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--- |
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license: apache-2.0 |
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datasets: |
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- asigalov61/Godzilla-Piano |
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- projectlosangeles/Discover-MIDI-Dataset |
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- projectlosangeles/midisim-embeddings |
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language: |
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- en |
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metrics: |
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- accuracy |
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tags: |
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- MIDI |
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- MIDI similarity |
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- MIDI search |
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- music |
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- similarity |
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- search |
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--- |
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# midisim |
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## Pre-trained models for midisim Python package |
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*** |
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## Main features |
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* Ultra-fast and flexible GPU/CPU MIDI-to-MIDI similarity calculation, search and analysis |
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* Quality pre-trained models and comprehensive pre-computed embeddings sets |
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* Stand-alone, versatile, and extensive codebase for general or custom MIDI-to-MIDI similarity tasks |
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* Full cross-platform compatibility and support |
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*** |
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## [Pre-trained models](https://huggingface.co/projectlosangeles/midisim) |
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* ```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 |
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* ```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 |
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#### Both pre-trained models were trained on full [Godzilla Piano](https://huggingface.co/datasets/asigalov61/Godzilla-Piano) dataset for 2 complete epochs |
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*** |
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## [Pre-computed embeddings sets](https://huggingface.co/datasets/projectlosangeles/midisim-embeddings) |
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### For small pre-trained model |
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```discover_midi_dataset_37292_genres_midis_embeddings_cc_by_nc_sa.npy``` - 37292 genre MIDIs embeddings for genre (artist and song) identification tasks |
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```discover_midi_dataset_202400_identified_midis_embeddings_cc_by_nc_sa.npy``` - 202400 identified MIDIs embeddings for MIDI identification tasks |
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```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 |
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### For large pre-trained model |
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```discover_midi_dataset_37303_genres_midis_embeddings_large_cc_by_nc_sa.npy``` - 37303 genre MIDIs embeddings for genre (artist and song) identification tasks |
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```discover_midi_dataset_202400_identified_midis_embeddings_large_cc_by_nc_sa.npy``` - 202400 identified MIDIs embeddings for MIDI identification tasks |
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```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 |
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#### Source MIDI dataset: [Discover MIDI Dataset](https://huggingface.co/datasets/projectlosangeles/Discover-MIDI-Dataset) |
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*** |
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### [Similarity search output samples](https://huggingface.co/datasets/projectlosangeles/midisim-samples) |
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```midisim-similarity-search-output-samples-CC-BY-NC-SA.zip``` - ~300000 MIDIs indentified with midisim music discovery pipeline with both pre-trained models |
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#### Source MIDI dataset: [Discover MIDI Dataset](https://huggingface.co/datasets/projectlosangeles/Discover-MIDI-Dataset) |
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*** |
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## Installation |
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### midisim PyPI package (for general use) |
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```sh |
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!pip install -U midisim |
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``` |
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### x-transformers 2.3.1 (for raw/custom tasks) |
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```sh |
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!pip install x-transformers==2.3.1 |
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``` |
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*** |
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## Basic use guide |
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### General use example |
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```python |
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# ================================================================================================ |
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# Initalize midisim |
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# ================================================================================================ |
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# Import main midisim module |
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import midisim |
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# ================================================================================================ |
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# Prepare midisim embeddings |
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# ================================================================================================ |
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# Option 1: Download sample pre-computed embeddings corpus from Hugging Face |
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emb_path = midisim.download_embeddings() |
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# Option 2: use custom pre-computed embeddings corpus |
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# See custom embeddings generation section of this README for details |
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# emb_path = './custom_midis_embeddings_corpus.npy' |
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# Load downloaded embeddings corpus |
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corpus_midi_names, corpus_emb = midisim.load_embeddings(emb_path) |
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# ================================================================================================ |
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# Prepare midisim model |
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# ================================================================================================ |
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# Option 1: Download main pre-trained midisim model from Hugging Face |
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model_path = midisim.download_model() |
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# Option 2: Use main pre-trained midisim model included in midisim PyPI package |
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# model_path = get_package_models()[0]['path'] |
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# Load midisim model |
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model, ctx, dtype = midisim.load_model(model_path) |
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# ================================================================================================ |
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# Prepare source MIDI |
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# ================================================================================================ |
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# Load source MIDI |
