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| import os.path | |
| import time as reqtime | |
| import datetime | |
| from pytz import timezone | |
| import torch | |
| import spaces | |
| import gradio as gr | |
| from x_transformer_1_23_2 import * | |
| import random | |
| import tqdm | |
| from midi_to_colab_audio import midi_to_colab_audio | |
| import TMIDIX | |
| import matplotlib.pyplot as plt | |
| in_space = os.getenv("SYSTEM") == "spaces" | |
| # ================================================================================================= | |
| def GenerateDrums(input_midi, input_num_tokens, input_top_k_value, input_max_drums_per_step): | |
| 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 = 8192 # Models seq len | |
| PAD_IDX = 393 # Models pad index | |
| DEVICE = 'cuda' # 'cuda' | |
| # instantiate the model | |
| model = TransformerWrapper( | |
| num_tokens = PAD_IDX+1, | |
| max_seq_len = SEQ_LEN, | |
| attn_layers = Decoder(dim = 1024, depth = 4, 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('Ultimate_Drums_Transformer_Small_Trained_Model_VER4_RST_VEL_4L_9107_steps_0.5467_loss_0.8231_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) | |
| fn = os.path.basename(input_midi.name) | |
| fn1 = fn.split('.')[0] | |
| input_num_tokens = max(16, min(2048, input_num_tokens)) | |
| print('-' * 70) | |
| print('Input file name:', fn) | |
| print('Req num toks:', input_num_tokens) | |
| print('Req top_k value:', input_top_k_value) | |
| print('Req max number of drums pitches:', input_max_drums_per_step) | |
| print('-' * 70) | |
| #=============================================================================== | |
| # Raw single-track ms score | |
| raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name) | |
| #=============================================================================== | |
| # Enhanced score notes | |
| escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0] | |
| #======================================================= | |
| # PRE-PROCESSING | |
| #=============================================================================== | |
| # Augmented enhanced score notes | |
| escore_notes = [e for e in escore_notes if e[3] != 9] | |
| escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32) | |
| patches = TMIDIX.patch_list_from_enhanced_score_notes(escore_notes) | |
| dscore = TMIDIX.delta_score_notes(escore_notes) | |
| cscore = TMIDIX.chordify_score([d[1:] for d in dscore]) | |
| cscore_melody = [c[0] for c in cscore] | |
| comp_times = [t[1] for t in dscore if t[1] != 0] | |
| comp_times = comp_times + [comp_times[-1]] | |
| #=============================================================================== | |
| print('=' * 70) | |
| print('Sample output events', escore_notes[:5]) | |
| print('=' * 70) | |
| print('Generating...') | |
| output = [] | |
| temperature=0.9 | |
| max_drums_limit=input_max_drums_per_step | |
| num_memory_tokens=4096 | |
| for c in comp_times[:input_num_tokens]: | |
| output.append(c) | |
| x = torch.tensor([output] * 1, dtype=torch.long, device=DEVICE) | |
| o = 128 | |
| ncount = 0 | |
| time = 0 | |
| ntime = output[-1] | |
| while o > 127 and ncount < max_drums_limit and time < ntime: | |
| with ctx: | |
| out = model.generate(x[-num_memory_tokens:], | |
| 1, | |
| filter_logits_fn=top_k, | |
| filter_kwargs={'k': input_top_k_value}, | |
| temperature=temperature, | |
| return_prime=False, | |
| verbose=False) | |
| o = out.tolist()[0][0] | |
| if 128 <= o < 256: | |
| time += (o-128) | |
| ncount = 0 | |
| if 256 < o < 384: | |
| ncount += 1 | |
| if o > 127 and time < ntime: | |
| x = torch.cat((x, out), 1) | |
| x_output = x.tolist()[0][len(output):] | |
| output.extend(x_output) | |
| print('=' * 70) | |
| print('Done!') | |
| print('=' * 70) | |
| #=============================================================================== | |
| print('Rendering results...') | |
| print('=' * 70) | |
| print('Sample INTs', output[:12]) | |
| print('=' * 70) | |
| if len(output) != 0: | |
| song = output | |
| song_f = [] | |
| time = 0 | |
| dtime = 0 | |
| ntime = 0 | |
| ptime = 0 | |
| dur = 32 | |
| vel = 90 | |
| vels = [100, 120] | |
| pitch = 0 | |
| channel = 0 | |
| idx = 0 | |
| for ss in song: | |
| if 0 <= ss < 128: | |
| dtime = ptime = time | |
| time += cscore[idx][0][0] * 32 | |
| for c in cscore[idx]: | |
| song_f.append(['note', time, c[1] * 32, c[2], c[3], c[4], c[5]]) | |
| dtime = time | |
| idx += 1 | |
| if 128 <= ss < 256: | |
| dtime += (ss-128) * 32 | |
| if 256 < ss < 384: | |
| pitch = (ss-256) | |
| if 384 < ss < 393: | |
| vel = (ss-384) * 15 | |
| song_f.append(['note', dtime, dur, 9, pitch, vel, 128]) | |
| detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, | |
| output_signature = 'Ultimate Drums 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:', '') | |
| 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" | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Ultimate Drums Transformer</h1>") | |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique drums track for any MIDI</h1>") | |
| gr.Markdown( | |
| "\n\n" | |
| "SOTA pure drums transformer which is capable of drums track generation for any source composition\n\n" | |
| "Check out [Ultimate Drums Transformer](https://github.com/asigalov61/Ultimate-Drums-Transformer) on GitHub!\n\n" | |
| "[Open In Colab]" | |
| "(https://colab.research.google.com/github/asigalov61/Ultimate-Drums-Transformer/blob/main/Ultimate_Drums_Transformer.ipynb)" | |
| " for faster execution and endless generation" | |
| ) | |
| gr.Markdown("## Upload your MIDI or select a sample example MIDI") | |
| input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) | |
| input_num_tokens = gr.Slider(16, 2048, value=256, step=16, label="Number of composition chords to generate drums for") | |
| input_top_k_value = gr.Slider(1, 50, value=5, step=1, label="Model sampling top_k value") | |
| input_max_drums_per_step = gr.Slider(1, 10, value=5, step=1, label="Maximum number of drums pitches per step") | |
| run_btn = gr.Button("generate", variant="primary") | |
| gr.Markdown("## Generation results") | |
| output_midi_title = gr.Textbox(label="Output MIDI title") | |
| output_midi_summary = gr.Textbox(label="Output MIDI summary") | |
| output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio") | |
| output_plot = gr.Plot(label="Output MIDI score plot") | |
| output_midi = gr.File(label="Output MIDI file", file_types=[".mid"]) | |
| run_event = run_btn.click(GenerateDrums, [input_midi, input_num_tokens, input_top_k_value, input_max_drums_per_step], | |
| [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) | |
| gr.Examples( | |
| [["Ultimate-Drums-Transformer-Melody-Seed-1.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-Melody-Seed-2.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-Melody-Seed-3.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-Melody-Seed-4.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-Melody-Seed-5.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-Melody-Seed-6.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-MI-Seed-1.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-MI-Seed-2.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-MI-Seed-3.mid", 128, 5, 5], | |
| ["Ultimate-Drums-Transformer-MI-Seed-4.mid", 128, 5, 5]], | |
| [input_midi, input_num_tokens, input_top_k_value, input_max_drums_per_step], | |
| [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], | |
| GenerateDrums, | |
| cache_examples=True, | |
| ) | |
| app.queue().launch() |