset Loader

class mirdata.datasets.phenicx_anechoic.Dataset(data_home=None)[source]

The Phenicx-Anechoic dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Phenicx-Anechoic audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_score(*args, **kwargs)[source]

Load a Phenicx-Anechoic score file.

Parameters

fhandle (str or file-like) – File-like object or path to score file

Returns

NoteData – Note data for the given track

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.phenicx_anechoic.MultiTrack(mtrack_id, data_home, dataset_name, index, track_class, metadata)[source]

Phenicx-Anechoic MultiTrack class

Parameters
  • mtrack_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored. If None, looks for the data in the default directory, ~/mir_datasets/Phenicx-Anechoic

Variables
  • track_audio_property (str) – the attribute of track which is used for mixing

  • mtrack_id (str) – multitrack id

  • piece (str) – the classical music piece associated with this multitrack

  • tracks (dict) – dict of track ids and the corresponding Tracks

  • instruments (dict) – dict of instruments and the corresponding track

  • sections (dict) – dict of sections and the corresponding list of tracks for each section

get_audio_for_instrument(instrument)[source]

Get the audio for a particular instrument

Parameters

instrument (str) – the instrument to get audio for

Returns

np.ndarray – instrument audio with shape (n_samples, n_channels)

get_audio_for_section(section)[source]

Get the audio for a particular section

Parameters

section (str) – the section to get audio for

Returns

np.ndarray – section audio with shape (n_samples, n_channels)

get_mix()[source]

Create a linear mixture given a subset of tracks.

Parameters

track_keys (list) – list of track keys to mix together

Returns

np.ndarray – mixture audio with shape (n_samples, n_channels)

get_notes_for_instrument(instrument, notes_property='notes')[source]

Get the notes for a particular instrument

Parameters
  • instrument (str) – the instrument to get the notes for

  • notes_property (str) – the attribute associated with NoteData, notes or notes_original

Returns

NoteData – Note data for the instrument

get_notes_for_section(section, notes_property='notes')[source]

Get the notes for a particular section

Parameters
  • section (str) – the section to get the notes for

  • notes_property (str) – the attribute associated with NoteData, notes or notes_original

Returns

NoteData – Note data for the section

get_notes_target(track_keys, notes_property='notes')[source]

Get the notes for all the tracks

Parameters
  • track_keys (list) – list of track keys to get the NoteData for

  • notes_property (str) – the attribute associated with NoteData, notes or notes_original

Returns

NoteData – Note data for the tracks

get_path(key)[source]

Get absolute path to multitrack audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

get_random_target(n_tracks=None, min_weight=0.3, max_weight=1.0)[source]

Get a random target by combining a random selection of tracks with random weights

Parameters
  • 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

get_target(track_keys, weights=None, average=True, enforce_length=True)[source]

Get target which is a linear mixture of tracks

Parameters
  • 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

class mirdata.datasets.phenicx_anechoic.Track(track_id, data_home, dataset_name, index, metadata)[source]

Phenicx-Anechoic Track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (list) – path to the audio files

  • notes_path (list) – path to the score files

  • notes_original_path (list) – path to the original score files

  • instrument (str) – the name of the instrument

  • piece (str) – the name of the piece

  • n_voices (int) – the number of voices in this instrument

  • track_id (str) – track id

Other Parameters

melody (F0Data) – melody annotation

property audio

the track’s audio

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

get_audio_voice(id_voice: int) → Optional[Tuple[numpy.ndarray, float]][source]

the track’s audio

Parameters

id_voice (int) – The integer identifier for the voice e.g. 2 for bassoon-2

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

notes

the track’s notes corresponding to the score aligned to the audio

Returns

NoteData – Note data for the track

notes_original

the track’s notes corresponding to the original score

Returns

NoteData – Note data for the track

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.phenicx_anechoic.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Phenicx-Anechoic audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the audio signal

  • float - The sample rate of the audio file

mirdata.datasets.phenicx_anechoic.load_score(fhandle: TextIO)mirdata.annotations.NoteData[source]

Load a Phenicx-Anechoic score file.

Parameters

fhandle (str or file-like) – File-like object or path to score file

Returns

NoteData – Note data for the given track

rwc_classical

RWC Classical Dataset Loader

class mirdata.datasets.rwc_classical.Dataset(data_home=None)[source]

The rwc_classical dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a RWC audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_beats(*args, **kwargs)[source]

Load rwc beat data from a file

Parameters

fhandle (str or file-like) – File-like object or path to beats annotation file

Returns

BeatData – beat data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_sections(*args, **kwargs)[source]

Load rwc section data from a file

Parameters

fhandle (str or file-like) – File-like object or path to sections annotation file

Returns

SectionData – section data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.rwc_classical.Track(track_id, data_home, dataset_name, index, metadata)[source]

rwc_classical Track class

Parameters

track_id (str) – track id of the track

Variables
  • artist (str) – the track’s artist

  • audio_path (str) – path of the audio file

  • beats_path (str) – path of the beat annotation file

  • category (str) – One of ‘Symphony’, ‘Concerto’, ‘Orchestral’, ‘Solo’, ‘Chamber’, ‘Vocal’, or blank.

  • composer (str) – Composer of this Track.

  • duration (float) – Duration of the track in seconds

  • piece_number (str) – Piece number of this Track, [1-50]

  • sections_path (str) – path of the section annotation file

  • suffix (str) – string within M01-M06

  • title (str) – Title of The track.