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input_toks_seqs = midisim.midi_to_tokens('Come To My Window.mid') |
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# ================================================================================================ |
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# Calculate and analyze embeddings |
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# ================================================================================================ |
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# Compute source/query embeddings |
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query_emb = midisim.get_embeddings_bf16(model, input_toks_seqs) |
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# Calculate cosine similarity between source/query MIDI embeddings and embeddings corpus |
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idxs, sims = midisim.cosine_similarity_topk(query_emb, corpus_emb) |
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# ================================================================================================ |
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# Processs, print and save results |
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# ================================================================================================ |
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# Convert the results to sorted list with transpose values |
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idxs_sims_tvs_list = midisim.idxs_sims_to_sorted_list(idxs, sims) |
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# Print corpus matches (and optionally) convert the final result to a handy list for further processing |
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corpus_matches_list midisim.print_sorted_idxs_sims_list(idxs_sims_tvs_list, corpus_midi_names, return_as_list=True) |
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# ================================================================================================ |
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# Copy matched MIDIs from the MIDI corpus for listening and further evaluation and analysis |
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# ================================================================================================ |
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# Copy matched corpus MIDI to a desired directory for easy evaluation and analysis |
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out_dir_path = midisim.copy_corpus_files(corpus_matches_list) |
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# ================================================================================================ |
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``` |
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### Raw/custom use example |
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#### Small model (2 epochs) |
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```python |
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import torch |
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from x_transformers import TransformerWrapper, Encoder |
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# Original model hyperparameters |
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SEQ_LEN = 3072 |
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MASK_IDX = 384 # Use this value for masked modelling |
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PAD_IDX = 385 # Model pad index |
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VOCAB_SIZE = 386 # Total vocab size |
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MASK_PROB = 0.15 # Original training mask probability value (use for masked modelling) |
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DEVICE = 'cuda' # You can use any compatible device or CPU |
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DTYPE = torch.bfloat16 # Original training dtype |
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# Official main midisim model checkpoint name |
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MODEL_CKPT = 'midisim_small_pre_trained_model_2_epochs_43117_steps_0.3148_loss_0.9229_acc.pth' |
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# Model architecture using x-transformers |
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model = TransformerWrapper( |
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num_tokens = VOCAB_SIZE, |
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max_seq_len = SEQ_LEN, |
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attn_layers = Encoder( |
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dim = 512, |
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depth = 8, |
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heads = 8, |
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rotary_pos_emb = True, |
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attn_flash = True, |
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), |
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) |
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model.load_state_dict(torch.load(MODEL_CKPT, map_location=DEVICE)) |
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model.to(DEVICE) |
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model.eval() |
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# Original training autoxast setup |
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autocast_ctx = torch.amp.autocast(device_type=DEVICE, dtype=DTYPE) |
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``` |
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#### Large model (2 epochs) |
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```python |
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import torch |
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from x_transformers import TransformerWrapper, Encoder |
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# Original model hyperparameters |
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SEQ_LEN = 3072 |
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MASK_IDX = 384 # Use this value for masked modelling |
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PAD_IDX = 385 # Model pad index |
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VOCAB_SIZE = 386 # Total vocab size |
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MASK_PROB = 0.15 # Original training mask probability value (use for masked modelling) |
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DEVICE = 'cuda' # You can use any compatible device or CPU |
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DTYPE = torch.bfloat16 # Original training dtype |
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# Official main midisim model checkpoint name |
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MODEL_CKPT = 'midisim_large_pre_trained_model_2_epochs_86275_steps_0.2054_loss_0.9385_acc.pth' |
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# Model architecture using x-transformers |
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model = TransformerWrapper( |
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num_tokens = VOCAB_SIZE, |
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max_seq_len = SEQ_LEN, |
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attn_layers = Encoder( |
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dim = 512, |
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depth = 16, |
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heads = 8, |
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rotary_pos_emb = True, |
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attn_flash = True, |
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), |
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) |
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model.load_state_dict(torch.load(MODEL_CKPT, map_location=DEVICE)) |
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model.to(DEVICE) |
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model.eval() |
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# Original training autoxast setup |
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autocast_ctx = torch.amp.autocast(device_type=DEVICE, dtype=DTYPE) |
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``` |
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*** |
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## Creating custom MIDI corpus embeddings |
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```python |
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# ================================================================================================ |
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# Load main midisim module |
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import midisim |
