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import os.path |
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import time as reqtime |
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import datetime |
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from pytz import timezone |
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import torch |
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import spaces |
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import gradio as gr |
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from x_transformer_1_23_2 import * |
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import random |
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import tqdm |
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from midi_to_colab_audio import midi_to_colab_audio |
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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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@spaces.GPU |
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def GenerateDrums(input_midi, input_num_tokens): |
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print('=' * 70) |
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print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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start_time = reqtime.time() |
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print('Loading model...') |
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SEQ_LEN = 8192 |
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PAD_IDX = 393 |
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DEVICE = 'cuda' |
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model = TransformerWrapper( |
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num_tokens = PAD_IDX+1, |
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max_seq_len = SEQ_LEN, |
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attn_layers = Decoder(dim = 1024, depth = 4, heads = 16, attn_flash = True) |
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) |
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model = AutoregressiveWrapper(model, ignore_index = PAD_IDX) |
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model.to(DEVICE) |
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print('=' * 70) |
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print('Loading model checkpoint...') |
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model.load_state_dict( |
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torch.load('Ultimate_Drums_Transformer_Small_Trained_Model_VER4_RST_VEL_4L_9107_steps_0.5467_loss_0.8231_acc.pth', |
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map_location=DEVICE)) |
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print('=' * 70) |
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model.eval() |
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if DEVICE == 'cpu': |
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dtype = torch.bfloat16 |
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else: |
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dtype = torch.bfloat16 |
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ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype) |
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print('Done!') |
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print('=' * 70) |
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fn = os.path.basename(input_midi.name) |
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fn1 = fn.split('.')[0] |
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input_num_tokens = max(16, min(2048, input_num_tokens)) |
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print('-' * 70) |
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print('Input file name:', fn) |
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print('Req num toks:', input_num_tokens) |
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print('-' * 70) |
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raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name) |
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escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0] |
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escore_notes = [e for e in escore_notes if e[3] != 9] |
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escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32) |
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patches = TMIDIX.patch_list_from_enhanced_score_notes(escore_notes) |
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dscore = TMIDIX.delta_score_notes(escore_notes) |
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cscore = TMIDIX.chordify_score([d[1:] for d in dscore]) |
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cscore_melody = [c[0] for c in cscore] |
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comp_times = [t[1] for t in dscore if t[1] != 0] |
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comp_times = comp_times + [comp_times[-1]] |
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print('=' * 70) |
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print('Sample output events', escore_notes[:5]) |
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print('=' * 70) |
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print('Generating...') |
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output = [] |
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temperature=0.9 |
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max_drums_limit=3 |
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num_memory_tokens=4096 |
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for c in comp_times[:input_num_tokens]: |
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output.append(c) |
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x = torch.tensor([output] * 1, dtype=torch.long, device=DEVICE) |
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o = 128 |
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ncount = 0 |
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time = 0 |
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ntime = output[-1] |
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while o > 127 and ncount < max_drums_limit and time < ntime: |
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with ctx: |
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out = model.generate(x[-num_memory_tokens:], |
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1, |
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temperature=temperature, |
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return_prime=False, |
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verbose=False) |
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o = out.tolist()[0][0] |
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if 128 <= o < 256: |
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time += (o-128) |
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ncount = 0 |
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if 256 < o < 384: |
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ncount += 1 |
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if o > 127 and time < ntime: |
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x = torch.cat((x, out), 1) |
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x_output = x.tolist()[0][len(output):] |
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output.extend(x_output) |
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print('=' * 70) |
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print('Done!') |
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print('=' * 70) |
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print('Rendering results...') |
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print('=' * 70) |
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print('Sample INTs', output[:12]) |
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print('=' * 70) |
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if len(output) != 0: |
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song = output |
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song_f = [] |
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time = 0 |
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dtime = 0 |
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ntime = 0 |
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ptime = 0 |
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dur = 32 |
