Ultra-fast and flexible GPU/CPU MIDI-to-MIDI similarity calculation, search and analysis
Quality pre-trained models and comprehensive pre-computed embeddings sets
Stand-alone, versatile, and extensive codebase for general or custom MIDI-to-MIDI similarity tasks
Full cross-platform compatibility and support

Pre-trained models

midisim_small_pre_trained_model_2_epochs_43117_steps_0.3148_loss_0.9229_acc.pth - Very fast and accurate small model, suitable for all tasks. This model is included in PyPI package or it can be downloaded from Hugging Face
midisim_large_pre_trained_model_2_epochs_86275_steps_0.2054_loss_0.9385_acc.pth - Fast large model for more nuanced embeddings generation. Download checkpoint from Hugging Face

Both pre-trained models were trained on full Godzilla Piano dataset for 2 complete epochs

Pre-computed embeddings sets

For small pre-trained model

Mean Pool Embeddings

These are standard general purpose embeddings which work best for comparing overall MIDI-to-MIDI similarity

discover_midi_dataset_37292_genres_midis_embeddings_cc_by_nc_sa.npy - 37292 genre MIDIs embeddings for genre (artist and song) identification tasks

discover_midi_dataset_202400_identified_midis_embeddings_cc_by_nc_sa.npy - 202400 identified MIDIs embeddings for MIDI identification tasks

discover_midi_dataset_3480123_clean_midis_embeddings_cc_by_nc_sa.npy - 3480123 select clean MIDIs embeddings for large scale similarity search and analysis tasks

Weighted Mean Pool Embeddings

These embeddings work best for comparing MIDI-tp=MIDI music structure (MIDI pitches) similarity.

discover_midi_dataset_37302_genre_midis_embeddings_1_1_2_weighted_cc_by_nc_sa.npy - 37302 genre MIDIs weighted embeddings for genre (artist and song) identification tasks

discover_midi_dataset_190032_identified_midis_embeddings_1_1_2_weighted_cc_by_nc_sa.npy - 190032 identified MIDIs weighted embeddings for MIDI identification tasks

discover_midi_dataset_3480123_clean_midis_embeddings_1_1_2_weighted_cc_by_nc_sa.npy - 3480123 select clean MIDIs weighted embeddings for large scale similarity search and analysis tasks

For large pre-trained model

Mean Pool Embeddings

These are standard general purpose embeddings which work best for comparing overall MIDI-to-MIDI similarity

discover_midi_dataset_37303_genres_midis_embeddings_large_cc_by_nc_sa.npy - 37303 genre MIDIs embeddings for genre (artist and song) identification tasks

discover_midi_dataset_202400_identified_midis_embeddings_large_cc_by_nc_sa.npy - 202400 identified MIDIs embeddings for MIDI identification tasks

discover_midi_dataset_3480123_clean_midis_embeddings_large_cc_by_nc_sa.npy - 3480123 select clean MIDIs embeddings for large scale similarity search and analysis tasks

Weighted Mean Pool Embeddings

These embeddings work best for comparing MIDI-tp=MIDI music structure (MIDI pitches) similarity.

discover_midi_dataset_37287_genres_midis_embeddings_1_1_2_weighted_large_cc_by_nc_sa.npy - 37287 genre MIDIs weighted embeddings for genre (artist and song) identification tasks

discover_midi_dataset_190032_identified_midis_embeddings_1_1_2_weighted_large_cc_by_nc_sa.npy - 190032 identified MIDIs weighted embeddings for MIDI identification tasks

discover_midi_dataset_3480123_clean_midis_embeddings_1_1_2_weighted_large_cc_by_nc_sa.npy - 3480123 select clean MIDIs weighted embeddings for large scale similarity search and analysis tasks

Source MIDI dataset: Discover MIDI Dataset

Similarity search output samples

midisim-similarity-search-output-samples-CC-BY-NC-SA.zip - ~300000 MIDIs indentified with midisim music discovery pipeline with both pre-trained models

midisim-similarity-search-output-samples-1-1-2-weighted-CC-BY-NC-SA.zip - ~366000 MIDIs indentified with weighted midisim music discovery pipeline with both pre-trained models

