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import os |
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import time as reqtime |
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import datetime |
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from pytz import timezone |
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import gradio as gr |
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import numpy as np |
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import os |
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import random |
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from collections import Counter |
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import math |
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from tqdm import tqdm |
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import TMIDIX |
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from midi_to_colab_audio import midi_to_colab_audio |
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def Generate_Chords_Progression(minimum_song_length_in_chords_chunks, |
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chords_chunks_memory_ratio, |
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chord_time_step, |
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merge_chords_notes, |
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melody_MIDI_patch_number, |
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chords_progression_MIDI_patch_number, |
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base_MIDI_patch_number |
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): |
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print('=' * 70) |
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print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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start_time = reqtime.time() |
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print('=' * 70) |
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print('Requested settings:') |
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print('Minimum song length in chords chunks:', minimum_song_length_in_chords_chunks) |
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print('Chords chunks memory ratio:', chords_chunks_memory_ratio) |
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print('Chord time step:', chord_time_step) |
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print('Merge chords notes max time:', merge_chords_notes) |
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print('Melody MIDI patch number:', melody_MIDI_patch_number) |
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print('Chords progression MIDI patch number:', chords_progression_MIDI_patch_number) |
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print('Base MIDI patch number:', base_MIDI_patch_number) |
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print('=' * 70) |
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print('=' * 70) |
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print('Pitches Chords Progressions Generator') |
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print('=' * 70) |
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print('=' * 70) |
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print('Chunk-by-chunk generation') |
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print('=' * 70) |
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print('Generating...') |
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print('=' * 70) |
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matching_long_chords_chunks = [] |
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ridx = random.randint(0, len(all_long_chords_tokens_chunks)-1) |
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matching_long_chords_chunks.append(ridx) |
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max_song_len = 0 |
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tries = 0 |
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while len(matching_long_chords_chunks) < minimum_song_length_in_chords_chunks: |
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matching_long_chords_chunks = [] |
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ridx = random.randint(0, len(all_long_chords_tokens_chunks)-1) |
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matching_long_chords_chunks.append(ridx) |
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seen = [ridx] |
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gseen = [ridx] |
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for a in range(minimum_song_length_in_chords_chunks * 10): |
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if not matching_long_chords_chunks: |
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break |
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if len(matching_long_chords_chunks) > minimum_song_length_in_chords_chunks: |
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break |
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schunk = all_long_chords_tokens_chunks[matching_long_chords_chunks[-1]] |
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trg_long_chunk = np.array(schunk[-chunk_size:]) |
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idxs = np.where((src_long_chunks == trg_long_chunk).all(axis=1))[0].tolist() |
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if len(idxs) > 1: |
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random.shuffle(idxs) |
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eidxs = [i for i in idxs if i not in seen] |
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if eidxs: |
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eidx = eidxs[0] |
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matching_long_chords_chunks.append(eidx) |
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seen.append(eidx) |
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gseen.append(eidx) |
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if 0 < chords_chunks_memory_ratio < 1: |
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seen = random.choices(gseen, k=math.ceil(len(gseen) * chords_chunks_memory_ratio)) |
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elif chords_chunks_memory_ratio == 0: |
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seen = [] |
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else: |
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gseen.pop() |
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matching_long_chords_chunks.pop() |
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else: |
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gseen.pop() |
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matching_long_chords_chunks.pop() |
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if len(matching_long_chords_chunks) > max_song_len: |
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print('Current song length:', len(matching_long_chords_chunks), 'chords chunks') |
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print('=' * 70) |
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final_song = matching_long_chords_chunks |
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max_song_len = max(max_song_len, len(matching_long_chords_chunks)) |
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tries += 1 |
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if tries % 500 == 0: |
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print('Number of passed tries:', tries) |
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print('=' * 70) |
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if len(matching_long_chords_chunks) > max_song_len: |
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print('Current song length:', len(matching_long_chords_chunks), 'chords chunks') |
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print('=' * 70) |
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final_song = matching_long_chords_chunks |
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f_song = [] |
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for mat in final_song: |
