Utilities for common tasks with the Slakh dataset.
The Synthesized Lakh (Slakh) Dataset is a new dataset for audio source separation that is synthesized from the Lakh MIDI Dataset v0.1 using professional-grade sample-based virtual instruments. Slakh2100 contains 2100 automatically mixed tracks and accompanying MIDI files synthesized using a professional-grade sampling engine. The tracks in Slakh2100 are split into training (1500 tracks), validation (375 tracks), and test (225 tracks) subsets, totaling 145 hours of mixtures.
Table of Contents
- At a Glance
- Setting Up Utils
- Converting to/from .flac
- Make Splits
- Making Submixes
- Mixing to Replicate Benchmark Experiments
At a Glance
- The dataset comes as a series of directories named like
XXXXXis a number between
02100. This number is the ID of the track.
- The directory structure looks like this:
Track00001 └─── all_src.mid └─── metadata.yaml └─── MIDI │ └─── S01.mid │ │ ... │ └─── SXX.mid └─── mix.flac └─── stems └─── S01.flac │ ... └─── SXX.flac
all_src.mid is the original MIDI file from Lakh that contains all of the sources.
metadata.yaml contains metadata for this track (see below.)
MIDI contains MIDI files separated by each instrument, the file names correspond with the stems.
mix.flac is the mixture, made my summing up all of the audio in the
stems contains isolated audio for each source in the mixture.
- All audio in Slakh2100 is distributed in the
.flacformat. (Scripts to batch convert below.)
- All audio is mono and was rendered at 44.1kHz, 16-bit (CD quality) before being converted to
- Slakh2100 is distributed as a tarball (
.tar.gz) that must be uncompressed prior to using any of these scripts.
- Each mixture has a variable number of sources, with a minimum of 4 sources per mix.
metadata.yamlhas information about each source.
- The sources have no guaranteed ordering. See
metadata.yamlto determine which sources are which.
- The MIDI for source XX in
all_src.midis not guaranteed to match
SXX.mid, as some instrument-specific heuristics were applied to the MIDI to ensure proper synthesis.
SXX.midis the exact file used to synthesize
SXX.flac, whereas entry for source XX in
all_src.midis a direct copy from Lakh.
- Some MIDI files are replicated (due to a bug). This has can be ameliorated depending on your use case. See Make Splits below.
All metadata is distributed as
yaml files and are similar in structure to MedleyDB's metadata files.
Here is an annotated overview of what is in the metadata files for these tracks. Annotations are in parentheses after each entry below:
UUID: 1a81ae092884234f3264e2f45927f00a (File name of the original MIDI file from Lakh, sans extension) audio_dir: stems (Directory name of where the stems are stored, will always be "stems") lmd_midi_dir: lmd_matched/O/O/H/TROOHTB128F931F9DF/1a81ae092884234f3264e2f45927f00a.mid (Path to the original MIDI file from a fresh download of Lakh) midi_dir: MIDI (Directory name of where the separated MIDI files are stored, will always be "MIDI") normalization_factor: -13.0 (target normalization [dB] for all stems before lowering gain to avoid clipping, will always be "-13.0") normalized: true (whether the mix and stems were normalized according to the ITU-R BS.1770-4 spec) overall_gain: 0.18270259567062658 (gain applied to every stem to make sure mixture does not clip when stems are summed) stems: S00: (name of the source on disc, so this is "stems/S01.flac") audio_rendered: true (whether the audio was rendered, some rare sources are skipped) inst_class: Guitar (MIDI instrument class) integrated_loudness: -12.82074180245363 (integrated loudness [dB] of this track as calculated by the ITU-R BS.1770-4 spec) is_drum: false (whether the "drum" flag is true for this MIDI track) midi_program_name: Distortion Guitar (MIDI instrument program name) midi_saved: true (whether the separate MIDI track was saved for this stem) plugin_name: elektrik_guitar.nkm (patch/plugin name that rendered this audio file) program_num: 30 (MIDI instrument program number) S01: ... S02: ... ... S10: ... target_peak: -1.0 (target peak [dB] when applying gain to all stems after summing mixture, will always be "-1.0")
For a list of the MIDI program numbers and their organization see these files.
Setting up utils
Before you can use any of the utils, you need python3 installed on your machine. It is recommended to use a new virtual environment or anaconda environment. Then download or clone the code in this repository and install the required packages like so:
$ pip install -r requirements.txt
All of the audio in Slakh2100 comes compressed as .flac files. To convert every .flac file to .wav
(and vice versa), use the script provided in the
conversion/ directory called
This script outputs a copy of the input Slakh with the .flac files converted to .wav files (or vice versa). It does not do the conversion in place! There is a toggle to determine whether you want to compress (to .flac) or decompress (to .wav) the audio within Slakh, and there is also an option to multithread this process. See below for all options.
$ python flac_converter.py -i /path/to/flac/Slakh2100 -o /output/path/Slakh2100_wav -c False
Full usage details:
$ python flac_converter.py [-h] --input-dir INPUT_DIR --output-dir OUTPUT_DIR --compress COMPRESS [--start START] [--end END] [--num-threads NUM_THREADS] [--verbose VERBOSE] arguments: -h, --help show this help message and exit --input-dir INPUT_DIR, -i INPUT_DIR Base path to input directory. (Required) --output-dir OUTPUT_DIR, -o OUTPUT_DIR Base path to output directory. (Required) --compress COMPRESS, -c COMPRESS If true, will convert from .wav to .flac, elsewill convert from .flac to .wav. (Required) --start START, -s START If converting a subset, the lowest Track ID. (Optional) --end END, -e END If converting a subset, the highest Track ID. (Optional) --num-threads NUM_THREADS, -t NUM_THREADS Number of threads to spawn to convert. (Optional) --verbose VERBOSE, -v VERBOSE Whether to print messages while processing. (Optional)
((Documentation coming soon, for now look at the script in the
The original Slakh2100 was found to have many duplicate MIDI files, some MIDI duplicates that are present in more than one of the train/test/validation splits. Even though each song is rendered with a random set of synthesizers, the same versions of the songs appear more than once. Same MIDI, different audio files. Still this can be an issue for some uses of Slakh2100.
