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| import os.path | |
| import time | |
| import datetime | |
| from pytz import timezone | |
| import torch | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| import spaces | |
| from x_transformer import * | |
| import tqdm | |
| import TMIDIX | |
| from midi_to_colab_audio import midi_to_colab_audio | |
| import matplotlib.pyplot as plt | |
| in_space = os.getenv("SYSTEM") == "spaces" | |
| # ================================================================================================= | |
| def GenerateMIDI(num_tok, idrums, iinstr): | |
| print('=' * 70) | |
| print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) | |
| start_time = time.time() | |
| print('-' * 70) | |
| print('Req num tok:', num_tok) | |
| print('Req instr:', iinstr) | |
| print('Drums:', idrums) | |
| print('-' * 70) | |
| if idrums: | |
| drums = 3074 | |
| else: | |
| drums = 3073 | |
| instruments_list = ["Piano", "Guitar", "Bass", "Violin", "Cello", "Harp", "Trumpet", "Sax", "Flute", 'Drums', | |
| "Choir", "Organ"] | |
| first_note_instrument_number = instruments_list.index(iinstr) | |
| start_tokens = [3087, drums, 3075 + first_note_instrument_number] | |
| print('Selected Improv sequence:') | |
| print(start_tokens) | |
| print('-' * 70) | |
| output_signature = 'Allegro Music Transformer' | |
| output_file_name = 'Allegro-Music-Transformer-Music-Composition' | |
| track_name = 'Project Los Angeles' | |
| list_of_MIDI_patches = [0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0] | |
| number_of_ticks_per_quarter = 500 | |
| text_encoding = 'ISO-8859-1' | |
| output_header = [number_of_ticks_per_quarter, | |
| [['track_name', 0, bytes(output_signature, text_encoding)]]] | |
| patch_list = [['patch_change', 0, 0, list_of_MIDI_patches[0]], | |
| ['patch_change', 0, 1, list_of_MIDI_patches[1]], | |
| ['patch_change', 0, 2, list_of_MIDI_patches[2]], | |
| ['patch_change', 0, 3, list_of_MIDI_patches[3]], | |
| ['patch_change', 0, 4, list_of_MIDI_patches[4]], | |
| ['patch_change', 0, 5, list_of_MIDI_patches[5]], | |
| ['patch_change', 0, 6, list_of_MIDI_patches[6]], | |
| ['patch_change', 0, 7, list_of_MIDI_patches[7]], | |
| ['patch_change', 0, 8, list_of_MIDI_patches[8]], | |
| ['patch_change', 0, 9, list_of_MIDI_patches[9]], | |
| ['patch_change', 0, 10, list_of_MIDI_patches[10]], | |
| ['patch_change', 0, 11, list_of_MIDI_patches[11]], | |
| ['patch_change', 0, 12, list_of_MIDI_patches[12]], | |
| ['patch_change', 0, 13, list_of_MIDI_patches[13]], | |
| ['patch_change', 0, 14, list_of_MIDI_patches[14]], | |
| ['patch_change', 0, 15, list_of_MIDI_patches[15]], | |
| ['track_name', 0, bytes(track_name, text_encoding)]] | |
| output = output_header + [patch_list] | |
| print('Loading model...') | |
| SEQ_LEN = 2048 | |
| # instantiate the model | |
| model = TransformerWrapper( | |
| num_tokens=3088, | |
| max_seq_len=SEQ_LEN, | |
| attn_layers=Decoder(dim=1024, depth=32, heads=8, attn_flash=True) | |
| ) | |
| model = AutoregressiveWrapper(model) | |
| model = torch.nn.DataParallel(model) | |
| model.cuda() | |
| print('=' * 70) | |
| print('Loading model checkpoint...') | |
| model.load_state_dict( | |
| torch.load('Allegro_Music_Transformer_Small_Trained_Model_56000_steps_0.9399_loss_0.7374_acc.pth', | |
| map_location='cuda')) | |
| print('=' * 70) | |
| model.eval() | |
| print('Done!') | |
| print('=' * 70) | |
| outy = start_tokens | |
| ctime = 0 | |
| dur = 0 | |
| vel = 90 | |
| pitch = 0 | |
| channel = 0 | |