  • track_id (str) – track id

  • track_number (str) – CD track number of this Track

Other Parameters
  • sections (SectionData) – human-labeled section annotations

  • beats (BeatData) – human-labeled beat annotations

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.rwc_classical.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a RWC audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.rwc_classical.load_beats(fhandle: TextIO)mirdata.annotations.BeatData[source]

Load rwc beat data from a file

Parameters

fhandle (str or file-like) – File-like object or path to beats annotation file

Returns

BeatData – beat data

mirdata.datasets.rwc_classical.load_sections(fhandle: TextIO) → Optional[mirdata.annotations.SectionData][source]

Load rwc section data from a file

Parameters

fhandle (str or file-like) – File-like object or path to sections annotation file

Returns

SectionData – section data

rwc_jazz

RWC Jazz Dataset Loader.

class mirdata.datasets.rwc_jazz.Dataset(data_home=None)[source]

The rwc_jazz dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a RWC audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_beats(*args, **kwargs)[source]

Load rwc beat data from a file

Parameters

fhandle (str or file-like) – File-like object or path to beats annotation file

Returns

BeatData – beat data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_sections(*args, **kwargs)[source]

Load rwc section data from a file

Parameters

fhandle (str or file-like) – File-like object or path to sections annotation file

Returns

SectionData – section data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.rwc_jazz.Track(track_id, data_home, dataset_name, index, metadata)[source]

rwc_jazz Track class

Parameters

track_id (str) – track id of the track

Variables
  • artist (str) – Artist name

  • audio_path (str) – path of the audio file

  • beats_path (str) – path of the beat annotation file

  • duration (float) – Duration of the track in seconds

  • instruments (str) – list of used instruments.

  • piece_number (str) – Piece number of this Track, [1-50]

  • sections_path (str) – path of the section annotation file

  • suffix (str) – M01-M04

  • title (str) – Title of The track.

  • track_id (str) – track id

  • track_number (str) – CD track number of this Track

  • variation (str) – style variations

Other Parameters
  • sections (SectionData) – human-labeled section data

  • beats (BeatData) – human-labeled beat data

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

salami

SALAMI Dataset Loader

class mirdata.datasets.salami.Dataset(data_home=None)[source]

The salami dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Salami audio file.

Parameters

fhandle (str or file-like) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_sections(*args, **kwargs)[source]

Load salami sections data from a file

Parameters

fhandle (str or file-like) – File-like object or path to sectin annotation file

Returns

SectionData – section data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.salami.Track(track_id, data_home, dataset_name, index, metadata)[source]

salami Track class

Parameters

track_id (str) – track id of the track

Variables
  • 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

Other Parameters
  • 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

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.salami.load_audio(fhandle: str) → Tuple[numpy.ndarray, float][source]

Load a Salami audio file.

Parameters

fhandle (str or file-like) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.salami.load_sections(fhandle: TextIO)mirdata.annotations.SectionData[source]

Load salami sections data from a file

Parameters

fhandle (str or file-like) – File-like object or path to sectin annotation file

Returns

SectionData – section data

saraga_carnatic

Saraga Dataset Loader

class mirdata.datasets.saraga_carnatic.Dataset(data_home=None)[source]

The saraga_carnatic dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Saraga Carnatic audio file.

Parameters

audio_path (str) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_metadata(*args, **kwargs)[source]

Load a Saraga Carnatic metadata file

Parameters

metadata_path (str) – path to metadata json file

Returns

dict

metadata with the following fields

  • title (str): Title of the piece in the track

  • mbid (str): MusicBrainz ID of the track

  • album_artists (list, dicts): list of dicts containing the album artists present in the track and its mbid

  • artists (list, dicts): list of dicts containing information of the featuring artists in the track

  • raaga (list, dict): list of dicts containing information about the raagas present in the track

  • form (list, dict): list of dicts containing information about the forms present in the track

  • work (list, dicts): list of dicts containing the work present in the piece, and its mbid

  • taala (list, dicts): list of dicts containing the talas present in the track and its uuid

  • concert (list, dicts): list of dicts containing the concert where the track is present and its mbid

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_phrases(*args, **kwargs)[source]

Load phrases

Parameters

phrases_path (str) – Local path where the phrase annotation is stored. If None, returns None.

Returns

EventData – phrases annotation for track

load_pitch(*args, **kwargs)[source]

Load pitch

Parameters

pitch path (str) – Local path where the pitch annotation is stored. If None, returns None.

Returns

F0Data – pitch annotation

load_sama(*args, **kwargs)[source]

Load sama

Parameters

sama_path (str) – Local path where the sama annotation is stored. If None, returns None.

Returns

BeatData – sama annotations

load_sections(*args, **kwargs)[source]

Load sections from carnatic collection

Parameters

sections_path (str) – Local path where the section annotation is stored.

Returns

SectionData – section annotations for track

load_tempo(*args, **kwargs)[source]

Load tempo from carnatic collection

Parameters

tempo_path (str) – Local path where the tempo annotation is stored.

Returns

dict

Dictionary of tempo information with the following keys:

  • tempo_apm: tempo in aksharas per minute (APM)

  • tempo_bpm: tempo in beats per minute (BPM)

  • sama_interval: median duration (in seconds) of one tāla cycle

  • beats_per_cycle: number of beats in one cycle of the tāla

  • subdivisions: number of aksharas per beat of the tāla

load_tonic(*args, **kwargs)[source]

Load track absolute tonic

Parameters

tonic_path (str) – Local path where the tonic path is stored. If None, returns None.

