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This is a project to synthesize large amounts of MIDI data into audio files (1000s of hours). It is built with the Lakh MIDI Dataset v0.1 (LMD). We call this Synthesized Lakh, or Slakh for short. This repository contains everything you need to use Native Instruments' Kontakt or other VSTs to synthesize large amounts of musical data.

A full dataset of synthesized data is available for download at the Slakh project website.

Table of Contents

  1. Overview
  2. About the Slakh Dataset
  3. License and Attribution
  4. Generating Data
     a. Step 1: Setting up RenderMan on Mac
     b. Step 2: Install/Setup Kontakt and/or other Synthesis VSTs
     c. Step 3: Set up the json files
     c. Step 4: Running data generation script
  5. Flakh & using FluidSynth
  6. Gotchas


This repository contains all of the code, config files, and patches used to create the original Slakh2100 dataset. The details regarding all of the settings used to make Slakh2100 are included in the file included in this repo. This guide assumes you have downloaded the Lakh MIDI Dataset (LMD) prior to using it.

At the base of creating Slakh is a script called, which looks for MIDI files that match a user-definable "band" definition, synthesizes each instrument separately using a VST host, and then mixes the resultant audio to have equal loudness according to the ITU-R BS.1770-4 loudness standard. All audio is synthesized to .wav files and is output with the same directory structure as Slakh. See the Slakh utils repo for more info.

The VST host used is called RenderMan. RenderMan is a C++ VST host (built on JUCE) that has python bindings. The source code is included here (in RenderMan-master/). The Linux and Mac versions of RenderMan both work, but Kontakt only runs on Mac and Windows. So this project is Mac only!!!

About 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.

Slakh is brought to you by Mitsubishi Electric Research Lab (MERL) and the Interactive Audio Lab at Northwestern University. For more info, please visit the Slakh website.

License and Attribution

If you use Slakh2100 or generate data using this method we ask that you cite it using the following bibtex entry:

  title={Cutting Music Source Separation Some {Slakh}: A Dataset to Study the Impact of Training Data Quality and Quantity},
  author={Manilow, Ethan and Wichern, Gordon and Seetharaman, Prem and Le Roux, Jonathan},
  booktitle={Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},

Slakh2100 and Flakh2100 are licensed under a Creative Commons Attribution 4.0 International License.

This code is licensed under an MIT license.

Generating Data

Step 1: Setting up RenderMan on Mac

RenderMan originally comes from here, but the version used here is based on a forked version that allows reading of entire MIDI files at once. That's from here. Our modified version is included in this repo (at RenderMan-master/). (Both linked versions RenderMan are in the public domain via an "unlicense".)

  • Open the RenderMan XCode project (RenderMan-master/Builds/MacOSX/RenderMan.xcodeproj), it should be configured correctly to build.

  • One common issue we ran into is not having Boost python installed or set up correctly (bindings between C++ and python). The easiest way to install it is with homebrew; make sure you get the python2 version (not sure if RenderMan has been tested with python3). The command is just brew install boost-python. IIRC, boost-python points to bindings for python2, and boost-python3 points to bindings for python3. We want python2 bindings, as I was unable to get python3 working with this old codebase (sorry! I know it's 2020 already!). You'll also need to know the location of both boost and boost-python. Do this by running brew info boost and brew info boost-python.

  • Then you need to make sure the XCode build settings are correct. The most common issue we encountered was a linker issue--making sure that boost-python was able to find and build against the system's python dynamic libraries. To fix this, check your build settings in XCode. Double check that the build target is a dynamic library, and under the 'Target' build settings look for the 'Linking' heading. Make sure 'Other Linker Flags' has the following setting: -shared -lpython2.7 -lboost_python27. You might need to set 'Other Linker Flags' to -shared -lpython -lboost_python27 (note the difference between -lpython and -lpython2.7), depending on how boost is installed. If this doesn't work, make sure the linker's search paths are set correctly under 'Search Paths' > 'Library Search Paths' on the same build page. I had to add the paths from boost and boost-python to get it to build (/usr/local/Cellar/boost-python/1.72.0/lib/**, and /usr/local/Cellar/boost/1.72.0/include/boost/** for me, as well as the $(inherited) "/usr/local/lib" path that's included).

