import argparse import glob import json import os.path import time as reqtime import datetime from pytz import timezone import torch 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 generate_drums(notes_times, max_drums_limit = 8, num_memory_tokens = 4096, temperature=0.9): x = torch.tensor([notes_times] * 1, dtype=torch.long, device=DEVICE) o = 128 ncount = 0 while o > 127 and ncount < max_drums_limit: with ctx: out = model.generate(x[-num_memory_tokens:], 1, temperature=temperature, return_prime=False, verbose=False) o = out.tolist()[0][0] if 256 <= o < 384: ncount += 1 if o > 127: x = torch.cat((x, out), 1) return x.tolist()[0][len(notes_times):] # ================================================================================================= @torch.no_grad() def GenerateDrums(input_midi, input_num_tokens, progress=gr.Progress()): print('=' * 70) print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) start_time = reqtime.time() fn = os.path.basename(input_midi.name) fn1 = fn.split('.')[0] print('-' * 70) print('Input file name:', fn) print('Req num toks:', input_num_tokens) 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) patches = TMIDIX.patch_list_from_enhanced_score_notes(escore_notes) dscore = TMIDIX.delta_score_notes(escore_notes, compress_timings=True, even_timings=True) cscore = TMIDIX.chordify_score([d[1:] for d in dscore]) cscore_melody = [c[0] for c in cscore] comp_times = [0] + [t[1] for t in dscore if t[1] != 0] #=============================================================================== print('=' * 70) print('Sample output events', escore_notes[:5]) print('=' * 70) print('Generating...') output = [] for c in progress.tqdm(comp_times[:input_num_tokens]): output.append(c) out = generate_drums(output, temperature=0.9, max_drums_limit=8, num_memory_tokens=4096 ) output.extend(out) 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 dur = 32 vel = 90 vels = [100, 120] pitch = 0 channel = 0 idx = 0 for ss in song: if 0 <= ss < 128: dtime = 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]]) idx += 1 if 128 <= ss < 256: dtime += (ss-128) * 32 if 256 <= ss < 384: pitch = (ss-256) song_f.append(['note', dtime, dur, 9, pitch, vels[pitch % 2], 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') yield 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) 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() soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2" print('Loading model...') SEQ_LEN = 8192 # Models seq len PAD_IDX = 385 # Models pad index DEVICE = 'cuda' # instantiate the model model = TransformerWrapper( num_tokens = PAD_IDX+1, max_seq_len = SEQ_LEN, attn_layers = Decoder(dim = 1024, depth = 4, heads = 8, 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_8134_steps_0.3745_loss_0.8736_acc.pth', map_location=DEVICE)) print('=' * 70) model.eval() if DEVICE == 'cpu': dtype = torch.bfloat16 else: dtype = torch.float16 ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype) print('Done!') print('=' * 70) app = gr.Blocks() with app: gr.Markdown("

Ultimate Drums Transformer

") gr.Markdown("

Generate unique drums track for any MIDI

") gr.Markdown( "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Ultimate-Drums-Transformer&style=flat)\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") input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) input_num_tokens = gr.Slider(16, 512, value=256, label="Number of Tokens", info="Number of tokens to generate") gr.Examples( [["Allegro-Music-Transformer-MI-Seed-1.mid"], ["Allegro-Music-Transformer-MI-Seed-2.mid"]], [input_midi, input_num_tokens], [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], GenerateDrums, cache_examples=True, ) 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], [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) app.queue(concurrency_count=8).launch(server_port=opt.port, share=opt.share, inbrowser=True)