app.py

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):
    
    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('=' * 70)
    print('Processing MIDI...Please wait...')
    
    #=======================================================
    # START PROCESSING

    raw_score = TMIDIX.midi2single_track_ms_score(fdata, recalculate_channels=False)

    escore = TMIDIX.advanced_score_processor(raw_score, return_score_analysis=False, return_enhanced_score_notes=True)[0]

    first_note_index = raw_score[1].index(escore[0][:6])

    for e in escore:
        e[1] = int(e[1] / 16)
        e[2] = int(e[2] / 16)

    # Sorting by patch, pitch, then by start-time

    escore.sort(key=lambda x: x[6])
    escore.sort(key=lambda x: x[4], reverse=True)
    escore.sort(key=lambda x: x[1])

    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":
        output_score = copy.deepcopy([c[0] for c in cscore if c[0][3] != 9])
        
        for e in output_score:
            e[3] = 0
            e[6] = 40

            if e[4] < 60:
                e[4] = (e[4] % 12) + 60
        
    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
        
    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
                                                              )
    
    audio = midi_to_colab_audio(new_fn, 
                        soundfont_path=soundfonts[0], 
                        sample_rate=16000, # 44100
                        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 = (16000, audio)
    output_plot = TMIDIX.plot_ms_SONG(escore, plot_title=output_midi)

    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("<h1 style='text-align: center; margin-bottom: 1rem'>Advanced MIDI Renderer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Transform and render any MIDI</h1>")
        
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Advanced-MIDI-Renderer&style=flat)\n\n"
                    "Los Angeles MIDI Dataset Demo\n\n"
                    "Please see [Los Angeles MIDI Dataset](https://github.com/asigalov61/Los-Angeles-MIDI-Dataset) for more information and features\n\n"
                    "[Open In Colab]"
                    "(https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Los_Angeles_MIDI_Dataset_Search_and_Explore.ipynb)"
                    " for all features\n\n"
                   )
        gr.Markdown("## Upload your MIDI")
        
        input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"], type="filepath")

        gr.Markdown("## Select desired render options")

        render_type = gr.Radio(["Render as-is", "Extract melody", "Transform"], label="Render type", value="Render as-is")

        submit = gr.Button()

        gr.Markdown("## Render results")
        
        output_midi_md5 = gr.Textbox(label="Output MIDI md5 hash")
        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(render_midi, [input_midi, render_type],
                                                [output_midi_md5, output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
        
    app.queue(1).launch(server_port=opt.port, share=opt.share, inbrowser=True)