Returns

int – Tonic annotation in Hz

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.saraga_carnatic.Track(track_id, data_home, dataset_name, index, metadata)[source]

Saraga Track Carnatic class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored. default=None If None, looks for the data in the default directory, ~/mir_datasets

Variables
  • audio_path (str) – path to audio file

  • audio_ghatam_path (str) – path to ghatam audio file

  • audio_mridangam_left_path (str) – path to mridangam left audio file

  • audio_mridangam_right_path (str) – path to mridangam right audio file

  • audio_violin_path (str) – path to violin audio file

  • audio_vocal_s_path (str) – path to vocal s audio file

  • audio_vocal_pat (str) – path to vocal pat audio file

  • ctonic_path (srt) – path to ctonic annotation file

  • pitch_path (srt) – path to pitch annotation file

  • pitch_vocal_path (srt) – path to vocal pitch annotation file

  • tempo_path (srt) – path to tempo annotation file

  • sama_path (srt) – path to sama annotation file

  • sections_path (srt) – path to sections annotation file

  • phrases_path (srt) – path to phrases annotation file

  • metadata_path (srt) – path to metadata file

Other Parameters
  • tonic (float) – tonic annotation

  • pitch (F0Data) – pitch annotation

  • pitch_vocal (F0Data) – vocal pitch annotation

  • tempo (dict) – tempo annotations

  • sama (BeatData) – sama section annotations

  • sections (SectionData) – track section annotations

  • phrases (SectionData) – phrase annotations

  • metadata (dict) – track metadata with the following fields:

    • title (str): Title of the piece in the track

    • mbid (str): MusicBrainz ID of the track

    • album_artists (list, dicts): list of dicts containing the album artists present in the track and its mbid

    • artists (list, dicts): list of dicts containing information of the featuring artists in the track

    • raaga (list, dict): list of dicts containing information about the raagas present in the track

    • form (list, dict): list of dicts containing information about the forms present in the track

    • work (list, dicts): list of dicts containing the work present in the piece, and its mbid

    • taala (list, dicts): list of dicts containing the talas present in the track and its uuid

    • concert (list, dicts): list of dicts containing the concert where the track is present and its mbid

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.saraga_carnatic.load_audio(audio_path)[source]

Load a Saraga Carnatic audio file.

Parameters

audio_path (str) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.saraga_carnatic.load_metadata(metadata_path)[source]

Load a Saraga Carnatic metadata file

Parameters

metadata_path (str) – path to metadata json file

Returns

dict

metadata with the following fields

  • title (str): Title of the piece in the track

  • mbid (str): MusicBrainz ID of the track

  • album_artists (list, dicts): list of dicts containing the album artists present in the track and its mbid

  • artists (list, dicts): list of dicts containing information of the featuring artists in the track

  • raaga (list, dict): list of dicts containing information about the raagas present in the track

  • form (list, dict): list of dicts containing information about the forms present in the track

  • work (list, dicts): list of dicts containing the work present in the piece, and its mbid

  • taala (list, dicts): list of dicts containing the talas present in the track and its uuid

  • concert (list, dicts): list of dicts containing the concert where the track is present and its mbid

mirdata.datasets.saraga_carnatic.load_phrases(phrases_path)[source]

Load phrases

Parameters

phrases_path (str) – Local path where the phrase annotation is stored. If None, returns None.

Returns

EventData – phrases annotation for track

mirdata.datasets.saraga_carnatic.load_pitch(pitch_path)[source]

Load pitch

Parameters

pitch path (str) – Local path where the pitch annotation is stored. If None, returns None.

Returns

F0Data – pitch annotation

mirdata.datasets.saraga_carnatic.load_sama(sama_path)[source]

Load sama

Parameters

sama_path (str) – Local path where the sama annotation is stored. If None, returns None.

Returns

BeatData – sama annotations

mirdata.datasets.saraga_carnatic.load_sections(sections_path)[source]

Load sections from carnatic collection

Parameters

sections_path (str) – Local path where the section annotation is stored.

Returns

SectionData – section annotations for track

mirdata.datasets.saraga_carnatic.load_tempo(tempo_path)[source]

Load tempo from carnatic collection

Parameters

tempo_path (str) – Local path where the tempo annotation is stored.

Returns

dict

Dictionary of tempo information with the following keys:

  • tempo_apm: tempo in aksharas per minute (APM)

  • tempo_bpm: tempo in beats per minute (BPM)

  • sama_interval: median duration (in seconds) of one tāla cycle

  • beats_per_cycle: number of beats in one cycle of the tāla

  • subdivisions: number of aksharas per beat of the tāla

mirdata.datasets.saraga_carnatic.load_tonic(tonic_path)[source]

Load track absolute tonic

Parameters

tonic_path (str) – Local path where the tonic path is stored. If None, returns None.

Returns

int – Tonic annotation in Hz

saraga_hindustani

Saraga Dataset Loader

class mirdata.datasets.saraga_hindustani.Dataset(data_home=None)[source]

The saraga_hindustani dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Saraga Hindustani audio file.

Parameters

audio_path (str) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_phrases(*args, **kwargs)[source]

Load phrases

Parameters

phrases_path (str) – Local path where the phrase annotation is stored. If None, returns None.

Returns

EventData – phrases annotation for track

load_pitch(*args, **kwargs)[source]

Load automatic extracted pitch or melody

Parameters

pitch path (str) – Local path where the pitch annotation is stored. If None, returns None.

Returns

F0Data – pitch annotation

load_sama(*args, **kwargs)[source]

Load sama

Parameters

sama_path (str) – Local path where the sama annotation is stored. If None, returns None.

Returns

SectionData – sama annotations

load_sections(*args, **kwargs)[source]

Load tracks sections

Parameters

sections_path (str) – Local path where the section annotation is stored.

Returns

SectionData – section annotations for track

load_tempo(*args, **kwargs)[source]

Load tempo from hindustani collection

Parameters

tempo_path (str) – Local path where the tempo annotation is stored.