Source MIDI dataset: Discover MIDI Dataset

Installation

midisim PyPI package (for general use)

!pip install -U midisim

x-transformers 2.3.1 (for raw/custom tasks)

!pip install x-transformers==2.3.1

Basic use guide

General use example

python

# ================================================================================================
# Initalize midisim
# ================================================================================================

# Import main midisim module
import midisim

# ================================================================================================
# Prepare midisim embeddings
# ================================================================================================

# Option 1: Download sample pre-computed embeddings corpus from Hugging Face
emb_path = midisim.download_embeddings()

# Option 2: use custom pre-computed embeddings corpus
# See custom embeddings generation section of this README for details
# emb_path = './custom_midis_embeddings_corpus.npy'

# Load downloaded embeddings corpus
corpus_midi_names, corpus_emb = midisim.load_embeddings(emb_path)

# ================================================================================================
# Prepare midisim model
# ================================================================================================

# Option 1: Download main pre-trained midisim model from Hugging Face
model_path = midisim.download_model()

# Option 2: Use main pre-trained midisim model included in midisim PyPI package
# model_path = midisim.get_package_models()[0]['path']

# Load midisim model
model, ctx, dtype = midisim.load_model(model_path)

# ================================================================================================
# Prepare source MIDI
# ================================================================================================

# Load source MIDI
input_toks_seqs = midisim.midi_to_tokens('Come To My Window.mid')

# ================================================================================================
# Calculate and analyze embeddings
# ================================================================================================

# Compute source/query embeddings
query_emb = midisim.get_embeddings_bf16(model, input_toks_seqs)

# Calculate cosine similarity between source/query MIDI embeddings and embeddings corpus
idxs, sims = midisim.cosine_similarity_topk(query_emb, corpus_emb)

# ================================================================================================
# Processs, print and save results
# ================================================================================================

# Convert the results to sorted list with transpose values
idxs_sims_tvs_list = midisim.idxs_sims_to_sorted_list(idxs, sims)

# Print corpus matches (and optionally) convert the final result to a handy list for further processing
corpus_matches_list = midisim.print_sorted_idxs_sims_list(idxs_sims_tvs_list, corpus_midi_names, return_as_list=True)

# ================================================================================================
# Copy matched MIDIs from the MIDI corpus for listening and further evaluation and analysis
# ================================================================================================

# Copy matched corpus MIDI to a desired directory for easy evaluation and analysis
out_dir_path = midisim.copy_corpus_files(corpus_matches_list)

# ================================================================================================

Raw/custom use example

Small model (8 layers - 2 epochs)

python

import torch
from x_transformers import TransformerWrapper, Encoder

# Original model hyperparameters
SEQ_LEN = 3072

MASK_IDX     = 384 # Use this value for masked modelling
PAD_IDX      = 385 # Model pad index
VOCAB_SIZE   = 386 # Total vocab size

MASK_PROB    = 0.15 # Original training mask probability value (use for masked modelling)

DEVICE = 'cuda' # You can use any compatible device or CPU
DTYPE  = torch.bfloat16 # Original training dtype

# Official main midisim model checkpoint name
MODEL_CKPT = 'midisim_small_pre_trained_model_2_epochs_43117_steps_0.3148_loss_0.9229_acc.pth'

# Model architecture using x-transformers
model = TransformerWrapper(
    num_tokens = VOCAB_SIZE,
    max_seq_len = SEQ_LEN,
    attn_layers = Encoder(
        dim   = 512,
        depth = 8,
        heads = 8,
        rotary_pos_emb = True,
        attn_flash = True,
    ),
)

model.load_state_dict(torch.load(MODEL_CKPT, map_location=DEVICE))

model.to(DEVICE)
model.eval()

# Original training autoxast setup
autocast_ctx = torch.amp.autocast(device_type=DEVICE, dtype=DTYPE)

Large model (16 layers - 2 epochs)

python

import torch
from x_transformers import TransformerWrapper, Encoder

# Original model hyperparameters
SEQ_LEN = 3072

MASK_IDX     = 384 # Use this value for masked modelling
PAD_IDX      = 385 # Model pad index
VOCAB_SIZE   = 386 # Total vocab size