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# Import helper modules |
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import os |
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import tqdm |
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# ================================================================================================ |
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# Call included TMIDIX module through midisim to create MIDI files list |
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custom_midi_corpus_file_names = midisim.TMIDIX.create_files_list(['./custom_midi_corpus_dir/']) |
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# ================================================================================================ |
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# Create two lists: one with MIDI corpus file names |
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# and another with MIDI corpus tokens representations suitable for embeddings generation |
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midi_corpus_file_names = [] |
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midi_corpus_tokens = [] |
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for midi_file in tqdm.tqdm(custom_midi_corpus_file_names): |
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midi_corpus_file_names.append(os.path.splitext(os.path.basename(midi_file))[0]) |
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midi_tokens = midisim.midi_to_tokens(midi_file, transpose_factor=0, verbose=False)[0] |
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midi_corpus_tokens.append(midi_tokens) |
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# It is highly recommended to sort the resulting corpus by tokens sequence length |
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# This greatly speeds up embeddings calculations |
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sorted_midi_corpus = sorted(zip(midi_corpus_file_names, midi_corpus_tokens), key=lambda x: len(x[1])) |
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midi_corpus_file_names, midi_corpus_tokens = map(list, zip(*sorted_midi_corpus)) |
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# ================================================================================================ |
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# Now you are ready to generate embeddings as follows: |
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# ================================================================================================ |
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# Load main midisim model |
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model, ctx, dtype = midisim.load_model(verbose=False) |
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# Generate MIDI corpus embeddings |
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midi_corpus_embeddings = midisim.get_embeddings_bf16(model, midi_corpus_tokens) |
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# ================================================================================================ |
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# Save generated MIDI corpus embeddings and MIDI corpus file names in one handy NumPy file |
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midisim.save_embeddings(midi_corpus_file_names, |
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midi_corpus_embeddings, |
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verbose=False |
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) |
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# ================================================================================================ |
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# You now can use this saved custom MIDI corpus NumPy file with midisim.load_embeddings() |
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# and the rest of the pipeline outlined in the general use section above |
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``` |
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*** |
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## Music discovery pipeline |
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Here is a complete MIDI music discovery pipeline example using midisim and [Discover MIDI Dataset](https://huggingface.co/datasets/projectlosangeles/Discover-MIDI-Dataset) |
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### Install midisim and discovermidi PyPI packages |
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```sh |
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!pip install -U midisim |
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``` |
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```sh |
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!pip install -U discovermidi |
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``` |
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### Download and unzip Discover MIDI Dataset |
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```python |
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import discovermidi |
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from discovermidi import fast_parallel_extract |
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discovermidi.download_dataset() |
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fast_parallel_extract.fast_parallel_extract() |
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``` |
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### Choose and prepare one midisim model and corresponding embeddings set |
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#### Small model |
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```python |
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model_ckpt = 'midisim_small_pre_trained_model_2_epochs_43117_steps_0.3148_loss_0.9229_acc.pth' |
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model_depth = 8 |
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embeddings_file = 'discover_midi_dataset_3480123_clean_midis_embeddings_cc_by_nc_sa.npy' |
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``` |
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#### Large model |
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```python |
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model_ckpt = 'midisim_large_pre_trained_model_2_epochs_86275_steps_0.2054_loss_0.9385_acc.pth' |
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model_depth = 16 |
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embeddings_file = 'discover_midi_dataset_3480123_clean_midis_embeddings_large_cc_by_nc_sa.npy' |
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``` |
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### Create Master MIDI dataset directory and upload your source/master MIDIs in it |
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```python |
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import os |
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os.makedirs('./Master-MIDI-Dataset/', exist_ok=True) |
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``` |
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### Initialize midisim, download and load chosen midisim model and embeddings set |
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```python |
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# Import main midisim module |
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import midisim |
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# Download embeddings from Hugging Face |
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emb_path = midisim.download_embeddings(filename=embeddings_file) |
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# Load downloaded embeddings corpus |
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corpus_midi_names, corpus_emb = midisim.load_embeddings(embeddings_path=emb_path) |
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# Download midisim model from Hugging Face |
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model_path = midisim.download_model(filename=model_ckpt) |
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# Load midisim model |
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model, ctx, dtype = midisim.load_model(model_path, |
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depth=model_depth |
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) |
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``` |
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### Create Master MIDI dataset files list |
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```python |
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filez = midisim.TMIDIX.create_files_list(['./Master-MIDI-Dataset/']) |
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``` |
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### Launch the search |
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```python |
