# ================================================================================================= # https://huggingface.co/spaces/asigalov61/Chords-Progressions-Generator # ================================================================================================= import os import time as reqtime import datetime from pytz import timezone import gradio as gr import numpy as np import os import random from collections import Counter import TMIDIX from midi_to_colab_audio import midi_to_colab_audio # ================================================================================================= def Generate_Chords_Progression(total_song_length_in_chords_chunks, chords_chunks_memory_length, chord_time_step, melody_MIDI_patch_number, chords_progression_MIDI_patch_number, base_MIDI_patch_number ): print('=' * 70) print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) start_time = reqtime.time() print('=' * 70) print('=' * 70) print('Ultimate MIDI Classifier') print('=' * 70) print('Input MIDI file name:', fn) print('=' * 70) print('Done!') print('=' * 70) classification_summary_string = '=' * 70 classification_summary_string += '\n' samples_overlap = 340 - chunk_size // input_sampling_resolution // 3 print('Composition has', notes_counter, 'notes') print('=' * 70) print('Composition was split into' , len(input_data), 'samples', 'of 340 notes each with', samples_overlap, 'notes overlap') print('=' * 70) print('Number of notes in all composition samples:', len(input_data) * 340) print('=' * 70) classification_summary_string += 'Composition has ' + str(notes_counter) + ' notes\n' classification_summary_string += '=' * 70 classification_summary_string += '\n' classification_summary_string += 'Composition was split into ' + 'samples of 340 notes each with ' + str(samples_overlap) + ' notes overlap\n' classification_summary_string += 'Number of notes in all composition samples: ' + str(len(input_data) * 340) + '\n' classification_summary_string += '=' * 70 classification_summary_string += '\n' #================================================================== print('=' * 70) print('Ultimate MIDI Classifier') print('=' * 70) print('=' * 70) print('Done!') print('=' * 70) result_toks = [final_result[0]-512, final_result[1]-512] mc_song_artist = song_artist_tokens_to_song_artist(result_toks) gidx = genre_labels_fnames.index(mc_song_artist) mc_genre = genre_labels[gidx][1] print('Most common classification genre label:', mc_genre) print('Most common classification song-artist label:', mc_song_artist) print('Most common song-artist classification label ratio:' , results.count(final_result) / len(results)) print('=' * 70) classification_summary_string += 'Most common classification genre label: ' + str(mc_genre) + '\n' classification_summary_string += 'Most common classification song-artist label: ' + str(mc_song_artist) + '\n' classification_summary_string += 'Most common song-artist classification label ratio: '+ str(results.count(final_result) / len(results)) + '\n' classification_summary_string += '=' * 70 classification_summary_string += '\n' print('All classification labels summary:') print('=' * 70) all_artists_labels = [] for i, res in enumerate(results): result_toks = [res[0]-512, res[1]-512] song_artist = song_artist_tokens_to_song_artist(result_toks) gidx = genre_labels_fnames.index(song_artist) genre = genre_labels[gidx][1] print('Notes', i*(340-samples_overlap), '-', (i*(340-samples_overlap))+340, '===', genre, '---', song_artist) classification_summary_string += 'Notes ' + str(i*samples_overlap) + ' - ' + str((i*samples_overlap)+340) + ' === ' + str(genre) + ' --- ' + str(song_artist) + '\n' artist_label = str_strip_artist(song_artist.split(' --- ')[1]) all_artists_labels.append(artist_label) classification_summary_string += '=' * 70 classification_summary_string += '\n' print('=' * 70) mode_artist_label = mode(all_artists_labels) mode_artist_label_count = all_artists_labels.count(mode_artist_label) print('Aggregated artist classification label:', mode_artist_label) print('Aggregated artist classification label ratio:', mode_artist_label_count / len(all_artists_labels)) classification_summary_string += 'Aggregated artist classification label: ' + str(mode_artist_label) + '\n' classification_summary_string += 'Aggregated artist classification label ratio: ' + str(mode_artist_label_count / len(all_artists_labels)) + '\n' classification_summary_string += '=' * 70 classification_summary_string += '\n' print('=' * 70) print('Done!') print('=' * 70) #=============================================================================== print('Rendering results...') print('=' * 70) fn1 = "Melody2Song-Seq2Seq-Music-Transformer-Composition" detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, output_signature = 'Melody2Song Seq2Seq Music Transformer', output_file_name = fn1, track_name='Project Los Angeles', list_of_MIDI_patches=patches ) new_fn = fn1+'.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(fn1) output_midi_summary = str(song_f[:3]) output_midi = str(new_fn) output_audio = (16000, audio) output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi, return_plt=True) #======================================================== 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_audio, output_plot, output_midi, output_cp_summary # ================================================================================================= 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) print('=' * 70) print('Loading Ultimate MIDI Classifier labels...') print('=' * 70) good_chords_chunks = TMIDIX.Tegridy_Any_Pickle_File_Reader('pitches_chords_progressions_5_3_15') print('=' * 70) print('Done!') print('=' * 70) app = gr.Blocks() with app: gr.Markdown("

Chords Progressions Generator

") gr.Markdown("

Generate unique chords progressions

") gr.Markdown( "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Chords-Progressions-Generator&style=flat)\n\n" "This is a demo for Tegridy MIDI Dataset\n\n" "Check out [Tegridy MIDI Dataset](https://github.com/asigalov61/Tegridy-MIDI-Dataset) on GitHub!\n\n" "[Open In Colab]" "(https://colab.research.google.com/github/asigalov61/Tegridy-MIDI-Dataset/blob/master/Chords-Progressions/Pitches_Chords_Progressions_Generator.ipynb)" " for all options, faster execution and endless generation" ) gr.Markdown("## Select generation options") total_song_length_in_chords_chunks = gr.Slider(5, 20, value=13, step=1, label="Total song length in chords chunks") chords_chunks_memory_length = gr.Slider(-1, 30, value=-1, step=1, label="Chords chunks memory length") chord_time_step = gr.Slider(100, 1000, value=500, step=50, label="Chord time step") melody_MIDI_patch_number = gr.Slider(0, 127, value=40, step=1, label="Melody MIDI patch number") chords_progression_MIDI_patch_number = gr.Slider(0, 127, value=0, step=1, label="Chords progression MIDI patch number") base_MIDI_patch_number = gr.Slider(0, 127, value=35, step=1, label="Base MIDI patch number") run_btn = gr.Button("generate", variant="primary") gr.Markdown("## Generation results") 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"]) output_cp_summary = gr.Textbox(label="Generated chords progression info and stats") run_event = run_btn.click(Generate_Chords_Progression, [total_song_length_in_chords_chunks, chords_chunks_memory_length, chord_time_step, melody_MIDI_patch_number, chords_progression_MIDI_patch_number, base_MIDI_patch_number], [output_audio, output_plot, output_midi, output_cp_summary] ) app.queue().launch()