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| import argparse | |
| import glob | |
| import os.path | |
| import torch | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| import onnxruntime as rt | |
| import tqdm | |
| from midi_synthesizer import synthesis | |
| import TMIDIX | |
| import matplotlib.pyplot as plt | |
| in_space = os.getenv("SYSTEM") == "spaces" | |
| #================================================================================================= | |
| def GenerateMIDI(idrums, iinstr, progress=gr.Progress()): | |
| 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] | |
| seq_len = 512 | |
| max_seq_len = 2048 | |
| temperature = 0.9 | |
| verbose=False | |
| return_prime=False | |
| out = torch.FloatTensor([start_tokens]) | |
| st = len(start_tokens) | |
| if verbose: | |
| print("Generating sequence of max length:", seq_len) | |
| progress(0, desc="Starting...") | |
| step = 0 | |
| for i in progress.tqdm(range(seq_len)): | |
| try: | |
| x = out[:, -max_seq_len:] | |
| torch_in = x.tolist()[0] | |
| logits = torch.FloatTensor(session.run(None, {'input': [torch_in]})[0])[:, -1] | |
| probs = F.softmax(logits / temperature, dim=-1) | |
| sample = torch.multinomial(probs, 1) | |
| out = torch.cat((out, sample), dim=-1) | |
| if step % 16 == 0: | |
| print(step, '/', seq_len) | |
| step += 1 | |
| if step >= seq_len: | |
| break | |
| except Exception as e: | |
| print('Error', e) | |
| break | |
| if return_prime: | |
| melody_chords_f = out[:, :] | |
| else: | |
| melody_chords_f = out[:, st:] | |
| melody_chords_f = melody_chords_f.tolist()[0] | |
| print('=' * 70) | |
| print('Sample INTs', melody_chords_f[:12]) | |
| print('=' * 70) | |
| if len(melody_chords_f) != 0: | |
| song = melody_chords_f | |
| song_f = [] | |
| time = 0 | |
| dur = 0 | |
| vel = 0 | |
| pitch = 0 | |
| channel = 0 | |
| for ss in song: | |
| ss1 = int(ss) | |
| if ss1 > 0 and ss1 < 256: | |
| time += ss1 * 8 | |
| if ss1 >= 256 and ss1 < 1280: | |
| dur = ((ss1-256) // 8) * 32 | |
| vel = (((ss1-256) % 8)+1) * 15 | |
| if ss1 >= 1280 and ss1 < 2816: | |
| channel = (ss1-1280) // 128 | |
| pitch = (ss1-1280) % 128 | |
| song_f.append(['note', int(time), int(dur), int(channel), int(pitch), int(vel) ]) | |
| 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 + song_f] | |
| midi_data = TMIDIX.score2midi(output, text_encoding) | |
| with open(f"Allegro-Music-Transformer-Music-Composition.mid", 'wb') as f: | |
| f.write(midi_data) | |
| output1 = [] | |
| itrack = 1 | |
| opus = TMIDIX.score2opus(output) | |
| while itrack < len(opus): | |
| for event in opus[itrack]: | |
| if (event[0] == 'note_on') or (event[0] == 'note_off'): | |
| output1.append(event) | |
| itrack += 1 | |
| audio = synthesis([500, output1], 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2') | |
| x = [] | |
| y =[] | |
| c = [] | |
| colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver'] | |
| for s in song_f: | |
| x.append(s[1] / 1000) | |
| y.append(s[4]) | |
| c.append(colors[s[3]]) | |
| plt.figure(figsize=(14,5)) | |
| ax=plt.axes(title='Allegro Music Transformer') | |
| ax.set_facecolor('black') | |
| plt.scatter(x,y, c=c) | |
| plt.xlabel("Time") | |
| plt.ylabel("Pitch") | |
| yield [500, output1], plt, "Allegro-Music-Transformer-Music-Composition.mid", (44100, audio) | |
| #================================================================================================= | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--share", action="store_true", default=False, help="share gradio app") | |
| parser.add_argument("--port", type=int, default=7860, help="gradio server port") | |
| opt = parser.parse_args() | |
| print('Loading model...') | |
| session = rt.InferenceSession('Allegro_Music_Transformer_Small_Trained_Model_56000_steps_0.9399_loss_0.7374_acc.onnx', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) | |
| print('Done!') | |
| 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" | |
| "[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_drums = gr.Checkbox(label="Drums Controls", value = True, info="Drums present or not") | |
| input_instrument = gr.Radio(["Piano", "Guitar", "Bass", "Violin", "Cello", "Harp", "Trumpet", "Sax", "Flute", "Choir", "Organ"], value="Guitar", label="Lead Instrument Controls", info="Desired lead instrument") | |
| run_btn = gr.Button("generate", variant="primary") | |
| output_midi_seq = gr.Variable() | |
| output_audio = gr.Audio(label="output audio", format="mp3", elem_id="midi_audio") | |
| output_plot = gr.Plot(label="output plot") | |
| output_midi = gr.File(label="output midi", file_types=[".mid"]) | |
| run_event = run_btn.click(GenerateMIDI, [input_drums, input_instrument], [output_midi_seq, output_plot, output_midi, output_audio]) | |
| app.queue(concurrency_count=1).launch(server_port=opt.port, share=opt.share, inbrowser=True) |