(Note: I was able to build with XCode 10.2.1 on OS X 10.14.6, and boost/boost-python 1.72.0)

  • Press [cmd]-[b] to build. Make sure to build in Debug mode. Currently this is hard coded.

  • If it builds successfully, you should have a file called (in RenderMan-master/Builds/MacOSX/build/Debug/). Rename that file to And now RenderMan is set up!

See Gotchas below if you get this error: __init__() should return None, not 'NoneType'.

Details for how to do to other things with RenderMan are provided in the RenderMan folder or in the RenderMan repos. These details are not required to continue working with this guide.

Step 2: Install/Setup Kontakt and/or other Synthesis VSTs

See Native Instruments' website for details on installing Kontakt. I don't believe there are any restrictions on which Kontakt build version to use, because they all conform to the VST API.

We used Kontakt Komplete 12, but any version of Kontakt with some instrument packs will work. You will need to know the absolute path to the Kontakt.component, and the path to any other .component files for every VST that you use. (Usually found in /Library/Audio/Plug-Ins/Components/)

If you don't want to shell out $_$ before testing this project, you can download a set of free Kontakt patches here and get the Kontakt 6 player here. I have also included a set of config files and nkms (labeled "Factory") that will work with the free version of Kontakt.

Step 2a: Make Kontakt patches/presets

The process for making Kontakt patches is a little involved so I'll outline it here. This doesn't apply for other VSTs, which might have their own method for storing and recalling presets.

Using Kontakt Player 6:

  1. Open Kontakt, find a sampler/patch that you like.
  2. Click the floppy disk icon in the top bar.
  3. Select "Save Multi as...". Note this will save all of the .nki instruments and settings currently loaded.
  4. Save. Keep track of this location.

Here's a gif of this process:

Saving an nkm file

NOTE: Slakh used Kontakt Komplete 12 (2018), which may have patches (.nkm files) that might not be present on your system. Make sure to check prior to running. These are labeled komplete.

Step 3: Set up the json files

Step 3a: Instrument Definition Metadata File

There are two json files that you'll need to set up to run the script.

The first is the Instrument Definition Metadata File (the "defs_metadata_file" in the config json file). This file defines how MIDI Instrument program numbers get mapped to Kontakt or VST patches.


  "{{Instrument Name}}" : {
    "class": "{{Instrument MIDI Class}}",
    "program_numbers" : [0, 1, 2, 3, 4], # MIDI Instrument program numbers
    "defs": [    # List of nkm files or paths to .component files
      "{{Kontakt patch}}.nkm",
      "{{another Kontakt patch}}.nkm",
      "{{absoulte path to a VST}}.component",
      "{{another absoulte path to a VST}}.component"

"program_numbers: A list of MIDI Instrument program numbers for this patch. See general_midi_inst_*based.txt for a readable layout of MIDI Instrument program numbers. Any time a number from this list is seen, a random VST from the "defs" list will be selected for synthesis.

"defs": A list containing either ".nkm" filenames (from the user_nkms_dir specified in config.json), or absolute paths to VST .component files.

Example from komplete_strict.json:

  "Acoustic Piano" : {
    "class": "Piano",
    "program_numbers" : [0, 1, 2, 4, 7],
    "defs": [

All entries in "defs": [] that end with .nkm will be loaded with Kontakt, and everything else will be loaded as a regular VST. Loading presets with other VSTs is highly dependent on how those VSTs work. As of right now, this functionality does not work. However, RenderMan does provide an interface for changing VST parameters programmatically, but the interface is highly specific to each VST. For generating Slakh, we saved presets for Kontakt (the .nkm files) and loaded those. For more information setting VST parameters see the RenderMan functions get_plugin_parameters_description() and set_parameter() or reach out to me.

It should also be noted that Drums are represented here as MIDI program number 128 (0-based), or MIDI program number 129 (1-based). Within MIDI, drum tracks have a special flag that is separate from their instrument program number, as such to keep consistency with the other program numbers throughout this script, they have tacked onto the end of the list as such.

All 129 program numbers should be assigned to something. If there are patches you do not want to synthesize, they should be represented here with an empty list for "defs".

There are two provided example instrument definition files:

  1. komplete_lax.json maps MIDI instrument program numbers to patches along the separations defined in the MIDI spec (as seen in general_midi_inst_0.txt), with little thought given to more granular splitting. In other words, all guitar instrument patches (acoustic guitars, clean electric guitars, and distorted electric guitars) get mapped to the same patch.