Returns

dict – Dictionary of tempo information with the following keys:

  • tempo: median tempo for the section in mātrās per minute (MPM)

  • matra_interval: tempo expressed as the duration of the mātra (essentially dividing 60 by tempo, expressed in seconds)

  • sama_interval: median duration of one tāl cycle in the section

  • matras_per_cycle: indicator of the structure of the tāl, showing the number of mātrā in a cycle of the tāl of the recording

  • start_time: start time of the section

  • duration: duration of the section

load_tonic(*args, **kwargs)[source]

Load track absolute tonic

Parameters

tonic_path (str) – Local path where the tonic path is stored. If None, returns None.

Returns

int – Tonic annotation in Hz

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.saraga_hindustani.Track(track_id, data_home, dataset_name, index, metadata)[source]

Saraga Hindustani Track class

Parameters
  • track_id (str) – track id of the track

  • data_home (str) – Local path where the dataset is stored. default=None If None, looks for the data in the default directory, ~/mir_datasets

Variables
  • audio_path (str) – path to audio file

  • ctonic_path (str) – path to ctonic annotation file

  • pitch_path (str) – path to pitch annotation file

  • tempo_path (str) – path to tempo annotation file

  • sama_path (str) – path to sama annotation file

  • sections_path (str) – path to sections annotation file

  • phrases_path (str) – path to phrases annotation file

  • metadata_path (str) – path to metadata annotation file

Other Parameters
  • tonic (float) – tonic annotation

  • pitch (F0Data) – pitch annotation

  • tempo (dict) – tempo annotations

  • sama (BeatData) – Sama section annotations

  • sections (SectionData) – track section annotations

  • phrases (EventData) – phrase annotations

  • metadata (dict) – track metadata with the following fields

    • title (str): Title of the piece in the track

    • mbid (str): MusicBrainz ID of the track

    • album_artists (list, dicts): list of dicts containing the album artists present in the track and its mbid

    • artists (list, dicts): list of dicts containing information of the featuring artists in the track

    • raags (list, dict): list of dicts containing information about the raags present in the track

    • forms (list, dict): list of dicts containing information about the forms present in the track

    • release (list, dicts): list of dicts containing information of the release where the track is found

    • works (list, dicts): list of dicts containing the work present in the piece, and its mbid

    • taals (list, dicts): list of dicts containing the taals present in the track and its uuid

    • layas (list, dicts): list of dicts containing the layas present in the track and its uuid

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.saraga_hindustani.load_audio(audio_path)[source]

Load a Saraga Hindustani audio file.

Parameters

audio_path (str) – path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.saraga_hindustani.load_metadata(metadata_path)[source]

Load a Saraga Hindustani metadata file

Parameters

metadata_path (str) – path to metadata json file

Returns

dict

metadata with the following fields

  • title (str): Title of the piece in the track

  • mbid (str): MusicBrainz ID of the track

  • album_artists (list, dicts): list of dicts containing the album artists present in the track and its mbid

  • artists (list, dicts): list of dicts containing information of the featuring artists in the track

  • raags (list, dict): list of dicts containing information about the raags present in the track

  • forms (list, dict): list of dicts containing information about the forms present in the track

  • release (list, dicts): list of dicts containing information of the release where the track is found

  • works (list, dicts): list of dicts containing the work present in the piece, and its mbid

  • taals (list, dicts): list of dicts containing the taals present in the track and its uuid

  • layas (list, dicts): list of dicts containing the layas present in the track and its uuid

mirdata.datasets.saraga_hindustani.load_phrases(phrases_path)[source]

Load phrases

Parameters

phrases_path (str) – Local path where the phrase annotation is stored. If None, returns None.

Returns

EventData – phrases annotation for track

mirdata.datasets.saraga_hindustani.load_pitch(pitch_path)[source]

Load automatic extracted pitch or melody

Parameters

pitch path (str) – Local path where the pitch annotation is stored. If None, returns None.

Returns

F0Data – pitch annotation

mirdata.datasets.saraga_hindustani.load_sama(sama_path)[source]

Load sama

Parameters

sama_path (str) – Local path where the sama annotation is stored. If None, returns None.

Returns

SectionData – sama annotations

mirdata.datasets.saraga_hindustani.load_sections(sections_path)[source]

Load tracks sections

Parameters

sections_path (str) – Local path where the section annotation is stored.

Returns

SectionData – section annotations for track

mirdata.datasets.saraga_hindustani.load_tempo(tempo_path)[source]

Load tempo from hindustani collection

Parameters

tempo_path (str) – Local path where the tempo annotation is stored.

Returns

dict – Dictionary of tempo information with the following keys:

  • tempo: median tempo for the section in mātrās per minute (MPM)

  • matra_interval: tempo expressed as the duration of the mātra (essentially dividing 60 by tempo, expressed in seconds)

  • sama_interval: median duration of one tāl cycle in the section

  • matras_per_cycle: indicator of the structure of the tāl, showing the number of mātrā in a cycle of the tāl of the recording

  • start_time: start time of the section

  • duration: duration of the section

mirdata.datasets.saraga_hindustani.load_tonic(tonic_path)[source]

Load track absolute tonic

Parameters

tonic_path (str) – Local path where the tonic path is stored. If None, returns None.

Returns

int – Tonic annotation in Hz

tinysol

TinySOL Dataset Loader.

class mirdata.datasets.tinysol.Dataset(data_home=None)[source]

The tinysol dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a TinySOL audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.tinysol.Track(track_id, data_home, dataset_name, index, metadata)[source]

tinysol Track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (str) – path of the audio file

  • dynamics (str) – dynamics abbreviation. Ex: pp, mf, ff, etc.

  • dynamics_id (int) – pp=0, p=1, mf=2, f=3, ff=4

  • family (str) – instrument family encoded by its English name

  • instance_id (int) – instance ID. Either equal to 0, 1, 2, or 3.