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import os |
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import tqdm |
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for fa in tqdm.tqdm(filez): |
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# Load source MIDI |
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input_toks_seqs = midisim.midi_to_tokens(fa, verbose=False) |
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if input_toks_seqs: |
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# ================================================================================================ |
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# Calculate and analyze embeddings |
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# ================================================================================================ |
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# Compute source/query embeddings |
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query_emb = midisim.get_embeddings_bf16(model, input_toks_seqs, verbose=False) |
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# Calculate cosine similarity between source/query MIDI embeddings and embeddings corpus |
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idxs, sims = midisim.cosine_similarity_topk(query_emb, corpus_emb, verbose=False) |
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# ================================================================================================ |
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# Processs, print and save results |
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# ================================================================================================ |
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# Convert the results to sorted list with transpose values |
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idxs_sims_tvs_list = midisim.idxs_sims_to_sorted_list(idxs, sims) |
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# Print corpus matches (and optionally) convert the final result to a handy list for further processing |
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corpus_matches_list = midisim.print_sorted_idxs_sims_list(idxs_sims_tvs_list, |
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corpus_midi_names, |
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return_as_list=True |
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) |
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# ================================================================================================ |
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# Copy matched MIDIs from the MIDI corpus for listening and further evaluation and analysis |
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# ================================================================================================ |
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# Copy matched corpus MIDI to a desired directory for easy evaluation and analysis |
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out_dir_path = midisim.copy_corpus_files(corpus_matches_list, |
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corpus_midis_dirs=['./Discover-MIDI-Dataset/MIDIs/'], |
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main_output_dir='Output-MIDI-Dataset', |
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sub_output_dir=os.path.splitext(os.path.basename(fa))[0], |
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verbose=False |
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) |
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# ================================================================================================ |
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``` |
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*** |
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## midisim functions reference lists |
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### Main functions |
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- ```midisim.copy_corpus_files``` — *Copy or synchronize MIDI corpus files from a source directory to a target corpus location.* |
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- ```midisim.cosine_similarity_topk``` — *Compute cosine similarities between a query embedding and a set of embeddings and return the top‑K matches.* |
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- ```midisim.download_all_embeddings``` — *Download an entire embeddings dataset snapshot from a Hugging Face dataset repository to a local directory.* |
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- ```midisim.download_embeddings``` — *Download a single precomputed embeddings `.npy` file from a Hugging Face dataset repository.* |
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- ```midisim.download_model``` — *Download a pre-trained model checkpoint file from a Hugging Face model repository to a local directory.* |
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- ```midisim.get_embeddings_bf16``` — *Load or convert embeddings into bfloat16 format for memory-efficient inference on supported hardware.* |
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- ```midisim.idxs_sims_to_sorted_list``` — *Convert parallel index and similarity arrays into a single sorted list of (index, similarity) pairs ordered by similarity.* |
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- ```midisim.load_embeddings``` — *Load a saved NumPy embeddings file and return the arrays of MIDI names and corresponding embedding vectors.* |
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- ```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.* |
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- ```midisim.masked_mean_pool``` — *Compute a masked mean pooling over sequence embeddings, ignoring padded positions via a boolean or numeric mask.* |
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- ```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.* |
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- ```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.* |
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- ```midisim.print_sorted_idxs_sims_list``` — *Pretty-print a sorted list of (index, similarity) pairs, optionally annotating entries with filenames or metadata.* |
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- ```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.* |
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### Helper functions |
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- ```midisim.helpers.get_package_models``` — *Return a sorted list of packaged model files and their paths.* |
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- ```midisim.helpers.get_package_embeddings``` — *Return a sorted list of packaged embedding files and their paths.* |
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- ```midisim.helpers.get_normalized_midi_md5_hash``` — *Compute original and normalized MD5 hashes for a MIDI file.* |
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- ```midisim.helpers.normalize_midi_file``` — *Normalize a MIDI file and write the result to disk.* |
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- ```midisim.helpers.install_apt_package``` — *Idempotently install an apt package with retries and optional python‑apt.* |
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|
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*** |
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|
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## Limitations |
|
|
|
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* Current code and models support only MIDI music elements similarity (start-times, durations and pitches) |
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* MIDI channels, instruments, velocities and drums similarites are not currently supported due to complexity and practicality considerations |
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* Current pre-trained models are limited by 3k sequence length (~1000 MIDI music notes) so long running MIDIs can only be analyzed in chunks |
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* Solo drum track MIDIs are not currently supported and can't be analyzed |
|
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|
|
|
*** |
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|
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|
## Citations |
|
|
|
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|
```bibtex |
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|
@misc{project_los_angeles_2025, |
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|
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 } |
|
|
} |
|
|
``` |
|
|
|
|
|
*** |
|
|
|
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|
### Project Los Angeles |
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|
### Tegridy Code 2025 |