  2. komplete_strict.json does a more keen segmentation of different instrument types, splitting up MIDI instrument program numbers into logical groups. For instance, acoustic guitars, clean electric guitars, and distorted electric guitars are all separately rendered patches.

These two files require Kontakt Komplete 12 to work, but if you don't have that you can use them as a guide for creating your own instrument definitions.

Step 3b: Band Definition file and MIDI file lists

The way this script selects which MIDI files to render happens in two ways, by providing a Band Definition file and/or a MIDI file list.

A Band definition file is a json file that determines if a MIDI file is okay to synthesize. If a candidate MIDI file has at least one of each instrument defined in this file then will be selected to be synthesized. Here's an example from band_defs/rock_band.json:

{ "key": "class",

The first line "key": "class" is fixed (for now, possibly extensible in the future). The list "band_def determines which MIDI instrument classes a MIDI file must have to be selected to synthesize.

The second way is by providing a MIDI file list. This is a text file that contains a list of absolute paths to MIDI files (one per line). If this is provided, the script will not traverse the LMD to find MIDI file candidates, but rather will look through the provided list. This is nice if you need to rerender a specific set of files for some reason.

These two methods are not mutually exclusive. You can use both simultaneously or neither.

Step 3c: config.json

The second json file is config.json. This handles all of the parameters that get sent to the main script. Here is an annotated list of all of the variables:

  • lmd_base_dir: Absolute path to base directory of the Lakh MIDI Dataset. (str)
  • kontakt_path: Absolute path to the .component for Kontakt. Usually "/Library/Audio/Plug-Ins/Components/Kontakt.component". (str)
  • kontakt_defs_dir: Absolute path to the default Kontakt patch location. Usually "/Users/{{username}}/Library/Application Support/Native Instruments/Kontakt/default". (str)
  • user_nkms_dir: Path to directory containing user defined patches (.nkm files created in step 2a, above). (str)
  • instrument_classes_file: Path to json file that defines MIDI program number and classes. Default general_midi_inst_0.json. (str)
  • defs_metadata_file: Path to your Instrument Definition Metadata File as defined above. (str)
  • output_dir: Absolute path to a base directory where audio will be output to. (str)
  • renderman_sr: Sample rate that the audio will be synthesized at. (44.1kHz) (int)
  • renderman_buf: Buffer size for the hosted VSTs. (int)
  • renderman_sleep: Sleep time (in seconds) after a VST is loaded. Kontakt needs time to load samples into memory. See info in the gotchas below (float)
  • renderman_restart_lim: The RenderMan engine can be glitchy (see below). This number is the of stem files RenderMan will make before restarting. See info in the gotchas below (int)
  • random_seed: Random seed for selecting MIDI files from the LMD. (int)
  • max_num_files: Total number of audio mixtures to generate. (int)
  • separate_drums: If true and if a MIDI file has multiple tracks for drums this will render them all separately, else files like these get skipped. (bool)
  • mix_normalization_factor: Value (in dB) that each track is normalized to according to ITU-R BS.1770-4. (float)
  • mix_target_peak: Value (in dB) that the mix is normalized to once all the stems are summed together. (float)
  • render_pgm0_as_piano: If true, MIDI program number 0 is interpreted as piano. Useful when using 1-based MIDI. (bool)
  • rerender_existing: If true, will overwrite existing audio files that have been synthesized. Else, will skip files if they've been seen before. Useful for restarting from a previously crashed session. (str)
  • band_definition_file: A path to a json file containing a band definition file. See step 3b for more details. Can be null. (str)
  • midi_file_list: A path to a text file with one MIDI file absolute path (from the LMD) per line. See step 3b for more details. Can be null. (str)
  • zero_based_midi: If true, will read MIDI program numbers as 0-based. (bool)
  • logfile_basename: Base file name containing the program's logs. (str)

Step 4: Running data generation script

Step 4a: Install python script requirements

Before you can run the script you need to install all of the required python libraries. As usual, I recommend you make a new conda environment to run this. You can install of the requirements by running pip install -r requirements.txt in the command line, where requirements.txt is included in this repo.

Step 4b: The data generation script

The data generation script is Call it like so:

python --config-file path/to/config.json

All of the configurations for generation are defined in the json file, as formatted above.