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f_song.extend(all_long_good_chords_chunks[mat][:-chunk_size]) |
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f_song.extend(all_long_good_chords_chunks[mat][-chunk_size:]) |
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print('Generated final song after', tries, 'tries with', len(final_song), 'chords chunks and', len(f_song), 'chords') |
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print('=' * 70) |
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print('Done!') |
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print('=' * 70) |
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print('Rendering results...') |
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print('=' * 70) |
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output_score = [] |
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time = 0 |
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patches = [0] * 16 |
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patches[0] = chords_progression_MIDI_patch_number |
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if base_MIDI_patch_number > -1: |
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patches[2] = base_MIDI_patch_number |
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if melody_MIDI_patch_number > -1: |
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patches[3] = melody_MIDI_patch_number |
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chords_labels = [] |
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for i, s in enumerate(f_song): |
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time += chord_time_step |
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dur = chord_time_step |
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chord_str = str(i+1) |
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for t in sorted(set([t % 12 for t in s])): |
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chord_str += '-' + str(t) |
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chords_labels.append(['text_event', time, chord_str]) |
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for p in s: |
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output_score.append(['note', time, dur, 0, p, max(40, p), chords_progression_MIDI_patch_number]) |
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if base_MIDI_patch_number > -1: |
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output_score.append(['note', time, dur, 2, (s[-1] % 12)+24, 120-(s[-1] % 12), base_MIDI_patch_number]) |
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if melody_MIDI_patch_number > -1: |
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output_score = TMIDIX.add_melody_to_enhanced_score_notes(output_score, |
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melody_patch=melody_MIDI_patch_number, |
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melody_notes_max_duration=max(merge_chords_notes, chord_time_step) |
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) |
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if merge_chords_notes > 0: |
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escore_matrix = TMIDIX.escore_notes_to_escore_matrix(output_score) |
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output_score = TMIDIX.escore_matrix_to_merged_escore_notes(escore_matrix, max_note_duration=merge_chords_notes) |
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midi_score = sorted(chords_labels + output_score, key=lambda x: x[1]) |
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fn1 = "Pitches-Chords-Progression-Composition" |
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detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(midi_score, |
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output_signature = 'Pitches Chords Progression', |
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output_file_name = fn1, |
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track_name='Project Los Angeles', |
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list_of_MIDI_patches=patches |
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) |
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new_fn = fn1+'.mid' |
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audio = midi_to_colab_audio(new_fn, |
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soundfont_path=soundfont, |
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sample_rate=16000, |
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volume_scale=10, |
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output_for_gradio=True |
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) |
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output_midi_title = str(fn1) |
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output_midi = str(new_fn) |
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output_audio = (16000, audio) |
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output_plot = TMIDIX.plot_ms_SONG(output_score, plot_title=output_midi, return_plt=True) |
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print('Done!') |
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print('=' * 70) |
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print('Generated chords progression info and stats:') |
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print('=' * 70) |
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chords_progression_summary_string = '=' * 70 |
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chords_progression_summary_string += '\n' |
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all_song_chords = [] |
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for pc in f_song: |
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tones_chord = tuple(sorted(set([p % 12 for p in pc]))) |
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all_song_chords.append([pc, tones_chord]) |
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print('=' * 70) |
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print('Total number of chords:', len(all_song_chords)) |
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chords_progression_summary_string += 'Total number of chords: ' + str(len(all_song_chords)) + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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print('=' * 70) |
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print('Most common pitches chord:', list(Counter(tuple([a[0] for a in all_song_chords])).most_common(1)[0][0]), '===', Counter(tuple([a[0] for a in all_song_chords])).most_common(1)[0][1], 'count') |
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chords_progression_summary_string += 'Most common pitches chord: ' + str(list(Counter(tuple([a[0] for a in all_song_chords])).most_common(1)[0][0])) + ' === ' + str(Counter(tuple([a[0] for a in all_song_chords])).most_common(1)[0][1]) + ' count' + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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print('=' * 70) |
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print('Most common tones chord:', list(Counter(tuple([a[1] for a in all_song_chords])).most_common(1)[0][0]), '===', Counter(tuple([a[1] for a in all_song_chords])).most_common(1)[0][1], 'count') |
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chords_progression_summary_string += 'Most common tones chord: ' + str(list(Counter(tuple([a[1] for a in all_song_chords])).most_common(1)[0][0])) + ' === ' + str(Counter(tuple([a[1] for a in all_song_chords])).most_common(1)[0][1]) + ' count' + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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print('=' * 70) |
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print('Sorted unique songs chords set:', len(sorted(set(tuple([a[1] for a in all_song_chords])))), 'count') |
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chords_progression_summary_string += 'Sorted unique songs chords set: ' + str(len(sorted(set(tuple([a[1] for a in all_song_chords]))))) + ' count' + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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for c in sorted(set(tuple([a[1] for a in all_song_chords]))): |
