Update README.md

README.md

@@ -20,6 +20,516 @@ size_categories:
 # midisim
 ## Similarity search output samples
 
+![midisim](https://cdn-uploads.huggingface.co/production/uploads/64820d166e41cac337e0ccb8/WTumWkEt0a4q5twDEJotx.png)
+
+***
+
+## Main features
+
+* 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](https://huggingface.co/projectlosangeles/midisim)
+
+* ```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](https://huggingface.co/datasets/asigalov61/Godzilla-Piano) dataset for 2 complete epochs
+
+***
+
+## [Pre-computed embeddings sets](https://huggingface.co/datasets/projectlosangeles/midisim-embeddings)
+
+### For small pre-trained model
+
+```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
+
+### For large pre-trained model
+
+```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
+
+#### Source MIDI dataset: [Discover MIDI Dataset](https://huggingface.co/datasets/projectlosangeles/Discover-MIDI-Dataset)
+
+***
+
+### [Similarity search output samples](https://huggingface.co/datasets/projectlosangeles/midisim-samples)
+
+```midisim-similarity-search-output-samples-CC-BY-NC-SA.zip``` - ~300000 MIDIs indentified with midisim music discovery pipeline with both pre-trained models
+
+#### Source MIDI dataset: [Discover MIDI Dataset](https://huggingface.co/datasets/projectlosangeles/Discover-MIDI-Dataset)
+
+***
+
+## Installation
+
+### midisim PyPI package (for general use)
+
+```sh
+!pip install -U midisim
+```
+
+### x-transformers 2.3.1 (for raw/custom tasks)
+
+```sh
+!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 = 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 (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 (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)
+
+# ================================================================================================
+
+# 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](https://huggingface.co/datasets/projectlosangeles/Discover-MIDI-Dataset)
+
+### Install midisim and discovermidi PyPI packages
+
+```sh
+!pip install -U midisim
+```
+
+```sh
+!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
+
+```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
+
+```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
+
+```python
+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)
+    
+        # 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 6394ee9) },
+	year         = 2025,
+	url          = { https://huggingface.co/datasets/projectlosangeles/midisim-samples },
+	doi          = { 10.57967/hf/7387 },
+	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 }
+}
+```
+
 ***
 
 ### Project Los Angeles