import argparse import glob import json import os.path import torch import torch.nn.functional as F import gradio as gr from x_transformer import * import tqdm from midi_synthesizer import synthesis import TMIDIX import matplotlib.pyplot as plt in_space = os.getenv("SYSTEM") == "spaces" # ================================================================================================= @torch.no_grad() def GenerateMIDI(num_tok, idrums, iinstr): 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] yield output, None, None, [create_msg("visualizer_clear", None)] outy = start_tokens time = 0 dur = 0 vel = 0 pitch = 0 channel = 0 for i in range(max(1, min(512, num_tok))): inp = torch.LongTensor([outy]).cpu() out = model.module.generate(inp, 1, temperature=0.9, return_prime=False, verbose=False) out0 = out[0].tolist() outy.extend(out0) ss1 = int(out0[0]) if 0 < ss1 < 256: time += 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 event = ['note', int(time), int(dur), int(channel), int(pitch), int(vel)] output[-1].append(event) yield output, None, None, [create_msg("visualizer_append", event), create_msg("progress", [i + 1, num_tok])] midi_data = TMIDIX.score2midi(output, text_encoding) with open(f"Allegro-Music-Transformer-Music-Composition.mid", 'wb') as f: f.write(midi_data) audio = synthesis(TMIDIX.score2opus(output), 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2') yield output, "Allegro-Music-Transformer-Music-Composition.mid", (44100, audio), [ create_msg("visualizer_end", None)] def cancel_run(mid_seq): if mid_seq is None: return None, None, None text_encoding = 'ISO-8859-1' midi_data = TMIDIX.score2midi(mid_seq, text_encoding) with open(f"Allegro-Music-Transformer-Music-Composition.mid", 'wb') as f: f.write(midi_data) audio = synthesis(TMIDIX.score2opus(mid_seq), 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2') yield "Allegro-Music-Transformer-Music-Composition.mid", (44100, audio), [ create_msg("visualizer_end", None)] # ================================================================================================= def load_javascript(dir="javascript"): scripts_list = glob.glob(f"{dir}/*.js") javascript = "" for path in scripts_list: with open(path, "r", encoding="utf8") as jsfile: javascript += f"\n" template_response_ori = gr.routes.templates.TemplateResponse def template_response(*args, **kwargs): res = template_response_ori(*args, **kwargs) res.body = res.body.replace( b'', f'{javascript}'.encode("utf8")) res.init_headers() return res gr.routes.templates.TemplateResponse = template_response class JSMsgReceiver(gr.HTML): def __init__(self, **kwargs): super().__init__(elem_id="msg_receiver", visible=False, **kwargs) def postprocess(self, y): if y: y = f"

{json.dumps(y)}

" return super().postprocess(y) def get_block_name(self) -> str: return "html" def create_msg(name, data): return {"name": name, "data": data} 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...') 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) ) model = AutoregressiveWrapper(model) model = torch.nn.DataParallel(model) model.cpu() 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='cpu')) print('=' * 70) model.eval() print('Done!') load_javascript() app = gr.Blocks() with app: gr.Markdown("

Allegro Music Transformer

") gr.Markdown( "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Allegro-Music-Transformer&style=flat)\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" ) js_msg = JSMsgReceiver() input_drums = gr.Checkbox(label="Add Drums", value=False, info="Add drums to the composition") 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_num_tokens = gr.Slider(16, 512, value=256, label="Number of Tokens", info="Number of tokens to generate") run_btn = gr.Button("generate", variant="primary") interrupt_btn = gr.Button("interrupt") output_midi_seq = gr.Variable() output_midi_visualizer = gr.HTML(elem_id="midi_visualizer_container") 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_midi_seq, output_midi, output_audio, js_msg]) interrupt_btn.click(cancel_run, output_midi_seq, [output_midi, output_audio, js_msg], cancels=run_event, queue=False) app.queue(concurrency_count=1).launch(server_port=opt.port, share=opt.share, inbrowser=True)