  • instrument_abbr (str) – instrument abbreviation

  • instrument_full (str) – instrument encoded by its English name

  • is_resampled (bool) – True if this sample was pitch-shifted from a neighbor; False if it was genuinely recorded.

  • pitch (str) – string containing English pitch class and octave number

  • pitch_id (int) – MIDI note index, where middle C (“C4”) corresponds to 60

  • string_id (NoneType) – string ID. By musical convention, the first string is the highest. On wind instruments, this is replaced by None.

  • technique_abbr (str) – playing technique abbreviation

  • technique_full (str) – playing technique encoded by its English name

  • track_id (str) – track id

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.tinysol.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a TinySOL audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

tonality_classicaldb

Tonality classicalDB Dataset Loader

class mirdata.datasets.tonality_classicaldb.Dataset(data_home=None)[source]

The tonality_classicaldb dataset

Variables
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_audio(*args, **kwargs)[source]

Load a Tonality classicalDB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

load_hpcp(*args, **kwargs)[source]

Load Tonality classicalDB HPCP feature from a file

Parameters

fhandle (str or file-like) – File-like object or path to HPCP file

Returns

np.ndarray – loaded HPCP data

load_key(*args, **kwargs)[source]

Load Tonality classicalDB format key data from a file

Parameters

fhandle (str or file-like) – File-like object or path to key annotation file

Returns

str – musical key data

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_musicbrainz(*args, **kwargs)[source]

Load Tonality classicalDB musicbraiz metadata from a file

Parameters

fhandle (str or file-like) – File-like object or path to musicbrainz metadata file

Returns

dict – musicbrainz metadata

load_spectrum(*args, **kwargs)[source]

Load Tonality classicalDB spectrum data from a file

Parameters

fhandle (str or file-like) – File-like object or path to spectrum file

Returns

np.ndarray – spectrum data

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.datasets.tonality_classicaldb.Track(track_id, data_home, dataset_name, index, metadata)[source]

tonality_classicaldb track class

Parameters

track_id (str) – track id of the track

Variables
  • audio_path (str) – track audio path

  • key_path (str) – key annotation path

  • title (str) – title of the track

  • track_id (str) – track id

Other Parameters
  • key (str) – key annotation

  • spectrum (np.array) – computed audio spectrum

  • hpcp (np.array) – computed hpcp

  • musicbrainz_metadata (dict) – MusicBrainz metadata

property audio

The track’s audio

Returns

  • np.ndarray - audio signal

  • float - sample rate

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

to_jams()[source]

Get the track’s data in jams format

Returns

jams.JAMS – the track’s data in jams format

mirdata.datasets.tonality_classicaldb.load_audio(fhandle: BinaryIO) → Tuple[numpy.ndarray, float][source]

Load a Tonality classicalDB audio file.

Parameters

fhandle (str or file-like) – File-like object or path to audio file

Returns

  • np.ndarray - the mono audio signal

  • float - The sample rate of the audio file

mirdata.datasets.tonality_classicaldb.load_hpcp(fhandle: TextIO)numpy.ndarray[source]

Load Tonality classicalDB HPCP feature from a file

Parameters

fhandle (str or file-like) – File-like object or path to HPCP file

Returns

np.ndarray – loaded HPCP data

mirdata.datasets.tonality_classicaldb.load_key(fhandle: TextIO) → str[source]

Load Tonality classicalDB format key data from a file

Parameters

fhandle (str or file-like) – File-like object or path to key annotation file

Returns

str – musical key data

mirdata.datasets.tonality_classicaldb.load_musicbrainz(fhandle: TextIO) → Dict[Any, Any][source]

Load Tonality classicalDB musicbraiz metadata from a file

Parameters

fhandle (str or file-like) – File-like object or path to musicbrainz metadata file

Returns

dict – musicbrainz metadata

mirdata.datasets.tonality_classicaldb.load_spectrum(fhandle: TextIO)numpy.ndarray[source]

Load Tonality classicalDB spectrum data from a file

Parameters

fhandle (str or file-like) – File-like object or path to spectrum file

Returns

np.ndarray – spectrum data

Core

Core mirdata classes

class mirdata.core.Dataset(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)[source]

mirdata Dataset class

Variables
  • 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

__init__(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)[source]

Dataset init method

Parameters
  • 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

choice_multitrack()[source]

Choose a random multitrack

Returns

Multitrack – a Multitrack object instantiated by a random mtrack_id

choice_track()[source]

Choose a random track

Returns

Track – a Track object instantiated by a random track_id

cite()[source]

Print the reference

property default_path

Get the default path for the dataset

Returns

str – Local path to the dataset

download(partial_download=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally print a message.

Parameters
  • 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

license()[source]

Print the license

load_multitracks()[source]

Load all multitracks in the dataset

Returns

dict – {mtrack_id: multitrack data}

Raises

NotImplementedError – If the dataset does not support Multitracks

load_tracks()[source]

Load all tracks in the dataset

Returns

dict – {track_id: track data}

Raises

NotImplementedError – If the dataset does not support Tracks

mtrack_ids[source]

Return track ids

Returns

list – A list of track ids

track_ids[source]

Return track ids

Returns

list – A list of track ids

validate(verbose=True)[source]

Validate if the stored dataset is a valid version

Parameters

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

class mirdata.core.MultiTrack(mtrack_id, data_home, dataset_name, index, track_class, metadata)[source]

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.

__init__(mtrack_id, data_home, dataset_name, index, track_class, metadata)[source]

Multitrack init method. Sets boilerplate attributes, including:

  • mtrack_id

  • _dataset_name

  • _data_home

  • _multitrack_paths

  • _multitrack_metadata

Parameters
  • 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

get_mix()[source]

Create a linear mixture given a subset of tracks.

