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import argparse |
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import glob |
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import os.path |
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import torch |
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import torch.nn.functional as F |
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import gradio as gr |
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import onnxruntime as rt |
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import tqdm |
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from midi_synthesizer import synthesis |
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import TMIDIX |
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import matplotlib.pyplot as plt |
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in_space = os.getenv("SYSTEM") == "spaces" |
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@torch.no_grad() |
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def GenerateMIDI(idrums, iinstr, progress=gr.Progress()): |
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if idrums: |
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drums = 3074 |
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else: |
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drums = 3073 |
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instruments_list = ["Piano", "Guitar", "Bass", "Violin", "Cello", "Harp", "Trumpet", "Sax", "Flute", 'Drums', "Choir", "Organ"] |
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first_note_instrument_number = instruments_list.index(iinstr) |
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start_tokens = [3087, drums, 3075+first_note_instrument_number] |
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seq_len = 512 |
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max_seq_len = 2048 |
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temperature = 0.9 |
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verbose=False |
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return_prime=False |
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out = torch.FloatTensor([start_tokens]) |
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st = len(start_tokens) |
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if verbose: |
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print("Generating sequence of max length:", seq_len) |
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progress(0, desc="Starting...") |
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step = 0 |
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for i in progress.tqdm(range(seq_len)): |
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try: |
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x = out[:, -max_seq_len:] |
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torch_in = x.tolist()[0] |
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logits = torch.FloatTensor(session.run(None, {'input': [torch_in]})[0])[:, -1] |
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probs = F.softmax(logits / temperature, dim=-1) |
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sample = torch.multinomial(probs, 1) |
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out = torch.cat((out, sample), dim=-1) |
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if step % 16 == 0: |
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print(step, '/', seq_len) |
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step += 1 |
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if step >= seq_len: |
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break |
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except Exception as e: |
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print('Error', e) |
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break |
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if return_prime: |
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melody_chords_f = out[:, :] |
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else: |
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melody_chords_f = out[:, st:] |
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melody_chords_f = melody_chords_f.tolist()[0] |
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print('=' * 70) |
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print('Sample INTs', melody_chords_f[:12]) |
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print('=' * 70) |
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if len(melody_chords_f) != 0: |
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song = melody_chords_f |
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song_f = [] |
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time = 0 |
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dur = 0 |
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vel = 0 |
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pitch = 0 |
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channel = 0 |
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for ss in song: |
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ss1 = int(ss) |
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if ss1 > 0 and ss1 < 256: |
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time += ss1 * 8 |
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if ss1 >= 256 and ss1 < 1280: |
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dur = ((ss1-256) // 8) * 32 |
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vel = (((ss1-256) % 8)+1) * 15 |
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if ss1 >= 1280 and ss1 < 2816: |
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channel = (ss1-1280) // 128 |
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pitch = (ss1-1280) % 128 |
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song_f.append(['note', int(time), int(dur), int(channel), int(pitch), int(vel) ]) |
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output_signature = 'Allegro Music Transformer' |
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output_file_name = 'Allegro-Music-Transformer-Music-Composition' |
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track_name='Project Los Angeles' |
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list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0] |
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number_of_ticks_per_quarter=500 |
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text_encoding='ISO-8859-1' |
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output_header = [number_of_ticks_per_quarter, |
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[['track_name', 0, bytes(output_signature, text_encoding)]]] |
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patch_list = [['patch_change', 0, 0, list_of_MIDI_patches[0]], |
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['patch_change', 0, 1, list_of_MIDI_patches[1]], |
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['patch_change', 0, 2, list_of_MIDI_patches[2]], |
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['patch_change', 0, 3, list_of_MIDI_patches[3]], |
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['patch_change', 0, 4, list_of_MIDI_patches[4]], |
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['patch_change', 0, 5, list_of_MIDI_patches[5]], |
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['patch_change', 0, 6, list_of_MIDI_patches[6]], |
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['patch_change', 0, 7, list_of_MIDI_patches[7]], |
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['patch_change', 0, 8, list_of_MIDI_patches[8]], |
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['patch_change', 0, 9, list_of_MIDI_patches[9]], |
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['patch_change', 0, 10, list_of_MIDI_patches[10]], |
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['patch_change', 0, 11, list_of_MIDI_patches[11]], |
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['patch_change', 0, 12, list_of_MIDI_patches[12]], |
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['patch_change', 0, 13, list_of_MIDI_patches[13]], |
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['patch_change', 0, 14, list_of_MIDI_patches[14]], |
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['patch_change', 0, 15, list_of_MIDI_patches[15]], |
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['track_name', 0, bytes(track_name, text_encoding)]] |
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output = output_header + [patch_list + song_f] |
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midi_data = TMIDIX.score2midi(output, text_encoding) |
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with open(f"Allegro-Music-Transformer-Music-Composition.mid", 'wb') as f: |
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f.write(midi_data) |
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output1 = [] |
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itrack = 1 |
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opus = TMIDIX.score2opus(output) |
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while itrack < len(opus): |
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for event in opus[itrack]: |
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if (event[0] == 'note_on') or (event[0] == 'note_off'): |
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output1.append(event) |
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itrack += 1 |
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audio = synthesis([500, output1], 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2') |
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x = [] |
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y =[] |
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c = [] |
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colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver'] |
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for s in song_f: |
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x.append(s[1] / 1000) |
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y.append(s[4]) |
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c.append(colors[s[3]]) |
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plt.figure(figsize=(14,5)) |
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ax=plt.axes(title='Allegro Music Transformer') |
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ax.set_facecolor('black') |
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plt.scatter(x,y, c=c) |
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plt.xlabel("Time") |
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plt.ylabel("Pitch") |
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yield [500, output1], plt, "Allegro-Music-Transformer-Music-Composition.mid", (44100, audio) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--share", action="store_true", default=False, help="share gradio app") |
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parser.add_argument("--port", type=int, default=7860, help="gradio server port") |
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opt = parser.parse_args() |
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print('Loading model...') |
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session = rt.InferenceSession('Allegro_Music_Transformer_Small_Trained_Model_56000_steps_0.9399_loss_0.7374_acc.onnx', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
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print('Done!') |
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app = gr.Blocks() |
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with app: |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Allegro Music Transformer</h1>") |
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gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Allegro-Music-Transformer&style=flat)\n\n" |
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"Full-attention multi-instrumental music transformer featuring asymmetrical encoding with octo-velocity, and chords counters tokens, optimized for speed and performance\n\n" |
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"Check out [Allegro Music Transformer](https://github.com/asigalov61/Allegro-Music-Transformer) on GitHub!\n\n" |
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"[Open In Colab]" |
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"(https://colab.research.google.com/github/asigalov61/Allegro-Music-Transformer/blob/main/Allegro_Music_Transformer_Composer.ipynb)" |
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" for faster execution and endless generation" |
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) |
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input_drums = gr.Checkbox(label="Drums Controls", value = True, info="Drums present or not") |
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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") |
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run_btn = gr.Button("generate", variant="primary") |
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output_midi_seq = gr.Variable() |
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output_audio = gr.Audio(label="output audio", format="mp3", elem_id="midi_audio") |
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output_plot = gr.Plot(label="output plot") |
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output_midi = gr.File(label="output midi", file_types=[".mid"]) |
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run_event = run_btn.click(GenerateMIDI, [input_drums, input_instrument], [output_midi_seq, output_plot, output_midi, output_audio]) |
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app.queue(concurrency_count=1).launch(server_port=opt.port, share=opt.share, inbrowser=True) |