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| #================================================================================== | |
| # https://huggingface.co/spaces/asigalov61/Guided-Accompaniment-Transformer | |
| #================================================================================== | |
| print('=' * 70) | |
| print('Guided Accompaniment Transformer Gradio App') | |
| print('=' * 70) | |
| print('Loading core Guided Accompaniment Transformer modules...') | |
| import os | |
| import copy | |
| import time as reqtime | |
| import datetime | |
| from pytz import timezone | |
| print('=' * 70) | |
| print('Loading main Guided Accompaniment Transformer modules...') | |
| os.environ['USE_FLASH_ATTENTION'] = '1' | |
| import torch | |
| torch.set_float32_matmul_precision('high') | |
| torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul | |
| torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn | |
| torch.backends.cuda.enable_mem_efficient_sdp(True) | |
| torch.backends.cuda.enable_math_sdp(True) | |
| torch.backends.cuda.enable_flash_sdp(True) | |
| torch.backends.cuda.enable_cudnn_sdp(True) | |
| from huggingface_hub import hf_hub_download | |
| import TMIDIX | |
| from midi_to_colab_audio import midi_to_colab_audio | |
| from x_transformer_1_23_2 import * | |
| import random | |
| import tqdm | |
| print('=' * 70) | |
| print('Loading aux Guided Accompaniment Transformer modules...') | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| import spaces | |
| print('=' * 70) | |
| print('PyTorch version:', torch.__version__) | |
| print('=' * 70) | |
| print('Done!') | |
| print('Enjoy! :)') | |
| print('=' * 70) | |
| #================================================================================== | |
| MODEL_CHECKPOINT = 'Guided_Accompaniment_Transformer_Trained_Model_36457_steps_0.5384_loss_0.8417_acc.pth' | |
| SOUDFONT_PATH = 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2' | |
| #================================================================================== | |
| print('=' * 70) | |
| print('Instantiating model...') | |
| device_type = 'cuda' | |
| dtype = 'bfloat16' | |
| ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype] | |
| ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) | |
| SEQ_LEN = 4096 | |
| PAD_IDX = 1794 | |
| model = TransformerWrapper( | |
| num_tokens = PAD_IDX+1, | |
| max_seq_len = SEQ_LEN, | |
| attn_layers = Decoder(dim = 2048, | |
| depth = 4, | |
| heads = 32, | |
| rotary_pos_emb = True, | |
| attn_flash = True | |
| ) | |
| ) | |
| model = AutoregressiveWrapper(model, ignore_index=PAD_IDX, pad_value=PAD_IDX) | |
| print('=' * 70) | |
| print('Loading model checkpoint...') | |
| model_checkpoint = hf_hub_download(repo_id='asigalov61/Guided-Accompaniment-Transformer', filename=MODEL_CHECKPOINT) | |
| model.load_state_dict(torch.load(model_checkpoint, map_location='cpu', weights_only=True)) | |
| model = torch.compile(model, mode='max-autotune') | |
| print('=' * 70) | |
| print('Done!') | |
| print('=' * 70) | |
| print('Model will use', dtype, 'precision...') | |
| print('=' * 70) | |
| #================================================================================== | |
| def load_midi(input_midi, melody_patch=-1): | |
| raw_score = TMIDIX.midi2single_track_ms_score(input_midi) | |
| escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0] | |
| escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32) | |
| sp_escore_notes = TMIDIX.solo_piano_escore_notes(escore_notes, keep_drums=False) | |
| if melody_patch == -1: | |
| zscore = TMIDIX.recalculate_score_timings(sp_escore_notes) | |
| else: | |
| zscore = TMIDIX.recalculate_score_timings([e for e in sp_escore_notes if e[6] == melody_patch]) | |
| cscore = TMIDIX.chordify_score([1000, zscore]) | |
| score = [] | |
| score_list = [] | |
| pc = cscore[0] | |
| for c in cscore: | |
| score.append(max(0, min(127, c[0][1]-pc[0][1]))) | |
| scl = [[max(0, min(127, c[0][1]-pc[0][1]))]] | |
| n = c[0] | |
| score.extend([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256]) | |
| scl.append([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256]) | |
| score_list.append(scl) | |
| pc = c | |
| score_list.append(scl) | |
