app.py

# https://huggingface.co/spaces/asigalov61/Bridge-Music-Transformer

import os
import time as reqtime
import datetime
from pytz import timezone

import torch

import spaces
import gradio as gr

from x_transformer_1_23_2 import *
import random
import tqdm

from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX

import matplotlib.pyplot as plt

in_space = os.getenv("SYSTEM") == "spaces"
         
# =================================================================================================
                       
@spaces.GPU
def GenerateBridge(input_midi, input_start_note):
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()


    print('=' * 70)

    fn = os.path.basename(input_midi.name)
    fn1 = fn.split('.')[0]

    print('-' * 70)
    print('Input file name:', fn)
    print('Start note', input_start_note)
    print('-' * 70)

    print('Loading model...')

    SEQ_LEN = 3074
    PAD_IDX = 653
    DEVICE = 'cpu' # 'cuda'

    # instantiate the model

    model = TransformerWrapper(
        num_tokens = PAD_IDX+1,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(dim = 1024, depth = 32, heads = 16, attn_flash = True)
        )
    
    model = AutoregressiveWrapper(model, ignore_index = PAD_IDX, pad_value=PAD_IDX)

    model.to(DEVICE)
    print('=' * 70)

    print('Loading model checkpoint...')

    model.load_state_dict(
        torch.load('Bridge_Music_Transformer_Trained_Model_30023_steps_0.482_loss_0.8523_acc.pth',
                   map_location=DEVICE))
    print('=' * 70)

    model.eval()

    if DEVICE == 'cpu':
        dtype = torch.bfloat16
    else:
        dtype = torch.bfloat16

    ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)

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

    print('Loading MIDI...')
    
    #===============================================================================
    # Raw single-track ms score
    
    raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name)
    
    #===============================================================================
    # Enhanced score notes
    
    escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
    
    #===============================================================================
    # Augmented enhanced score notes
    
    escore_notes = TMIDIX.recalculate_score_timings(TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32))
    
    #=======================================================
    # FINAL PROCESSING
    
    melody_chords = []
    
    #=======================================================
    # MAIN PROCESSING CYCLE
    #=======================================================
    
    pe = escore_notes[0]
    
    for e in escore_notes:
    
        #=======================================================
        # Timings...
    
        delta_time = max(0, min(127, e[1]-pe[1]))
    
        # Durations and channels
    
        dur = max(0, min(127, e[2]))
    
        cha = max(0, min(15, e[3]))
    
        # Patches
        pat = max(0, min(128, e[6]))
    
        # Pitches
        if cha != 9:
            ptc = max(1, min(127, e[4]))
        else:
            ptc = max(1, min(127, e[4]))+128
    
        # Velocities
        # Calculating octo-velocity
        velocity = max(8, min(127, e[5]))
        vel = round(velocity / 15)-1
    
        #=======================================================
        # FINAL NOTE SEQ
    
        # Writing final note synchronously
    
        melody_chords.extend([delta_time, dur+128, pat+256, ptc+384, vel+640])
    
        pe = e
    
        #=======================================================

    melody_chords = melody_chords[input_start_note*5:]
    
    SEQ_L = 3060
    STEP = SEQ_L // 3
    
    score_chunk = melody_chords[:SEQ_L]
    
    td = [649]
    
    td.extend(score_chunk[:STEP])
    
    td += [650]
    
    td.extend(score_chunk[-STEP:])
    
    td += [651]
    
    start_note = score_chunk[:STEP][-5:]
    end_note = score_chunk[-STEP:][:5]
    
    print('Done!')
    print('=' * 70)
    
    print('Start note', start_note)
    print('Etart note', end_note)
    
    print('=' * 70)
    print('Generating...')

    x = (torch.tensor(td, dtype=torch.long, device=DEVICE)[None, ...])
    
    with ctx:
      out = model.generate(x,
                          1032,
                          temperature=0.9,
                          return_prime=False,
                          verbose=False)
    
    y = out.tolist()
  
    output = []
    
    for i in range(0, len(y[0]), 5):
        if len(y[0][i:i+5]) == 5:
            output.append(y[0][i:i+5])
        
    print('=' * 70)
    print('Done!')
    print('=' * 70)

    start_note_idx = output.index(start_note)
    end_note_idx = len(output)-output[::-1].index(end_note)-1
    
    print('Start note check:', start_note in output, '---', start_note_idx)
    print('End note check:',end_note in output, '---', end_note_idx)
    
    #===============================================================================
    print('Rendering results...')

    data = score_chunk[:STEP] + TMIDIX.flatten(output[:end_note_idx]) + score_chunk[-STEP:]
    
    print('=' * 70)
    print('Sample INTs', data[:15])
    print('=' * 70)

    if len(data) != 0:
    
        song = data
        song_f = []
    
        time = 0
        dur = 0
        vel = 90
        pitch = 0
        pat = 0
        channel = 0
    
        for ss in song:
    
            if 0 < ss < 128:
    
                time += (ss * 32)
    
            if 128 < ss < 256:
    
                dur = (ss-128) * 32
    
            if 256 <= ss <= 384:
    
                pat = (ss-256)
                channel = pat // 8
    
                if channel == 9:
                    channel = 15
                if channel == 16:
                    channel = 9
    
            if 384 < ss < 640:
    
                pitch = (ss-384) % 128
    
            if 640 <= ss < 648:
    
                vel = ((ss-640)+1) * 15
    
                song_f.append(['note', time, dur, channel, pitch, vel, pat])
    
    song_f, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f)


    fn1 = "Bridge-Music-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Bridge 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('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 = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"
   
    app = gr.Blocks()
    
    with app:
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Bridge Music Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate a seamless bridge between two parts of any composition</h1>")
        gr.Markdown(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Bridge-Music-Transformer&style=flat)\n\n")
        
        gr.Markdown("## Upload your MIDI or select a sample example MIDI below")
        gr.Markdown("### Please note that the MIDI must have at least 615 notes for this demo to work properly")
        
        input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
        input_start_note = gr.Slider(0, 205, value=0, step=1, label="Start note number")
        run_btn = gr.Button("generate", variant="primary")

        gr.Markdown("## Generation 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 = run_btn.click(GenerateBridge, [input_midi, input_start_note],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
        
        gr.Examples(
            [["Sharing The Night Together.kar", 0],
             ["Sharing The Night Together.kar", 100],
             ["Deep Relaxation Melody #6.mid", 0],
             ["Deep Relaxation Melody #6.mid", 100]
            ],
            [input_midi, input_start_note],
            [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
            GenerateBridge,
            cache_examples=False,
        )
        app.queue().launch()