app.py

import os.path
import copy
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
    
# =================================================================================================
                       
@spaces.GPU
def GenerateAccompaniment(input_midi, input_num_tokens, input_acc_type):
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()

    print('Loading model...')

    SEQ_LEN = 8192 # Models seq len
    PAD_IDX = 767 # Models pad index
    DEVICE = 'cuda' # 'cpu'

    # instantiate the model

    model = TransformerWrapper(
        num_tokens = PAD_IDX+1,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(dim = 2048, depth = 4, heads = 16, attn_flash = True)
        )
    
    model = AutoregressiveWrapper(model, ignore_index = PAD_IDX)

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

    print('Loading model checkpoint...')

    model.load_state_dict(
        torch.load('Ultimate_Accompaniment_Transformer_Small_Improved_Trained_Model_13649_steps_0.3229_loss_0.898_acc.pth',
                   map_location=DEVICE))
    print('=' * 70)

    model.eval()

    if DEVICE == 'cpu':
        dtype = torch.bfloat16
    else:
        dtype = torch.float16

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

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

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

    input_num_tokens = max(4, min(128, input_num_tokens))

    print('-' * 70)
    print('Input file name:', fn)
    print('Req num toks:', input_num_tokens)
    print('Force acc:', input_acc_type)
    print('-' * 70)

    #===============================================================================
    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]
    
    escore_notes = [e for e in escore_notes if e[3] != 9]
    
    if len(escore_notes) > 0:
    
      #=======================================================
      # PRE-PROCESSING
    
      #===============================================================================
      # Augmented enhanced score notes
    
      escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32)
    
      cscore = TMIDIX.chordify_score([1000, escore_notes])
    
      melody = TMIDIX.fix_monophonic_score_durations([sorted(e, key=lambda x: x[4], reverse=True)[0] for e in cscore])
    
      #=======================================================
      # FINAL PROCESSING
    
      melody_chords = []
    
      #=======================================================
      # MAIN PROCESSING CYCLE
      #=======================================================
    
      pe = cscore[0][0]
    
      mpe = melody[0]
    
      midx = 1
    
      for i, c in enumerate(cscore):
    
          c.sort(key=lambda x: (x[3], x[4]), reverse=True)
    
          # Next melody note
    
          if midx < len(melody):
    
            # Time
            mtime = melody[midx][1]-mpe[1]
    
            mdur = melody[midx][2]
    
            mdelta_time = max(0, min(127, mtime))
    
            # Durations
            mdur = max(0, min(127, mdur))
    
            # Pitch
            mptc = melody[midx][4]
    
          else:
            mtime = 127-mpe[1]
    
            mdur = mpe[2]
    
            mdelta_time = max(0, min(127, mtime))
    
            # Durations
            mdur = max(0, min(127, mdur))
    
            # Pitch
            mptc = mpe[4]
    
    
          e = melody[i]
    
          #=======================================================
          # Timings...
    
          time = e[1]-pe[1]
    
          dur = e[2]
    
          delta_time = max(0, min(127, time))
    
          # Durations
    
          dur = max(0, min(127, dur))
    
          # Pitches
    
          ptc = max(1, min(127, e[4]))
    
          if ptc < 60:
            ptc = 60 + (ptc % 12)
    
          cha = e[3]
    
          #=======================================================
          # FINAL NOTE SEQ
    
          if midx < len(melody):
            melody_chords.append([delta_time, dur+128, ptc+384, mdelta_time+512, mptc+640])
            mpe = melody[midx]
            midx += 1
          else:
            melody_chords.append([delta_time, dur+128, ptc+384, mdelta_time+512, mptc+640])
    
          pe = e

    #===============================================================================

    print('=' * 70)
    
    print('Sample output events', melody_chords[:5])
    print('=' * 70)
    print('Generating...')