Parameters

track_keys (list) – list of track keys to mix together

Returns

np.ndarray – mixture audio with shape (n_samples, n_channels)

get_path(key)[source]

Get absolute path to multitrack audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

get_random_target(n_tracks=None, min_weight=0.3, max_weight=1.0)[source]

Get a random target by combining a random selection of tracks with random weights

Parameters
  • 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

get_target(track_keys, weights=None, average=True, enforce_length=True)[source]

Get target which is a linear mixture of tracks

Parameters
  • 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

class mirdata.core.Track(track_id, data_home, dataset_name, index, metadata)[source]

Track base class

See the docs for each dataset loader’s Track class for details

__init__(track_id, data_home, dataset_name, index, metadata)[source]

Track init method. Sets boilerplate attributes, including:

  • track_id

  • _dataset_name

  • _data_home

  • _track_paths

  • _track_metadata

Parameters
  • 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

get_path(key)[source]

Get absolute path to track audio and annotations. Returns None if the path in the index is None

Parameters

key (string) – Index key of the audio or annotation type

Returns

str or None – joined path string or None

class mirdata.core.cached_property(func)[source]

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

mirdata.core.copy_docs(original)[source]

Decorator function to copy docs from one function to another

mirdata.core.docstring_inherit(parent)[source]

Decorator function to inherit docstrings from the parent class.

Adds documented Attributes from the parent to the child docs.

Annotations

mirdata annotation data types

class mirdata.annotations.Annotation[source]

Annotation base class

class mirdata.annotations.BeatData(times, positions=None)[source]

BeatData class

Variables
  • times (np.ndarray) – array of time stamps (as floats) in seconds with positive, strictly increasing values

  • positions (np.ndarray or None) – array of beat positions (as ints) e.g. 1, 2, 3, 4

class mirdata.annotations.ChordData(intervals, labels, confidence=None)[source]

ChordData class

Variables
  • intervals (np.ndarray or None) – (n x 2) array of intervals (as floats) in seconds in the form [start_time, end_time] with positive time stamps and end_time >= start_time.

  • labels (list) – list chord labels (as strings)

  • confidence (np.ndarray or None) – array of confidence values between 0 and 1

class mirdata.annotations.EventData(intervals, events)[source]

TempoData class

Variables
  • intervals (np.ndarray) – (n x 2) array of intervals (as floats) in seconds in the form [start_time, end_time] with positive time stamps and end_time >= start_time.

  • events (list) – list of event labels (as strings)

class mirdata.annotations.F0Data(times, frequencies, confidence=None)[source]

F0Data class

Variables
  • times (np.ndarray) – array of time stamps (as floats) in seconds with positive, strictly increasing values

  • frequencies (np.ndarray) – array of frequency values (as floats) in Hz

  • confidence (np.ndarray or None) – array of confidence values between 0 and 1

class mirdata.annotations.KeyData(intervals, keys)[source]

KeyData class

Variables
  • intervals (np.ndarray) – (n x 2) array of intervals (as floats) in seconds in the form [start_time, end_time] with positive time stamps and end_time >= start_time.

  • keys (list) – list key labels (as strings)

class mirdata.annotations.LyricData(intervals, lyrics, pronunciations=None)[source]

LyricData class

Variables
  • intervals (np.ndarray) – (n x 2) array of intervals (as floats) in seconds in the form [start_time, end_time] with positive time stamps and end_time >= start_time.

  • lyrics (list) – list of lyrics (as strings)

  • pronunciations (list or None) – list of pronunciations (as strings)

class mirdata.annotations.MultiF0Data(times, frequency_list, confidence_list=None)[source]

MultiF0Data class

Variables
  • times (np.ndarray) – array of time stamps (as floats) in seconds with positive, strictly increasing values

  • frequency_list (list) – list of lists of frequency values (as floats) in Hz

  • confidence_list (list or None) – list of lists of confidence values between 0 and 1

class mirdata.annotations.NoteData(intervals, notes, confidence=None)[source]

NoteData class

Variables
  • intervals (np.ndarray) – (n x 2) array of intervals (as floats) in seconds in the form [start_time, end_time] with positive time stamps and end_time >= start_time.

  • notes (np.ndarray) – array of notes (as floats) in Hz

  • confidence (np.ndarray or None) – array of confidence values between 0 and 1

class mirdata.annotations.SectionData(intervals, labels=None)[source]

SectionData class

Variables
  • intervals (np.ndarray) – (n x 2) array of intervals (as floats) in seconds in the form [start_time, end_time] times should be positive and intervals should have non-negative duration

  • labels (list or None) – list of labels (as strings)

class mirdata.annotations.TempoData(intervals, value, confidence=None)[source]

TempoData class

Variables
  • intervals (np.ndarray) – (n x 2) array of intervals (as floats) in seconds in the form [start_time, end_time] with positive time stamps and end_time >= start_time.

  • value (list) – array of tempo values (as floats)

  • confidence (np.ndarray or None) – array of confidence values between 0 and 1

mirdata.annotations.validate_array_like(array_like, expected_type, expected_dtype, none_allowed=False)[source]

Validate that array-like object is well formed

If array_like is None, validation passes automatically.

Parameters
  • array_like (array-like) – object to validate

  • expected_type (type) – expected type, either list or np.ndarray

  • expected_dtype (type) – expected dtype

  • none_allowed (bool) – if True, allows array to be None

Raises
  • TypeError – if type/dtype does not match expected_type/expected_dtype

  • ValueError – if array

mirdata.annotations.validate_confidence(confidence)[source]

Validate if confidence is well-formed.

If confidence is None, validation passes automatically

Parameters

confidence (np.ndarray) – an array of confidence values

Raises

ValueError – if confidence are not between 0 and 1

mirdata.annotations.validate_intervals(intervals)[source]

Validate if intervals are well-formed.

