app.py

# https://huggingface.co/spaces/asigalov61/MIDI-Search

import os

import time as reqtime
import datetime
from pytz import timezone

import numpy as np

import gradio as gr

import copy
import random
import pickle

import zlib

from midi_to_colab_audio import midi_to_colab_audio

import TMIDIX

import matplotlib.pyplot as plt

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

def find_midi(title, artist):

    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()

    print('-' * 70)
    print('Req title:', title)
    print('Req artist:', artist)
    print('-' * 70)


    input_text = ''

    if title != '':
        input_text += title
    if artist != '':
        input_text += ' by ' + artist
    
    print('Searching...')

    query_embedding = model.encode([input_text])
    
    # Compute cosine similarity between query and each sentence in the corpus
    similarities = util.cos_sim(query_embedding, corpus_embeddings)
    
    # Find the index of the most similar sentence
    closest_index = np.argmax(similarities)
    closest_index_match_ratio = max(similarities[0]).tolist()

    best_corpus_match = all_MIDI_files_names[closest_index]

    print('Done!')
    print('=' * 70)
    
    print('Match corpus index', closest_index)
    print('Match corpus ratio', closest_index_match_ratio)
    
    print('=' * 70)
    print('Done!')
    print('=' * 70)

    song_artist = best_corpus_match[0]
    zlib_file_name = best_corpus_match[1]

    print('Fetching MIDI score...')

    with open(zlib_file_name, 'rb') as f:
        compressed_data = f.read()
    
    # Decompress the data
    decompressed_data = zlib.decompress(compressed_data)
    
    # Convert the bytes back to a list using pickle
    scores_data = pickle.loads(decompressed_data)
    
    fnames = [f[0] for f in scores_data]
    
    fnameidx = fnames.index(song_artist)
    
    MIDI_score_data = scores_data[fnameidx][1]
    
    print('Sample INTs', MIDI_score_data[:12])
    print('=' * 70)
    
    if len(outy) != 0:
    
        song = outy
        song_f = []
        
        time = 0
        dur = 0
        vel = 90
        pitch = 0
        channel = 0
        
        patches = [-1] * 16
        
        channels = [0] * 16
        channels[9] = 1
        
        for ss in song:
    
            if 0 <= ss < 256:
            
              time += ss * 16
            
            if 256 <= ss < 512:
            
              dur = (ss-256) * 16
            
            if 512 <= ss <= 640:

                patch = (ss-512)
                
                if patch < 128:
            
                  if patch not in patches:
                    if 0 in channels:
                        cha = channels.index(0)
                        channels[cha] = 1
                    else:
                        cha = 15
            
                    patches[cha] = patch
                    channel = patches.index(patch)
                  else:
                    channel = patches.index(patch)
            
              if patch == 128:
                  channel = 9
                
            if 640 < ss < 768:

                ptc = (ss-640)

            if 768 < ss < 896:

                vel = (ss - 768)
            
                song_f.append(['note', time, dur, channel, ptc, vel, patch ])
    
    patches = [0 if x==-1 else x for x in patches]

    print('=' * 70)
    
    #===============================================================================
    print('Rendering results...')
    
    print('=' * 70)
    print('Sample INTs', song_f[:3])
    print('=' * 70)
    
    output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f)
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score,
                                                          output_signature = 'Los Angeles MIDI Dataset Search',
                                                          output_file_name = song_artist,
                                                          track_name='Project Los Angeles',
                                                          list_of_MIDI_patches=patches,
                                                          timings_multiplier=16
                                                          )
    
    new_fn = song_artist + '.mid'
        
    audio = midi_to_colab_audio(new_fn, 
                    soundfont_path=soundfont,
                    sample_rate=16000,
                    volume_scale=10,
                    output_for_gradio=True
                    )
    
    print('Done!')
    print('=' * 70)
    
    #========================================================
    
    output_midi_title = str(song_artist)
    output_midi_summary = str(song_f[:3])
    output_midi = str(new_fn)
    output_audio = (16000, audio)
    
    output_plot = TMIDIX.plot_ms_SONG(output_score, plot_title=output_midi_title, return_plt=True)
    
    print('Output MIDI file name:', output_midi)
    print('Output MIDI title:', output_midi_title)
    print('Output MIDI summary:', output_midi_summary)
    print('=' * 70) 
    
    #========================================================
    
    print('-' * 70)
    print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    print('-' * 70)
    print('Req execution time:', (reqtime.time() - start_time), 'sec')
    
    return output_midi_title, output_midi_summary, output_midi, output_audio, output_plot        
    
#==========================================================================================================

if __name__ == "__main__":

    PDT = timezone('US/Pacific')
    
    print('=' * 70)
    print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    print('=' * 70)

    soundfont_path = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"
    print('Loading files list...')
    
    all_MIDI_files_names = TMIDIX.Tegridy_Any_Pickle_File_Reader('all_MIDI_files_names')

    print('Done!')
    print('=' * 70)

    print('Loading clean_midi corpus...')

    clean_midi_artist_song_description_summaries_lyrics_score = TMIDIX.Tegridy_Any_Pickle_File_Reader('clean_midi_artist_song_description_summaries_lyrics_scores')

    print('Done!')
    print('=' * 70)

    print('Loading MIDI corpus embeddings...')
    
    corpus_embeddings = np.load('MIDI_corpus_embeddings_all-mpnet-base-v2.npz')['data']
    
    print('Done!')
    print('=' * 70)

    print('Loading Sentence Transformer model...')
    
    model = SentenceTransformer('all-mpnet-base-v2')
    
    print('Done!')
    print('=' * 70)
    
    app = gr.Blocks()
    
    with app:
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Advanced MIDI Search</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Search and explore 179k+ MIDI titles</h1>")
        
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.MIDI-Search&style=flat)\n\n"
                    "Giant Music Transformer Aux Data Demo\n\n"
                    "Please see [Giant Music Transformer](https://github.com/asigalov61/Giant-Music-Transformer) for more information and features\n\n"
                    "[Open In Colab]"
                    "(https://colab.research.google.com/github/asigalov61/Giant-Music-Transformer/blob/main/Giant_Music_Transformer_TTM.ipynb)"
                    " for all features"
                    )
        
        title = gr.Textbox(label="Desired Song Title", value="Family Guy")
        artist = gr.Textbox(label="Desired Song Artist", value="TV Themes")
        submit = gr.Button()

        gr.Markdown("# Search results")

        output_midi_title = gr.Textbox(label="Output MIDI title")
        output_midi_summary = gr.Textbox(label="Output MIDI summary")
        output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio")
        output_plot = gr.Plot(label="Output MIDI score plot")
        output_midi = gr.File(label="Output MIDI file", file_types=[".mid"])
        
        run_event = submit.click(find_midi, [title, artist],
                                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot ])
        
    app.launch()