| for i in range(max(1, min(1024, num_tok))): | |
| inp = torch.LongTensor([outy]).cuda() | |
| with torch.amp.autocast(device_type='cuda', dtype=torch.float16): | |
| out = model.module.generate(inp, | |
| 1, | |
| temperature=0.9, | |
| return_prime=False, | |
| verbose=False) | |
| out0 = out[0].tolist() | |
| outy.extend(out0) | |
| ss1 = out0[0] | |
| if 0 < ss1 < 256: | |
| ctime += ss1 * 8 | |
| if 256 <= ss1 < 1280: | |
| dur = ((ss1 - 256) // 8) * 32 | |
| vel = (((ss1 - 256) % 8) + 1) * 15 | |
| if 1280 <= ss1 < 2816: | |
| channel = (ss1 - 1280) // 128 | |
| pitch = (ss1 - 1280) % 128 | |
| if channel != 9: | |
| pat = list_of_MIDI_patches[channel] | |
| else: | |
| pat = 128 | |
| event = ['note', ctime, dur, channel, pitch, vel, pat] | |
| output[-1].append(event) | |
| midi_data = TMIDIX.score2midi(output, text_encoding) | |
| with open(f"Allegro-Music-Transformer-Composition.mid", 'wb') as f: | |
| f.write(midi_data) | |
| output_plot = TMIDIX.plot_ms_SONG(output[2], plot_title='Allegro-Music-Transformer-Composition', return_plt=True) | |
| audio = midi_to_colab_audio('Allegro-Music-Transformer-Composition.mid', | |
| soundfont_path="SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2", | |
| sample_rate=16000, | |
| volume_scale=10, | |
| output_for_gradio=True | |
| ) | |
| print('Sample INTs', outy[:16]) | |
| print('-' * 70) | |
| print('Last generated MIDI event', output[2][-1]) | |
| print('-' * 70) | |
| print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) | |
| print('-' * 70) | |
| print('Req execution time:', (time.time() - start_time), 'sec') | |
| return output_plot, "Allegro-Music-Transformer-Composition.mid", (16000, audio) | |
| # ================================================================================================= | |
| 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) | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Allegro Music Transformer</h1>") | |
| gr.Markdown( | |
| "\n\n" | |
| "Full-attention multi-instrumental music transformer featuring asymmetrical encoding with octo-velocity, and chords counters tokens, optimized for speed and performance\n\n" | |
| "Check out [Allegro Music Transformer](https://github.com/asigalov61/Allegro-Music-Transformer) on GitHub!\n\n" | |
| "Special thanks go out to [SkyTNT](https://github.com/SkyTNT/midi-model) for fantastic FluidSynth Synthesizer and MIDI Visualizer code\n\n" | |
| "[Open In Colab]" | |
| "(https://colab.research.google.com/github/asigalov61/Allegro-Music-Transformer/blob/main/Allegro_Music_Transformer_Composer.ipynb)" | |
| " for faster execution and endless generation" | |
| ) | |
| input_instrument = gr.Radio( | |
| ["Piano", "Guitar", "Bass", "Violin", "Cello", "Harp", "Trumpet", "Sax", "Flute", "Choir", "Organ"], | |
| value="Piano", label="Lead Instrument Controls", info="Desired lead instrument") | |
| input_drums = gr.Checkbox(label="Add Drums", value=False, info="Add drums to the composition") | |
| input_num_tokens = gr.Slider(16, 1024, value=512, label="Number of Tokens", info="Number of tokens to generate") | |
| run_btn = gr.Button("generate", variant="primary") | |
| output_plot = gr.Plot(label='output plot') | |
| output_audio = gr.Audio(label="output audio", format="mp3", elem_id="midi_audio") | |
| output_midi = gr.File(label="output midi", file_types=[".mid"]) | |
| run_event = run_btn.click(GenerateMIDI, [input_num_tokens, input_drums, input_instrument], | |
| [output_plot, output_midi, output_audio]) | |
| app.queue().launch() |