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vel = 90 |
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vels = [100, 120] |
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pitch = 0 |
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channel = 0 |
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idx = 0 |
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for ss in song: |
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if 0 <= ss < 128: |
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dtime = ptime = time |
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time += cscore[idx][0][0] * 32 |
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for c in cscore[idx]: |
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song_f.append(['note', time, c[1] * 32, c[2], c[3], c[4], c[5]]) |
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dtime = time |
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idx += 1 |
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if 128 <= ss < 256: |
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dtime += (ss-128) * 32 |
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if 256 < ss < 384: |
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pitch = (ss-256) |
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if 384 < ss < 393: |
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vel = (ss-384) * 15 |
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song_f.append(['note', dtime, dur, 9, pitch, vel, 128]) |
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detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, |
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output_signature = 'Ultimate Drums Transformer', |
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output_file_name = fn1, |
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track_name='Project Los Angeles', |
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list_of_MIDI_patches=patches |
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) |
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new_fn = fn1+'.mid' |
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audio = midi_to_colab_audio(new_fn, |
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soundfont_path=soundfont, |
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sample_rate=16000, |
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volume_scale=10, |
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output_for_gradio=True |
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) |
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print('Done!') |
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print('=' * 70) |
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output_midi_title = str(fn1) |
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output_midi_summary = str(song_f[:3]) |
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output_midi = str(new_fn) |
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output_audio = (16000, audio) |
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output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi, return_plt=True) |
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print('Output MIDI file name:', output_midi) |
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print('Output MIDI title:', output_midi_title) |
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print('Output MIDI summary:', '') |
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print('=' * 70) |
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print('-' * 70) |
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print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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print('-' * 70) |
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print('Req execution time:', (reqtime.time() - start_time), 'sec') |
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return [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot] |
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if __name__ == "__main__": |
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PDT = timezone('US/Pacific') |
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print('=' * 70) |
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print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) |
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print('=' * 70) |
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soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2" |
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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'>Ultimate Drums Transformer</h1>") |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique drums track for any MIDI</h1>") |
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gr.Markdown( |
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"\n\n" |
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"SOTA pure drums transformer which is capable of drums track generation for any source composition\n\n" |
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"Check out [Ultimate Drums Transformer](https://github.com/asigalov61/Ultimate-Drums-Transformer) on GitHub!\n\n" |
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"[Open In Colab]" |
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"(https://colab.research.google.com/github/asigalov61/Ultimate-Drums-Transformer/blob/main/Ultimate_Drums_Transformer.ipynb)" |
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" for faster execution and endless generation" |
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) |
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gr.Markdown("## Upload your MIDI or select a sample example MIDI") |
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input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) |
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input_num_tokens = gr.Slider(16, 2048, value=256, step=16, label="Number of composition chords to generate drums for") |
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run_btn = gr.Button("generate", variant="primary") |
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gr.Markdown("## Generation results") |
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output_midi_title = gr.Textbox(label="Output MIDI title") |
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output_midi_summary = gr.Textbox(label="Output MIDI summary") |
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output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio") |
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output_plot = gr.Plot(label="Output MIDI score plot") |
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output_midi = gr.File(label="Output MIDI file", file_types=[".mid"]) |
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run_event = run_btn.click(GenerateDrums, [input_midi, input_num_tokens], |
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[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) |
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gr.Examples( |
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[["Ultimate-Drums-Transformer-Melody-Seed-1.mid", 128], |
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["Ultimate-Drums-Transformer-Melody-Seed-2.mid", 128], |
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["Ultimate-Drums-Transformer-Melody-Seed-3.mid", 128], |
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["Ultimate-Drums-Transformer-Melody-Seed-4.mid", 128], |
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["Ultimate-Drums-Transformer-Melody-Seed-5.mid", 128], |
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["Ultimate-Drums-Transformer-Melody-Seed-6.mid", 128], |
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["Ultimate-Drums-Transformer-MI-Seed-1.mid", 128], |
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["Ultimate-Drums-Transformer-MI-Seed-2.mid", 128], |
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["Ultimate-Drums-Transformer-MI-Seed-3.mid", 128], |
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["Ultimate-Drums-Transformer-MI-Seed-4.mid", 128]], |
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[input_midi, input_num_tokens], |
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[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], |
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GenerateDrums, |
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cache_examples=True, |
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) |
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app.queue().launch() |
