#======================================================================================= # 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" print('Loading seed meldoies data...') seed_melodies_data = TMIDIX.Tegridy_Any_Pickle_File_Reader('Melody2Song_Seq2Seq_Music_Transformer_Seed_Melodies_Data') print('=' * 70) app = gr.Blocks() with app: gr.Markdown("