Note: This hasn't been tested with python 3 yet, so use python 2. (Sorry, but our dependencies are old!)

How this script works

At a high level, this script works in three stages.

  1. First it reads the MIDI files (either from the LMD or from a provided list of MIDI file paths) and splits each multi-track MIDI file into separate MIDI files for each track, with each resultant MIDI file containing only one instrument. At this stage, it randomly assigns a patch to each track based on the mapping defined in your Instrument Definition Metadata File (as described above).

  2. In the second stage, the script loads up a single instrument patch and generates audio for every MIDI track that has been assigned to that patch, regardless of which original MIDI file it came from. This is done such that only one VST is loaded at a time.

  3. The final stage is the mixing stage. Each track has a gain applied such that they all have equal loudness according to ITU-R BS.1770 (the loudness, in dB, is set in config.json). Then each track is summed to make an instantaneous mixture. The peak of that mixture is calculated and if it is above target_peak (in dB, from config.json), the gain of the mixture and each track are lowered to match target_peak.

Each of these three stages is a giant function, these functions talk via dicts that collect the required info for the next stage.

Script output

The script outputs mixes, isolated audio tracks for each instrument, the original MIDI file, a set of split MIDI files for each instrument, and a yaml metadata file. All of these things are in a dedicated folder for every song. The script also outputs a (very large) log file where you can track the progress of the script. All audio is output as uncompressed wav files. For batch compression of the audio to flac, see the Slakh utils repo for more info.

Remarks on running time and storage

This script takes quite a while to run. We used a 2015 Macbook Pro 13" with modest specs and it took on the order of days to generate 2100 mixes. (We had some storage problems, so I can't say for certain the exact run time). All in all, I would not be surprised if it took a week or more.

On the subject of storage: we used a dedicated machine for this and network storage for the output. The machine itself had 500Gb of storage and most of it was dedicated to storing the large amount of sample data from Kontakt. The output of the script was another ~500Gb.

Flakh & using FluidSynth

Currently, I am not releasing the code to create Flakh, which is the accompanying dataset to Slakh that is the same MIDI files generated with FluidSynth. This is simple to do, but is a separate process entirely. If you need to generate Flakh, please contact me and I will provide scripts.


1-based vs 0-based
The official MIDI specification is 1-based but many but not all MIDI files in the LMD are 0-based. There is a switch in config.json to swap between parsing 1-based and 0-based MIDI files. 0-based is a good setting for most use cases.

I recommend NOT parallelizing this script as loading many patches at once is quite resource intensive. If you are planning on synthesizing many hours of audio give yourself time! That being said, I did try a few ways to speed this up and the three stage process outlined above seems best, but if you do figure out a more efficient way to parallelize this type of data generation, please let me know!

Restarting the Engine
The RenderMan engine is finicky. It can sometimes fail silently in that it does not report any issues, but it outputs waveforms of all 0's without notice. There are some checks for this in the code, but there is also a hard restart built into the code. I.e., the script will restart a new RenderMan engine after it synthesizes every n tracks (as defined by renderman_restart_lim above). The engine restart seems to help this problem, but if you are experiencing issues play around with this number. I don't know if the source of this problem stems from RenderMan or the VSTs getting hosted (Kotakt, etc). But I doubt the Native Instruments engineers planned for the type of massive processing of MIDI files that occurs with this project.

Loading the Engine
Some VSTs need a few seconds before they can accept note data to synthesize to audio. For instance, Kontakt needs to load gigabytes of sample data into memory. Therefore, there is a sleep timer set when a VST is initially loaded to let the VST completely load. If you are having trouble, open up your VST with a full DAW (Logic, Ableton, etc) and time it a few times to make sure you have given it enough sleep time.

Error when running: __init__() should return None, not 'NoneType'
This is a linker issue. The python that you built RenderMan against is different than the python you're running the script with. The easiest way to fix this is to use install_name_tool. I was able to run the following command to get RenderMan working: install_name_tool -change /System/Library/Frameworks/Python.framework/Versions/2.7/Python /Users/{{MAC_USERNAME}}/anaconda2/envs/slakh_gen/lib/libpython2.7.dylib from the RenderMan-master build directory, where the path is to the libpython2.7.dylib of your desired conda environment. For more info see this github post or this one.


A project to synthesize massive amounts of multitrack audio data from MIDI.







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