MASK_PROB    = 0.15 # Original training mask probability value (use for masked modelling)

DEVICE = 'cuda' # You can use any compatible device or CPU
DTYPE  = torch.bfloat16 # Original training dtype

# Official main midisim model checkpoint name
MODEL_CKPT = 'midisim_large_pre_trained_model_2_epochs_86275_steps_0.2054_loss_0.9385_acc.pth'

# Model architecture using x-transformers
model = TransformerWrapper(
    num_tokens = VOCAB_SIZE,
    max_seq_len = SEQ_LEN,
    attn_layers = Encoder(
        dim   = 512,
        depth = 16,
        heads = 8,
        rotary_pos_emb = True,
        attn_flash = True,
    ),
)

model.load_state_dict(torch.load(MODEL_CKPT, map_location=DEVICE))

model.to(DEVICE)
model.eval()

# Original training autoxast setup
autocast_ctx = torch.amp.autocast(device_type=DEVICE, dtype=DTYPE)

Creating custom MIDI corpus embeddings

python

# ================================================================================================

# Load main midisim module
import midisim

# Import helper modules
import os
import tqdm

# ================================================================================================

# Call included TMIDIX module through midisim to create MIDI files list
custom_midi_corpus_file_names = midisim.TMIDIX.create_files_list(['./custom_midi_corpus_dir/'])

# ================================================================================================

# Create two lists: one with MIDI corpus file names 
# and another with MIDI corpus tokens representations suitable for embeddings generation
midi_corpus_file_names = []
midi_corpus_tokens = []

for midi_file in tqdm.tqdm(custom_midi_corpus_file_names):
    midi_corpus_file_names.append(os.path.splitext(os.path.basename(midi_file))[0])
    
    midi_tokens = midisim.midi_to_tokens(midi_file, transpose_factor=0, verbose=False)[0]
    midi_corpus_tokens.append(midi_tokens)

# It is highly recommended to sort the resulting corpus by tokens sequence length
# This greatly speeds up embeddings calculations
sorted_midi_corpus = sorted(zip(midi_corpus_file_names, midi_corpus_tokens), key=lambda x: len(x[1]))
midi_corpus_file_names, midi_corpus_tokens = map(list, zip(*sorted_midi_corpus))

# ================================================================================================
# Now you are ready to generate embeddings as follows:
# ================================================================================================

# Load main midisim model
model, ctx, dtype = midisim.load_model(verbose=False)

# Generate MIDI corpus embeddings
midi_corpus_embeddings = midisim.get_embeddings_bf16(model, midi_corpus_tokens, verbose=False)

# ================================================================================================

# Save generated MIDI corpus embeddings and MIDI corpus file names in one handy NumPy file
midisim.save_embeddings(midi_corpus_file_names,
                        midi_corpus_embeddings,
                        verbose=False
                       )

# ================================================================================================

# You now can use this saved custom MIDI corpus NumPy file with midisim.load_embeddings()
# and the rest of the pipeline outlined in the general use section above

Music discovery pipeline

Here is a complete MIDI music discovery pipeline example using midisim and Discover MIDI Dataset

Install midisim and discovermidi PyPI packages

!pip install -U midisim

!pip install -U discovermidi

Download and unzip Discover MIDI Dataset

python

import discovermidi
from discovermidi import fast_parallel_extract

discovermidi.download_dataset()

fast_parallel_extract.fast_parallel_extract()

Choose and prepare one midisim model and corresponding embeddings set

Small model (8 layers)

python

model_ckpt = 'midisim_small_pre_trained_model_2_epochs_43117_steps_0.3148_loss_0.9229_acc.pth'
model_depth = 8

embeddings_file = 'discover_midi_dataset_3480123_clean_midis_embeddings_cc_by_nc_sa.npy'

Large model (16 layers)

python

model_ckpt = 'midisim_large_pre_trained_model_2_epochs_86275_steps_0.2054_loss_0.9385_acc.pth'
model_depth = 16

embeddings_file = 'discover_midi_dataset_3480123_clean_midis_embeddings_large_cc_by_nc_sa.npy'