To that end, we are releasing two additional ways to split Slakh2100. Here's the information about all of the Slakh2100 splits. We ask that if you use Slakh2100 in a research paper, that you conform to this naming convention for easier reproducibility.
Slakh2100-orig: This is the name for the original splits of the Slakh2100 dataset.
Slakh2100-split2: This is the name for the new split that still contains all 2100 tracks. This new split
moves all tracks with duplicated MIDI files such that no duplicated MIDI files are in more than one split.
There are still 1500 train tracks, 375 validation tracks, and 225 test tracks. For this configuration, use
splits_v2.json with the script below.
Slakh2100-redux: This is the name for the new split that does not contain all 2100 tracks. This omits
tracks such that each MIDI file only occurs once. As such, there are 1710 total tracks, 1289 in train,
270 in validation, and 151 in test. For this configuration, use
redux.json with the script below.
All of the code and json data are in the
splits/ directory of this repository.
We have included a json file that links tracks to their MIDI duplicates at
Additionally, we have included a script that will convert from
It will also convert
Slakh2100-redux back to
Slakh2100-orig using the
Slakh2100-redux, or vice versa, you must first convert to
usage: resplit_slakh.py [-h] --slakh-dir SLAKH_DIR [--split-file SPLIT_FILE] [--reset] optional arguments: -h, --help show this help message and exit --slakh-dir SLAKH_DIR, -d SLAKH_DIR Base directory of Slakh2100 (with train/val/test subdirectories) --split-file SPLIT_FILE, -s SPLIT_FILE Path to a json file containing split data. Either splits_v2.json or redux.json --reset, -r Reset Slakh2100 directory to original splits.
$python resplit_slakh.py -d /path/to/slakh2100/ -s split_v2.json
$python resplit_slakh.py -d /path/to/slakh2100/ -s redux.json
Slakh2100-redux back to
$python resplit_slakh.py -d /path/to/slakh2100/ -r
This is a script that makes submixes by combining sets of instruments within a mix. It is possible to define customizable submixes by providing a submix definition file. A few examples of submix definition files are provided here.
To use this script you can either provide the base path to all of Slakh to make submixes for every track,
or you can provide it a single track to make a submix for only the provided track. Submix output
is put into the
TrackXXXXX/stems/ directory with the name of the submix definition file. For example,
for a submix definition file named
band.yaml, the output of this script will go into
Full usage details:
$ python submixes.py [-h] -submix-definition-file SUBMIX_DEFINITION_FILE [-input-dir INPUT_DIR] [-src-dir SRC_DIR] [-num-threads NUM_THREADS] arguments: -h, --help show this help message and exit -submix-definition-file SUBMIX_DEFINITION_FILE, -s SUBMIX_DEFINITION_FILE Path to yaml file to define a submix. -input-dir INPUT_DIR, -i INPUT_DIR Base directory to apply a submix to the whole dataset. -src-dir SRC_DIR, -s SRC_DIR Directory of a single track to create a submix for -num-threads NUM_THREADS, -t NUM_THREADS Number of threads to spwan to do the submixing.
The submix definition files are
yaml files that contain "recipes" about which types of isolated sources get
mixed together into submix sources. Inside the yaml file is a dictionary that contains two elements,
Mixing Key and the
Mixing key tells the script what part of the metadata to look at to define submix
sources (that get mixed together). Options for
Mixing key could technically be any entry under the
source name in
metadata.yaml, but most common
Mixing keys are (ordered from most to least
program_num (equivalent to
Recipes list is a list of dictionaries that define what isolated sources get mixed into submix
sources. The key of each dictionary is the name of the source (and its name on disc after the script is run),
and the value is a list of possible entries that make the source. Everything that is encountered that
isn't defined by a recipe will be put into a file called
Here's an example. If we want to make a submix with every piano except harpsichord and clavinet, first we look at the MIDI instrument chart:
- Piano 0 Acoustic Grand Piano 1 Bright Acoustic Piano 2 Electric Grand Piano 3 Honky-tonk Piano 4 Electric Piano 1 5 Electric Piano 2 6 Harpsichord 7 Clavinet - Chromatic Percussion 8 Celesta 9 Glockenspiel 10 Music Box ...
- Piano defines the MIDI instrument class (
inst_class in the metadata), and the eight values
below it are the instrument program number (
program_num) and instrument program name (
So it looks like we want MIDI program numbers [0-5]. So we set our mixing key to
make a recipe called
Piano with our values:
Mixing key: "program_num" Recipes: Favorite Piano Sounds: - 0 - 1 - 2 - 3 - 4 - 5
Let's name this file
my_pianos.yaml. When give this submix definition to
submixes.py it will
make a new folder in the
stems directory of every track called
will be a file called
favorite_piano_sounds.wav containing every track that has those MIDI instrument
values and another file called
residuals.wav containing everything else.
Mixing to Replicate Benchmark Experiments
((Documentation and code coming soon.))