| return score, score_list | |
| #================================================================================== | |
| def Generate_Accompaniment(input_midi, | |
| generation_type, | |
| melody_patch, | |
| model_temperature | |
| ): | |
| #=============================================================================== | |
| def generate_full_seq(input_seq, temperature=0.9, verbose=True): | |
| seq_abs_run_time = sum([t for t in input_seq if t < 128]) | |
| cur_time = 0 | |
| full_seq = input_seq | |
| toks_counter = 0 | |
| while cur_time < seq_abs_run_time: | |
| if verbose: | |
| if toks_counter % 128 == 0: | |
| print('Generated', toks_counter, 'tokens') | |
| x = torch.LongTensor(full_seq).cuda() | |
| with ctx: | |
| out = model.generate(x, | |
| 1, | |
| temperature=temperature, | |
| return_prime=False, | |
| verbose=False) | |
| y = out.tolist()[0][0] | |
| if y < 128: | |
| cur_time += y | |
| full_seq.append(y) | |
| toks_counter += 1 | |
| return full_seq | |
| #=============================================================================== | |
| def generate_block_seq(input_seq, trg_dtime, temperature=0.9): | |
| cur_time = 0 | |
| block_seq = [128] | |
| while cur_time != trg_dtime and len(block_seq) < 2 and block_seq[-1] > 127: | |
| inp_seq = copy.deepcopy(input_seq) | |
| block_seq = [] | |
| cur_time = 0 | |
| while cur_time < trg_dtime: | |
| x = torch.LongTensor(inp_seq).cuda() | |
| with ctx: | |
| out = model.generate(x, | |
| 1, | |
| temperature=temperature, | |
| return_prime=False, | |
| verbose=False) | |
| y = out.tolist()[0][0] | |
| if y < 128: | |
| cur_time += y | |
| inp_seq.append(y) | |
| block_seq.append(y) | |
| return block_seq | |
| #=============================================================================== | |
| print('=' * 70) | |
| print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) | |
| start_time = reqtime.time() | |
| print('=' * 70) | |
| fn = os.path.basename(input_midi) | |
| fn1 = fn.split('.')[0] | |
| print('=' * 70) | |
| print('Requested settings:') | |
| print('=' * 70) | |
| print('Input MIDI file name:', fn) | |
| print('Generation type:', generation_type) | |
| print('Source melody patch:', melody_patch) | |
| print('Model temperature:', model_temperature) | |
| print('=' * 70) | |
| #================================================================== | |
| score, score_list = load_midi(input_midi.name) | |
| print('Sample score events', score[:12]) | |
| #================================================================== | |
| print('=' * 70) | |
| print('Generating...') | |
| model.to(device_type) | |
| model.eval() | |
| #================================================================== | |
| start_score_seq = [1792] + score + [1793] | |
| #================================================================== | |
| if generation_type == 'Guided': | |
| input_seq = [] | |
| input_seq.extend(start_score_seq) | |
| input_seq.extend(score_list[0][0]) | |
| for i in tqdm.tqdm(range(len(score_list)-1)): | |
| input_seq.extend(score_list[i][1]) | |
| block_seq = generate_block_seq(input_seq, score_list[i+1][0][0], temperature=model_temperature) | |
| input_seq.extend(block_seq) | |
| else: | |
| input_seq = generate_full_seq(start_score_seq, temperature=model_temperature) | |
| #================================================================== | |
| final_song = input_seq[len(start_score_seq):] | |
| #================================================================== | |
| print('=' * 70) | |
| print('Done!') | |
| print('=' * 70) | |
| #=============================================================================== | |
| print('Rendering results...') | |
| print('=' * 70) | |
| print('Sample INTs', final_song[:15]) | |
| print('=' * 70) | |
| song_f = [] | |
| if len(final_song) != 0: | |
| time = 0 | |
| dur = 0 | |
| vel = 90 | |
| pitch = 0 | |
| channel = 0 | |
| patch = 0 | |
| channels_map = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 9, 12, 13, 14, 15] | |
| patches_map = [40, 0, 10, 19, 24, 35, 40, 52, 56, 9, 65, 73, 0, 0, 0, 0] | |
| velocities_map = [125, 80, 100, 80, 90, 100, 100, 80, 110, 110, 110, 110, 80, 80, 80, 80] | |
| for m in final_song: | |
| if 0 <= m < 128: | |