Create Master MIDI dataset directory and upload your source/master MIDIs in it

python

import os

os.makedirs('./Master-MIDI-Dataset/', exist_ok=True)

Initialize midisim, download and load chosen midisim model and embeddings set

python

# Import main midisim module
import midisim

# Download embeddings from Hugging Face
emb_path = midisim.download_embeddings(filename=embeddings_file)

# Load downloaded embeddings corpus
corpus_midi_names, corpus_emb = midisim.load_embeddings(embeddings_path=emb_path)

# Download midisim model from Hugging Face
model_path = midisim.download_model(filename=model_ckpt)

# Load midisim model
model, ctx, dtype = midisim.load_model(model_path,
                                       depth=model_depth
                                      )

Create Master MIDI dataset files list

filez = midisim.TMIDIX.create_files_list(['./Master-MIDI-Dataset/'])

Launch the search

python

import os
import tqdm

for fa in tqdm.tqdm(filez):
    
    # Load source MIDI
    input_toks_seqs = midisim.midi_to_tokens(fa, verbose=False)

    if input_toks_seqs:
    
        # ================================================================================================
        # Calculate and analyze embeddings
        # ================================================================================================
        
        # Compute source/query embeddings
        query_emb = midisim.get_embeddings_bf16(model,
                                                input_toks_seqs,
                                                verbose=False,
                                                show_progress_bar=False
                                               )
    
        # Calculate cosine similarity between source/query MIDI embeddings and embeddings corpus
        idxs, sims = midisim.cosine_similarity_topk(query_emb,
													corpus_emb,
													verbose=False
												   )
       
        # ================================================================================================
        # Processs, print and save results
        # ================================================================================================
         
        # Convert the results to sorted list with transpose values
        idxs_sims_tvs_list = midisim.idxs_sims_to_sorted_list(idxs, sims)
       
        # Print corpus matches (and optionally) convert the final result to a handy list for further processing
        corpus_matches_list = midisim.print_sorted_idxs_sims_list(idxs_sims_tvs_list,
                                                                  corpus_midi_names,
                                                                  return_as_list=True
                                                                 )
         
        # ================================================================================================
        # Copy matched MIDIs from the MIDI corpus for listening and further evaluation and analysis
        # ================================================================================================
        
        # Copy matched corpus MIDI to a desired directory for easy evaluation and analysis
        out_dir_path = midisim.copy_corpus_files(corpus_matches_list,
                                                 corpus_midis_dirs=['./Discover-MIDI-Dataset/MIDIs/'],
                                                 main_output_dir='Output-MIDI-Dataset',
                                                 sub_output_dir=os.path.splitext(os.path.basename(fa))[0],
                                                 verbose=False
                                                )
        # ================================================================================================

midisim functions reference lists

Main functions

midisim.copy_corpus_files — Copy or synchronize MIDI corpus files from a source directory to a target corpus location.
midisim.cosine_similarity_topk — Compute cosine similarities between a query embedding and a set of embeddings and return the top‑K matches.
midisim.download_all_embeddings — Download an entire embeddings dataset snapshot from a Hugging Face dataset repository to a local directory.
midisim.download_embeddings — Download a single precomputed embeddings .npy file from a Hugging Face dataset repository.
midisim.download_model — Download a pre-trained model checkpoint file from a Hugging Face model repository to a local directory.
midisim.get_embeddings_bf16 — Load or convert embeddings into bfloat16 format for memory-efficient inference on supported hardware.
midisim.idxs_sims_to_sorted_list — Convert parallel index and similarity arrays into a single sorted list of (index, similarity) pairs ordered by similarity.
midisim.load_embeddings — Load a saved NumPy embeddings file and return the arrays of MIDI names and corresponding embedding vectors.
midisim.load_model — Construct a Transformer model, load weights from a checkpoint, move it to the requested device, and return the model with an AMP autocast context and dtype.
midisim.masked_mean_pool — Compute a masked mean pooling over sequence embeddings, ignoring padded positions via a boolean or numeric mask.
midisim.midi_to_tokens — Convert a single-track MIDI file into one or more compact integer token sequences (with optional transpositions) suitable for model input.
midisim.pad_and_mask — Pad a batch of variable-length token sequences to a common length and produce an attention/mask tensor indicating real tokens vs padding.
midisim.print_sorted_idxs_sims_list — Pretty-print a sorted list of (index, similarity) pairs, optionally annotating entries with filenames or metadata.
midisim.save_embeddings — Save a list of name strings and their corresponding embedding vectors into a structured NumPy array and optionally persist it to disk.