    output = []
    
    force_acc = input_acc_type
    num_toks_per_note = 32
    temperature=0.9
    max_drums_limit=4
    num_memory_tokens=4096

    output1 = []
    output2 = []

    ctime = 0

    for m in melody_chords[:input_num_tokens]:

        mel = copy.deepcopy(m)
        mel[0] = mel[0]-ctime
    
        output1.extend(mel)

        input_seq = output1
    
        if force_acc:
          x = torch.LongTensor([input_seq+[0]]).to(DEVICE)
        else:
          x = torch.LongTensor([input_seq]).to(DEVICE)
    
        time = input_seq[-2]-512
    
        cur_time = 0
        ctime = 0
    
        for _ in range(num_toks_per_note):
          with ctx:
            out = model.generate(x[-num_memory_tokens:],
                                1,
                                temperature=temperature,
                                return_prime=False,
                                verbose=False)
    
          o = out.tolist()[0][0]
    
          if 0 <= o < 128:
            cur_time += o
    
          if cur_time < time and o < 384:
              
            ctime = cur_time
    
            out = torch.LongTensor([[o]]).to(DEVICE)
            x = torch.cat((x, out), 1)
          else:
            break
    
        outy =  x.tolist()[0][len(input_seq):]

        output1.extend(outy)
        output2.append(outy)

    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    print('Rendering results...')
    
    print('=' * 70)
    print('Sample INTs', output1[:12])
    print('=' * 70)
    
    out1 = output2

    accompaniment_MIDI_patch_number = 0
    melody_MIDI_patch_number = 40

    if len(out1) != 0:
    
        song = out1
        song_f = []
    
        time = 0
        ntime = 0
        ndur = 0
        vel = 90
        npitch = 0
        channel = 0
    
        patches = [0] * 16
        patches[0] = accompaniment_MIDI_patch_number
        patches[3] = melody_MIDI_patch_number
    
        for i, ss in enumerate(song):
    
                ntime += melody_chords[i][0] * 32
                ndur = (melody_chords[i][1]-128) * 32
                nchannel = 1
                npitch = (melody_chords[i][2]-256) % 128
                vel = max(40, npitch)+20
    
                song_f.append(['note', ntime, ndur, 3, npitch, vel, melody_MIDI_patch_number ])
    
                time = ntime
    
                for s in ss:
    
                  if 0 <= s < 128:
    
                      time += s * 32
    
                  if 128 <= s < 256:
    
                      dur = (s-128) * 32
    
                  if 256 <= s < 384:
    
                      pitch = (s-256)
    
                      vel = max(40, pitch)
    
                      song_f.append(['note', time, dur, 0, pitch, vel, accompaniment_MIDI_patch_number])

    fn1 = "Ultimate-Accompaniment-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Ultimate Accompaniment 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:', '')
    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'>Ultimate Accompaniment Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique accompaniment for any melody</h1>")
        gr.Markdown(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Ultimate-Accompaniment-Transformer&style=flat)\n\n"
            "Accompaniment generation for any monophonic melody\n\n"
            "Check out [Ultimate Drums Transformer](https://github.com/asigalov61/Ultimate-Accompaniment-Transformer) on GitHub!\n\n"
            "[Open In Colab]"
            "(https://colab.research.google.com/github/asigalov61/Ultimate-Accompaniment-Transformer/blob/main/Ultimate_Accompaniment_Transformer.ipynb)"
            " for faster execution and endless generation"
        )
        gr.Markdown("## Upload your MIDI or select a sample example MIDI")
        
        input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
        input_num_tokens = gr.Slider(4, 128, value=32, step=1, label="Number of composition chords to generate accompaniment for")
        input_acc_type = gr.Checkbox(label='Force accompaniment generation for each melody note')
        
        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(GenerateAccompaniment, [input_midi, input_num_tokens, input_acc_type],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        gr.Examples(
            [["Ultimate-Accompaniment-Transformer-Melody-Seed-1.mid", 128, True], 
             ["Ultimate-Accompaniment-Transformer-Melody-Seed-2.mid", 128, False], 
             ["Ultimate-Accompaniment-Transformer-Melody-Seed-3.mid", 128, True],
             ["Ultimate-Accompaniment-Transformer-Melody-Seed-4.mid", 128, False],
             ["Ultimate-Accompaniment-Transformer-Melody-Seed-5.mid", 128, True],
             ["Ultimate-Accompaniment-Transformer-Melody-Seed-6.mid", 128, False],
             ["Ultimate-Accompaniment-Transformer-Melody-Seed-7.mid", 128, True]],
            [input_midi, input_num_tokens, input_acc_type],
            [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
            GenerateAccompaniment,
            cache_examples=True,
        )
        
        app.queue().launch()