| time += m * 32 | |
| elif 128 < m < 256: | |
| dur = (m-128) * 32 | |
| elif 256 < m < 1792: | |
| cha = (m-256) // 128 | |
| pitch = (m-256) % 128 | |
| channel = channels_map[cha] | |
| patch = patches_map[channel] | |
| vel = velocities_map[channel] | |
| song_f.append(['note', time, dur, channel, pitch, vel, patch]) | |
| fn1 = "Guided-Accompaniment-Transformer-Composition" | |
| detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, | |
| output_signature = 'Guided Accompaniment Transformer', | |
| output_file_name = fn1, | |
| track_name='Project Los Angeles', | |
| list_of_MIDI_patches=patches_map | |
| ) | |
| 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 = 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('=' * 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_audio, output_plot, output_midi | |
| #================================================================================== | |
| PDT = timezone('US/Pacific') | |
| print('=' * 70) | |
| print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) | |
| print('=' * 70) | |
| #================================================================================== | |
| with gr.Blocks() as demo: | |
| #================================================================================== | |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Guided Accompaniment Transformer</h1>") | |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Guided melody accompaniment generation with transformers</h1>") | |
| gr.HTML(""" | |
| Check out <a href="https://github.com/asigalov61/monsterpianotransformer">Guided Accompaniment Transformer</a> on GitHub or on | |
| <p> | |
| <a href="https://pypi.org/project/monsterpianotransformer/"> | |
| <img src="https://upload.wikimedia.org/wikipedia/commons/6/64/PyPI_logo.svg" alt="PyPI Project" style="width: 100px; height: auto;"> | |
| </a> or | |
| <a href="https://huggingface.co/spaces/asigalov61/Guided-Accompaniment-Transformer?duplicate=true"> | |
| <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate in Hugging Face"> | |
| </a> | |
| </p> | |
| for faster execution and endless generation! | |
| """) | |
| #================================================================================== | |
| gr.Markdown("## Upload source melody MIDI") | |
| input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) | |
| gr.Markdown("## Generation options") | |
| generation_type = gr.Radio(["Guided", "Freestyle"], value="Guided", label="Generation type") | |
| melody_patch = gr.Slider(-1, 127, value=-1, step=1, label="Source melody MIDI patch") | |
| model_temperature = gr.Slider(0.1, 1, value=0.9, step=0.01, label="Model temperature") | |
| generate_btn = gr.Button("Generate", variant="primary") | |
| gr.Markdown("## Generation results") | |
| output_audio = gr.Audio(label="MIDI audio", format="wav", elem_id="midi_audio") | |
| output_plot = gr.Plot(label="MIDI score plot") | |
| output_midi = gr.File(label="MIDI file", file_types=[".mid"]) | |
| generate_btn.click(Generate_Accompaniment, | |
| [input_midi, | |
| generation_type, | |
| melody_patch, | |
| model_temperature | |
| ], | |
| [ | |
| output_audio, | |
| output_plot, | |
| output_midi, | |
| ] | |
| ) | |
| '''gr.Examples( | |
| [["asap_midi_score_21.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5], | |
| ["asap_midi_score_45.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5], | |
| ["asap_midi_score_69.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5], | |
| ["asap_midi_score_118.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5], | |
| ["asap_midi_score_167.mid", "Score", "Durations and Velocities", 8, 600, 1, 1.1, 1.5], | |
| ], | |
| [input_midi, | |
| input_midi_type, | |
| input_conv_type, | |
| input_number_prime_notes, | |
| input_number_conv_notes, | |
| input_model_dur_top_k, | |
| input_model_dur_temperature, | |
| input_model_vel_temperature | |
| ], | |
| [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], | |
| Convert_Score_to_Performance | |
| )''' | |
| #================================================================================== | |
| demo.launch() | |
| #================================================================================== |