Helper functions

midisim.helpers.get_package_models — Return a sorted list of packaged model files and their paths.
midisim.helpers.get_package_embeddings — Return a sorted list of packaged embedding files and their paths.
midisim.helpers.get_normalized_midi_md5_hash — Compute original and normalized MD5 hashes for a MIDI file.
midisim.helpers.normalize_midi_file — Normalize a MIDI file and write the result to disk.
midisim.helpers.install_apt_package — Idempotently install an apt package with retries and optional python‑apt.

Limitations

Current code and models support only MIDI music elements similarity (start-times, durations and pitches)
MIDI channels, instruments, velocities and drums similarites are not currently supported due to complexity and practicality considerations
Current pre-trained models are limited by 3k sequence length (~1000 MIDI music notes) so long running MIDIs can only be analyzed in chunks
Solo drum track MIDIs are not currently supported and can't be analyzed

Citations

bibtex

@misc{project_los_angeles_2025,
	author       = { Project Los Angeles },
	title        = { midisim (Revision 707e311) },
	year         = 2025,
	url          = { https://huggingface.co/projectlosangeles/midisim },
	doi          = { 10.57967/hf/7383 },
	publisher    = { Hugging Face }
}

bibtex

@misc{project_los_angeles_2025,
	author       = { Project Los Angeles },
	title        = { midisim-embeddings (Revision 8ebb453) },
	year         = 2025,
	url          = { https://huggingface.co/datasets/projectlosangeles/midisim-embeddings },
	doi          = { 10.57967/hf/7382 },
	publisher    = { Hugging Face }
}

bibtex

@misc{project_los_angeles_2025,
	author       = { Project Los Angeles },
	title        = { midisim-samples (Revision 79afcc1) },
	year         = 2025,
	url          = { https://huggingface.co/datasets/projectlosangeles/midisim-samples },
	doi          = { 10.57967/hf/7388 },
	publisher    = { Hugging Face }
}

bibtex

@misc{project_los_angeles_2025,
	author       = { Project Los Angeles },
	title        = { Discover-MIDI-Dataset (Revision 0eaecb5) },
	year         = 2025,
	url          = { https://huggingface.co/datasets/projectlosangeles/Discover-MIDI-Dataset },
	doi          = { 10.57967/hf/7361 },
	publisher    = { Hugging Face }
}

projectlosangeles/midisim

midisim

Pre-trained models for midisim Python package

Main features

Pre-trained models

Both pre-trained models were trained on full Godzilla Piano dataset for 2 complete epochs

Pre-computed embeddings sets

For small pre-trained model

Mean Pool Embeddings

Weighted Mean Pool Embeddings

For large pre-trained model

Mean Pool Embeddings

Weighted Mean Pool Embeddings

Source MIDI dataset: Discover MIDI Dataset

Similarity search output samples

Source MIDI dataset: Discover MIDI Dataset

Installation

midisim PyPI package (for general use)

x-transformers 2.3.1 (for raw/custom tasks)

Basic use guide

General use example

Raw/custom use example

Small model (8 layers - 2 epochs)

Large model (16 layers - 2 epochs)

Creating custom MIDI corpus embeddings

Music discovery pipeline

Install midisim and discovermidi PyPI packages

Download and unzip Discover MIDI Dataset

Choose and prepare one midisim model and corresponding embeddings set

Small model (8 layers)

Large model (16 layers)

Create Master MIDI dataset directory and upload your source/master MIDIs in it

Initialize midisim, download and load chosen midisim model and embeddings set

Create Master MIDI dataset files list

Launch the search

midisim functions reference lists

Main functions

Helper functions

Limitations

Citations

Project Los Angeles

Tegridy Code 2026

projectlosangeles/
midisim