app.py

#=========================================================================
# https://huggingface.co/spaces/asigalov61/Parsons-Code-Melody-Transformer
#=========================================================================

import time as reqtime
import datetime
from pytz import timezone

import re
import tqdm

import gradio as gr

from x_transformer_1_23_2 import *
import random

from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX

import matplotlib.pyplot as plt
    
#=====================================================================================

def parsons_code_to_tokens(parsons_code_str):

  tokens = [388]

  for chr in parsons_code_str[1:]:

    if chr == 'D':
      tokens.extend([385])

    elif chr == 'R':
      tokens.extend([386])

    elif chr == 'U':
      tokens.extend([387])

  return tokens

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

def Generate_Melody(input_parsons_code, 
                   input_first_note_duration, 
                   iinput_first_note_MIDI_pitch
                 ):
    
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()

    print('=' * 70)
    print('Requested settings:')
    print('-' * 70)
    print('Parsons code:', input_parsons_code)
    print('First note duration:', input_first_note_duration)
    print('First note MIDI pitch:', iinput_first_note_MIDI_pitch)
    print('=' * 70)

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

    print('Instantiating Parsons Code Melody Transformer model...')

    SEQ_LEN = 322
    PAD_IDX = 392
    
    model = TransformerWrapper(
        num_tokens = PAD_IDX+1,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(dim = 1024,
                              depth = 4,
                              heads = 8,
                              rotary_pos_emb = True,
                              attn_flash = True
                             )
        )
    
    model = AutoregressiveWrapper(model, ignore_index = PAD_IDX, pad_value=PAD_IDX)
    
    print('=' * 70)
    print('Loading model checkpoint...')
    
    model_path = 'Parsons_Code_Melody_Transformer_Trained_Model_13786_steps_0.3058_loss_0.8819_acc.pth'
    
    model.load_state_dict(torch.load(model_path, map_location='cpu'))

    model.cpu()
    model.eval()

    dtype = torch.bfloat16
    
    ctx = torch.amp.autocast(device_type='cpu', dtype=dtype)

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

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

    print('Prepping Parsons code string...')

    td_str = re.sub('[^*DRU]', '', input_parsons_code)
    
    print(len(td_str))
    print('=' * 70)
    
    if '*' in td_str and len(td_str) > 1:
      code_mult = (64 // len(td_str[1:]))+1
      mult_code = ('*' + (td_str[1:] * code_mult))[:64]
    
    else:
      mult_code = '*UUUUUUUDDDDDDDUUUUUUUDDDDDDDUUUUUUUDDDDDDDUUUUUUUDDDDDDDUUUUUUU'

    pcode = parsons_code_to_tokens(mult_code)

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

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

    print('Generating melody...')

    song = []

    song.append(389)
    song.extend(pcode)
    song.append(390)
    
    song.extend([388, 0, 10+128, 66+256])
    
    for i in tqdm.tqdm(range(1, len(td_str[:64]))):
    
        song.append(pcode[i])
    
        x = torch.tensor(song, dtype=torch.long, device='cpu')
    
        with ctx:
            out = model.generate(x,
                                 3,
                                 filter_logits_fn=top_k,
                                 filter_kwargs={'k': 1},
                                 temperature=1.0,
                                 return_prime=False,
                                 verbose=False)
    
        y = out.tolist()[0]
    
        song.extend(y)

    print('Done!')
    print('=' * 70)
        
    #===============================================================================
    print('Rendering results...')
    
    print('=' * 70)
    print('Sample INTs', song[:5])
    print('=' * 70)

    song_f = []

    time = 0
    dur = 4
    vel = 90
    pitch = 60
    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:

            pitch = ss-256

            song_f.append(['note', time, dur, channel, pitch, vel, 0])

    fn1 = 'Parsons-Code-Melody-Transformer-Composition'

    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Parsons Code Melody Transformer',
                                                              output_file_name = fn1,
                                                              track_name='Project Los Angeles'
                                                              )
    
    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 = str(new_fn)
    output_audio = (16000, audio)
    
    output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi_title, return_plt=True)

    print('Output MIDI file name:', output_midi)
    print('Output MIDI title:', output_midi_title)
    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, 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'>Parsons Code Melody Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique melodies from Parsons codes</h1>")
        gr.Markdown(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Parsons-Code-Melody-Transformer&style=flat)\n\n"
            "This is a demo for Clean Melodies subset of Tegridy MIDI Dataset\n\n"
            "Check out [Tegridy MIDI Dataset](https://github.com/asigalov61/Tegridy-MIDI-Dataset) on GitHub!\n\n"
        )
        
        gr.Markdown("## Enter Parsons code:")
        
        input_parsons_code = gr.Textbox(label="Parsons code",
                                        info="Make sure your Parsons code starts with *",
                                        lines=1,
                                        value="*"
                                        )
        
        clr_btn = gr.ClearButton(components=input_parsons_code)

        def reset_pcode():
            return '*'

        clr_btn.click(reset_pcode, outputs=input_parsons_code)

        gr.Markdown("## Select generation options:")
                
        input_first_note_duration = gr.Slider(1, 127, value=15, step=1, label="First note duration value")
        iinput_first_note_MIDI_pitch = gr.Slider(1, 127, value=60, step=1, label="First note MIDI pitch")
         
        run_btn = gr.Button("Generate melody", variant="primary")
        
        gr.Markdown("## Output results")

        output_midi_title = gr.Textbox(label="Output MIDI title")
        output_audio = gr.Audio(label="Output MIDI audio", format="mp3", 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(Generate_Melody, [input_parsons_code, 
                                                   input_first_note_duration, 
                                                   iinput_first_note_MIDI_pitch
                                                  ],
                                                [output_midi_title, output_midi, output_audio, output_plot])

        gr.Examples(
            [["*UUUUUUUDDDDDDDUUUUUUUDDDDDDDUUUUUUUDDDDDDDUUUUUUUDDDDDDDUUUUUUU", 15, 60],
             ["*UDDDUDDDUDRURUDUUDRDDUDDRUDUDURUDRUDUDDDUDDDRDUURUDUUDDDUDRRUUD", 15, 60],
             ["*DUDDDUUDDUUDDUDUDDDUUUUUDDDDUDDDUUDDUUDDUUDUDDUDDDUUDDUUDDUDUDD", 15, 60],
             ["*DUUDDRDDUURUDUDDDUDDDDDURDDUDRDURUURUURDDDUURDUURUDUUDURDUDUDRD", 15, 60],
             ["*UUUDDUUUDDDDDUDDUUDDDDUUDDUDDDDDUUUDDDDDUDDUUUDDDURDUDUUUDDUUUD", 15, 60],
             ["*UDUUDRUDDUDRURUURUUUUUDUDDUDDUDDUDRUDDUDRUDDDUDUUDRUDDUDRURUURU", 15, 60],
            ],
            [input_parsons_code, 
            input_first_note_duration, 
            iinput_first_note_MIDI_pitch
            ],
            [output_midi_title, output_midi, output_audio, output_plot],
            Generate_Melody,
            cache_examples=True,
        )
        
        app.queue().launch()