If intervals is None, validation passes automatically

Parameters

intervals (np.ndarray) – (n x 2) array

Raises
  • ValueError – if intervals have an invalid shape, have negative values

  • or if end times are smaller than start times.

mirdata.annotations.validate_lengths_equal(array_list)[source]

Validate that arrays in list are equal in length

Some arrays may be None, and the validation for these are skipped.

Parameters

array_list (list) – list of array-like objects

Raises

ValueError – if arrays are not equal in length

mirdata.annotations.validate_times(times)[source]

Validate if times are well-formed.

If times is None, validation passes automatically

Parameters

times (np.ndarray) – an array of time stamps

Raises

ValueError – if times have negative values or are non-increasing

Advanced

mirdata.validate

Utility functions for mirdata

mirdata.validate.log_message(message, verbose=True)[source]

Helper function to log message

Parameters
  • message (str) – message to log

  • verbose (bool) – if false, the message is not logged

mirdata.validate.md5(file_path)[source]

Get md5 hash of a file.

Parameters

file_path (str) – File path

Returns

str – md5 hash of data in file_path

mirdata.validate.validate(local_path, checksum)[source]

Validate that a file exists and has the correct checksum

Parameters
  • local_path (str) – file path

  • checksum (str) – md5 checksum

Returns

  • bool - True if file exists

  • bool - True if checksum matches

mirdata.validate.validate_files(file_dict, data_home, verbose)[source]

Validate files

Parameters
  • file_dict (dict) – dictionary of file information

  • data_home (str) – path where the data lives

  • verbose (bool) – if True, show progress

Returns

  • dict - missing files

  • dict - files with invalid checksums

mirdata.validate.validate_index(dataset_index, data_home, verbose=True)[source]

Validate files in a dataset’s index

Parameters
  • dataset_index (list) – dataset indices

  • data_home (str) – Local home path that the dataset is being stored

  • verbose (bool) – if true, prints validation status while running

Returns

  • dict - file paths that are in the index but missing locally

  • dict - file paths with differing checksums

mirdata.validate.validate_metadata(file_dict, data_home, verbose)[source]

Validate files

Parameters
  • file_dict (dict) – dictionary of file information

  • data_home (str) – path where the data lives

  • verbose (bool) – if True, show progress

Returns

  • dict - missing files

  • dict - files with invalid checksums

mirdata.validate.validator(dataset_index, data_home, verbose=True)[source]

Checks the existence and validity of files stored locally with respect to the paths and file checksums stored in the reference index. Logs invalid checksums and missing files.

Parameters
  • dataset_index (list) – dataset indices

  • data_home (str) – Local home path that the dataset is being stored

  • verbose (bool) – if True (default), prints missing and invalid files to stdout. Otherwise, this function is equivalent to validate_index.

Returns

missing_files (list)

List of file paths that are in the dataset index

but missing locally.

invalid_checksums (list): List of file paths that file exists in the

dataset index but has a different checksum compare to the reference checksum.

mirdata.download_utils

Utilities for downloading from the web.

class mirdata.download_utils.DownloadProgressBar(*_, **__)[source]

Wrap tqdm to show download progress

class mirdata.download_utils.RemoteFileMetadata(filename, url, checksum, destination_dir=None, unpack_directories=None)[source]

The metadata for a remote file

Variables
  • filename (str) – the remote file’s basename

  • url (str) – the remote file’s url

  • checksum (str) – the remote file’s md5 checksum

  • destination_dir (str or None) – the relative path for where to save the file

  • unpack_directories (list or None) – list of relative directories. For each directory the contents will be moved to destination_dir (or data_home if not provieds)

mirdata.download_utils.download_from_remote(remote, save_dir, force_overwrite)[source]

Download a remote dataset into path Fetch a dataset pointed by remote’s url, save into path using remote’s filename and ensure its integrity based on the MD5 Checksum of the downloaded file.

Adapted from scikit-learn’s sklearn.datasets.base._fetch_remote.

Parameters
  • remote (RemoteFileMetadata) – Named tuple containing remote dataset meta information: url, filename and checksum

  • save_dir (str) – Directory to save the file to. Usually data_home

  • force_overwrite (bool) – If True, overwrite existing file with the downloaded file. If False, does not overwrite, but checks that checksum is consistent.

Returns

str – Full path of the created file.

mirdata.download_utils.download_tar_file(tar_remote, save_dir, force_overwrite, cleanup)[source]

Download and untar a tar file.

Parameters
  • tar_remote (RemoteFileMetadata) – Object containing download information

  • save_dir (str) – Path to save downloaded file

  • force_overwrite (bool) – If True, overwrites existing files

  • cleanup (bool) – If True, remove tarfile after untarring

mirdata.download_utils.download_zip_file(zip_remote, save_dir, force_overwrite, cleanup)[source]

Download and unzip a zip file.

Parameters
  • zip_remote (RemoteFileMetadata) – Object containing download information

  • save_dir (str) – Path to save downloaded file

  • force_overwrite (bool) – If True, overwrites existing files

  • cleanup (bool) – If True, remove zipfile after unziping

mirdata.download_utils.downloader(save_dir, remotes=None, partial_download=None, info_message=None, force_overwrite=False, cleanup=False)[source]

Download data to save_dir and optionally log a message.

Parameters
  • save_dir (str) – The directory to download the data

  • remotes (dict or None) – A dictionary of RemoteFileMetadata tuples of data in zip format. If None, there is no data to download

  • partial_download (list or None) – A list of keys to partially download the remote objects of the download dict. If None, all data is downloaded

  • info_message (str or None) – A string of info to log when this function is called. If None, no string is logged.

