import argparse import glob import os.path import hashlib import time import datetime from pytz import timezone import gradio as gr import pickle import tqdm import json import TMIDIX from midi_to_colab_audio import midi_to_colab_audio import copy from collections import Counter import random import statistics import matplotlib.pyplot as plt #========================================================================================================== in_space = os.getenv("SYSTEM") == "spaces" #========================================================================================================== def render_midi(input_midi, render_type, soundfont_bank, render_sample_rate, custom_render_patch): print('*' * 70) print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) start_time = time.time() print('=' * 70) print('Loading MIDI...') fn = os.path.basename(input_midi) fn1 = fn.split('.')[0] fdata = open(input_midi, 'rb').read() input_midi_md5hash = hashlib.md5(fdata).hexdigest() print('=' * 70) print('Input MIDI file name:', fn) print('Input MIDI md5 hash', input_midi_md5hash) print('Render type:', render_type) print('Soudnfont bank', soundfont_bank) print('Audio render sample rate', render_sample_rate) print('Custom MIDI render patch', custom_render_patch) print('=' * 70) print('Processing MIDI...Please wait...') #======================================================= # START PROCESSING raw_score = TMIDIX.midi2single_track_ms_score(fdata) escore = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0] escore= TMIDIX.augment_enhanced_score_notes(escore) first_note_index = [e[0] for e in escore].index('note') cscore = TMIDIX.chordify_score([1000, escore]) meta_data = raw_score[1][:first_note_index] + [escore[0]] + [escore[-1]] + [raw_score[1][-1]] print('Done!') print('=' * 70) print('Input MIDI metadata:', meta_data) print('=' * 70) print('Processing...Please wait...') if render_type == "Render as-is" or not render_type: output_score = copy.deepcopy(escore) elif render_type == "Extract melody": melody_score = copy.deepcopy([c[0] for c in cscore if c[0][3] != 9]) for e in melody_score: e[3] = 0 if e[4] < 60: e[4] = (e[4] % 12) + 60 output_score = TMIDIX.fix_monophonic_score_durations(melody_score) elif render_type == "Transform": min_pitch = min([e[4] for e in escore if e[3] != 9]) fliped_score_pitches = [127 - e[4]for e in escore if e[3] != 9] delta_min_pitch = min_pitch - min([p for p in fliped_score_pitches]) output_score = copy.deepcopy(escore) for e in output_score: if e[3] != 9: e[4] = (127 - e[4]) + delta_min_pitch elif render_type == 'Repair': output_score = [] for c in cscore: c.sort(key=lambda x: x[4], reverse=True) fixed_chord = TMIDIX.check_and_fix_chord(c) output_score.extend(fixed_chord) output_score.sort(key= lambda x: x[1]) print('Done processing!') print('=' * 70) print('Recalculating timings...') print('=' * 70) for e in output_score: e[1] = e[1] * 16 e[2] = e[2] * 16 if -1 < custom_render_patch < 128: if e[3] != 9: e[6] = custom_render_patch else: if e[3] != 9: e[6] = 0 print('Done recalculating timings!') print('=' * 70) print('Sample output events', output_score[:5]) print('=' * 70) print('Final processing...') new_fn = fn1+'.mid' patches = [-1] * 16 patches[9] = 9 for e in output_score: if e[3] != 9: if patches[e[3]] == -1: patches[e[3]] = e[6] else: if patches[e[3]] != e[6]: if -1 in patches: patches[patches.index(-1)] = e[6] else: patches[-1] = e[6] patches = [p if p != -1 else 0 for p in patches] detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score, output_signature = 'Advanced MIDI Renderer', output_file_name = fn1, track_name='Project Los Angeles', list_of_MIDI_patches=patches ) if soundfont_bank in ["General MIDI", "Nice strings plus orchestra", "Real choir"]: sf2bank = ["General MIDI", "Nice strings plus orchestra", "Real choir"].index(soundfont_bank) else: sf2bank = 0 if render_sample_rate in ["16000", "32000", "44100"]: srate = int(render_sample_rate) else: srate = 16000 audio = midi_to_colab_audio(new_fn, soundfont_path=soundfonts[sf2bank], sample_rate=srate, volume_scale=10, output_for_gradio=True ) new_md5_hash = hashlib.md5(open(new_fn,'rb').read()).hexdigest() print('Done!') print('=' * 70) #======================================================== output_midi_md5 = str(new_md5_hash) output_midi_title = str(fn1) output_midi_summary = str(meta_data) output_midi = str(new_fn) output_audio = (srate, audio) output_plot = TMIDIX.plot_ms_SONG(output_score, plot_title=output_midi, return_plt=True) print('Output MIDI file name:', output_midi) print('Output MIDI title:', output_midi_title) print('Output MIDI hash:', output_midi_md5) print('Output MIDI summary:', output_midi_summary) print('=' * 70) #======================================================== print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) print('-' * 70) print('Req execution time:', (time.time() - start_time), 'sec') print('*' * 70) #======================================================== yield output_midi_md5, 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) parser = argparse.ArgumentParser() parser.add_argument("--share", action="store_true", default=False, help="share gradio app") parser.add_argument("--port", type=int, default=7860, help="gradio server port") opt = parser.parse_args() soundfonts = ["SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2", "Nice-Strings-PlusOrchestra-v1.6.sf2", "KBH-Real-Choir-V2.5.sf2"] app = gr.Blocks() with app: gr.Markdown("