app.py

#=======================================================================================
# https://huggingface.co/spaces/asigalov61/Imagen-POP-Music-Medley-Diffusion-Transformer
#=======================================================================================

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

import torch
from imagen_pytorch import Unet, Imagen, ImagenTrainer
from imagen_pytorch.data import Dataset

import spaces
import gradio as gr

import random
import tqdm

from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX
         
# =================================================================================================
                       
@spaces.GPU
def GenerateSong(input_melody_seed_number):
    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 = 2560
    PAD_IDX = 514
    DEVICE = 'cuda' # 'cuda'

    # instantiate the model

    model = TransformerWrapper(
        num_tokens = PAD_IDX+1,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(dim = 1024, depth = 24, 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('Melody2Song_Seq2Seq_Music_Transformer_Trained_Model_28482_steps_0.719_loss_0.7865_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)
    seed_melody = seed_melodies_data[input_melody_seed_number]
    print('Input melody seed number:', input_melody_seed_number)
    print('-' * 70)

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

    print('=' * 70)
    
    print('Sample output events', seed_melody[:16])
    print('=' * 70)
    print('Generating...')

    x = (torch.tensor(seed_melody, dtype=torch.long, device='cuda')[None, ...])

    with ctx:
        with torch.inference_mode():
            out = model.generate(x,
                                1024,
                                temperature=0.9,
                                return_prime=False,
                                verbose=False)
    
    output = out[0].tolist()
        
    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    print('Rendering results...')
    
    print('=' * 70)
    print('Sample INTs', output[:15])
    print('=' * 70)
    
    out1 = output

    if len(out1) != 0:
    
        song = out1
        song_f = []
    
        time = 0
        dur = 0
        vel = 90
        pitch = 0
        channel = 0
    
        patches = [0] * 16
        patches[3] = 40
    
        for ss in song:
    
            if 0 < ss < 128:
    
                time += (ss * 32)
    
            if 128 < ss < 256:
    
                dur = (ss-128) * 32
    
            if 256 < ss < 512:
    
                pitch = (ss-256) % 128
    
                channel = (ss-256) // 128
    
                if channel == 1:
                    channel = 3
                    vel = 110 + (pitch % 12)
                    song_f.append(['note', time, dur, channel, pitch, vel, 40])
                    
                else:
                    vel = 80 + (pitch % 12)
                    channel = 0
                    song_f.append(['note', time, dur, channel, pitch, vel, 0])

    fn1 = "Melody2Song-Seq2Seq-Music-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Melody2Song Seq2Seq 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'>Imagen POP Music Medley Diffusion Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique POP music medleys with Imagen diffusion transformer</h1>")
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Imagen-POP-Music-Medley-Diffusion-Transformer&style=flat)\n\n"
                    "This is a demo for MIDI Images dataset\n\n"
                    "Please see [MIDI Images](https://huggingface.co/datasets/asigalov61/MIDI-Images) Hugging Face repo for more information\n\n"
                    )        
        
        input_num_medley_comps = gr.Slider(1, 64, value=8, step=1, label="Number of medley compositions")

        run_btn = gr.Button("Generate POP Medley", 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(GenerateSong, [input_melody_seed_number],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        app.queue().launch()