  • force_overwrite (bool) – If True, existing files are overwritten by the downloaded files.

  • cleanup (bool) – Whether to delete the zip/tar file after extracting.

mirdata.download_utils.extractall_unicode(zfile, out_dir)[source]

Extract all files inside a zip archive to a output directory.

In comparison to the zipfile, it checks for correct file name encoding

Parameters
  • zfile (obj) – Zip file object created with zipfile.ZipFile

  • out_dir (str) – Output folder

mirdata.download_utils.move_directory_contents(source_dir, target_dir)[source]

Move the contents of source_dir into target_dir, and delete source_dir

Parameters
  • source_dir (str) – path to source directory

  • target_dir (str) – path to target directory

mirdata.download_utils.untar(tar_path, cleanup)[source]

Untar a tar file inside it’s current directory.

Parameters
  • tar_path (str) – Path to tar file

  • cleanup (bool) – If True, remove tarfile after untarring

mirdata.download_utils.unzip(zip_path, cleanup)[source]

Unzip a zip file inside it’s current directory.

Parameters
  • zip_path (str) – Path to zip file

  • cleanup (bool) – If True, remove zipfile after unzipping

mirdata.jams_utils

Utilities for converting mirdata Annotation classes to jams format.

mirdata.jams_utils.beats_to_jams(beat_data, description=None)[source]

Convert beat annotations into jams format.

Parameters
  • beat_data (annotations.BeatData) – beat data object

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.chords_to_jams(chord_data, description=None)[source]

Convert chord annotations into jams format.

Parameters
  • chord_data (annotations.ChordData) – chord data object

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.events_to_jams(event_data, description=None)[source]

Convert events annotations into jams format.

Parameters
  • event_data (annotations.EventData) – event data object

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.f0s_to_jams(f0_data, description=None)[source]

Convert f0 annotations into jams format.

Parameters
  • f0_data (annotations.F0Data) – f0 annotation object

  • description (str) – annotation descriptoin

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.jams_converter(audio_path=None, spectrogram_path=None, beat_data=None, chord_data=None, note_data=None, f0_data=None, section_data=None, multi_section_data=None, tempo_data=None, event_data=None, key_data=None, lyrics_data=None, tags_gtzan_data=None, tags_open_data=None, metadata=None)[source]

Convert annotations from a track to JAMS format.

Parameters
  • audio_path (str or None) – A path to the corresponding audio file, or None. If provided, the audio file will be read to compute the duration. If None, ‘duration’ must be a field in the metadata dictionary, or the resulting jam object will not validate.

  • spectrogram_path (str or None) – A path to the corresponding spectrum file, or None.

  • beat_data (list or None) – A list of tuples of (annotations.BeatData, str), where str describes the annotation (e.g. ‘beats_1’).

  • chord_data (list or None) – A list of tuples of (annotations.ChordData, str), where str describes the annotation.

  • note_data (list or None) – A list of tuples of (annotations.NoteData, str), where str describes the annotation.

  • f0_data (list or None) – A list of tuples of (annotations.F0Data, str), where str describes the annotation.

  • section_data (list or None) – A list of tuples of (annotations.SectionData, str), where str describes the annotation.

  • multi_section_data (list or None) – A list of tuples. Tuples in multi_section_data should contain another list of tuples, indicating annotations in the different levels e.g. ([(segments0, level0), ‘(segments1, level1)], annotator) and a str indicating the annotator

  • tempo_data (list or None) – A list of tuples of (float, str), where float gives the tempo in bpm and str describes the annotation.

  • event_data (list or None) – A list of tuples of (annotations.EventData, str), where str describes the annotation.

  • key_data (list or None) – A list of tuples of (annotations.KeyData, str), where str describes the annotation.

  • lyrics_data (list or None) – A list of tuples of (annotations.LyricData, str), where str describes the annotation.

  • tags_gtzan_data (list or None) – A list of tuples of (str, str), where the first srt is the tag and the second is a descriptor of the annotation.

  • tags_open_data (list or None) – A list of tuples of (str, str), where the first srt is the tag and the second is a descriptor of the annotation.

  • metadata (dict or None) – A dictionary containing the track metadata.

Returns

jams.JAMS – A JAMS object containing the annotations.

mirdata.jams_utils.keys_to_jams(key_data, description)[source]

Convert key annotations into jams format.

Parameters
  • key_data (annotations.KeyData) – key data object

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.lyrics_to_jams(lyric_data, description=None)[source]

Convert lyric annotations into jams format.

Parameters
  • lyric_data (annotations.LyricData) – lyric annotation object

  • description (str) – annotation descriptoin

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.multi_sections_to_jams(multisection_data, description)[source]

Convert multi-section annotations into jams format.

Parameters
  • multisection_data (list) – list of tuples of the form [(SectionData, int)]

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.notes_to_jams(note_data, description)[source]

Convert note annotations into jams format.

Parameters
  • note_data (annotations.NoteData) – note data object

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.sections_to_jams(section_data, description=None)[source]

Convert section annotations into jams format.

Parameters
  • section_data (annotations.SectionData) – section data object

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.tag_to_jams(tag_data, namespace='tag_open', description=None)[source]

Convert lyric annotations into jams format.

Parameters
  • lyric_data (annotations.LyricData) – lyric annotation object

  • namespace (str) – the jams-compatible tag namespace

  • description (str) – annotation descriptoin

Returns

jams.Annotation – jams annotation object.

mirdata.jams_utils.tempos_to_jams(tempo_data, description=None)[source]

Convert tempo annotations into jams format.

Parameters
  • tempo_data (annotations.TempoData) – tempo data object

  • description (str) – annotation description

Returns

jams.Annotation – jams annotation object.