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chords_progression_summary_string += str(list(c)) + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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print('=' * 70) |
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print('Grouped songs chords set:', len(TMIDIX.grouped_set(tuple([a[1] for a in all_song_chords]))), 'count') |
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chords_progression_summary_string += 'Grouped songs chords set: ' + str(len(TMIDIX.grouped_set(tuple([a[1] for a in all_song_chords])))) + ' count' + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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print('=' * 70) |
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for c in TMIDIX.grouped_set(tuple([a[1] for a in all_song_chords])): |
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chords_progression_summary_string += str(list(c)) + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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chords_progression_summary_string += 'All songs chords' + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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for i, pc_tc in enumerate(all_song_chords): |
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chords_progression_summary_string += 'Song chord # ' + str(i) + '\n' |
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chords_progression_summary_string += str(list(pc_tc[0])) + ' === ' + str(list(pc_tc[1])) + '\n' |
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chords_progression_summary_string += '=' * 70 |
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chords_progression_summary_string += '\n' |
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print('-' * 70) |
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print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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print('-' * 70) |
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print('Req execution time:', (reqtime.time() - start_time), 'sec') |
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return output_audio, output_plot, output_midi, chords_progression_summary_string |
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if __name__ == "__main__": |
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PDT = timezone('US/Pacific') |
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print('=' * 70) |
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print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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print('=' * 70) |
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soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2" |
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print('Loading processed Pitches Chords Progressions dataset data...') |
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print('=' * 70) |
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long_tones_chords_dict, all_long_chords_tokens_chunks, all_long_good_chords_chunks = TMIDIX.Tegridy_Any_Pickle_File_Reader('processed_chords_progressions_chunks_data') |
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print('=' * 70) |
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print('Resulting chords dictionary size:', len(long_tones_chords_dict)) |
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print('=' * 70) |
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print('Loading chords chunks...') |
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chunk_size = 4 |
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src_long_chunks = np.array([a[:chunk_size] for a in all_long_chords_tokens_chunks]) |
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print('Done!') |
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print('=' * 70) |
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print('Total chords chunks count:', len(all_long_good_chords_chunks)) |
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print('=' * 70) |
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app = gr.Blocks() |
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with app: |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Chords Progressions Generator</h1>") |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique chords progressions</h1>") |
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gr.Markdown( |
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"\n\n" |
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"This is a demo for Tegridy MIDI Dataset\n\n" |
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"Check out [Tegridy MIDI Dataset](https://github.com/asigalov61/Tegridy-MIDI-Dataset) on GitHub!\n\n" |
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"[Open In Colab]" |
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"(https://colab.research.google.com/github/asigalov61/Tegridy-MIDI-Dataset/blob/master/Chords-Progressions/Pitches_Chords_Progressions_Generator.ipynb)" |
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" for all options, faster execution and endless generation" |
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) |
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gr.Markdown("## Select generation options") |
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minimum_song_length_in_chords_chunks = gr.Slider(4, 60, value=30, step=1, label="Minimum song length in chords chunks") |
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chords_chunks_memory_ratio = gr.Slider(0, 1, value=1, step=0.1, label="Chords chunks memory ratio") |
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chord_time_step = gr.Slider(100, 1000, value=500, step=50, label="Chord time step") |
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merge_chords_notes = gr.Slider(0, 4000, value=1000, step=100, label="Merged chords notes max time") |
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melody_MIDI_patch_number = gr.Slider(0, 127, value=40, step=1, label="Melody MIDI patch number") |
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chords_progression_MIDI_patch_number = gr.Slider(0, 127, value=0, step=1, label="Chords progression MIDI patch number") |
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base_MIDI_patch_number = gr.Slider(0, 127, value=35, step=1, label="Base MIDI patch number") |
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run_btn = gr.Button("generate", variant="primary") |
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gr.Markdown("## Generation results") |
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output_audio = gr.Audio(label="Output MIDI audio", format="mp3", elem_id="midi_audio") |
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output_plot = gr.Plot(label="Output MIDI score plot") |
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output_midi = gr.File(label="Output MIDI file", file_types=[".mid"]) |
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output_cp_summary = gr.Textbox(label="Generated chords progression info and stats") |
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run_event = run_btn.click(Generate_Chords_Progression, |
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[minimum_song_length_in_chords_chunks, |
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chords_chunks_memory_ratio, |
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chord_time_step, |
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merge_chords_notes, |
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melody_MIDI_patch_number, |
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chords_progression_MIDI_patch_number, |
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base_MIDI_patch_number], |
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[output_audio, output_plot, output_midi, output_cp_summary] |
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) |
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app.queue().launch() |
