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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 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 | |
| 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_CHECKPOINTS = 'Guided_Accompaniment_Transformer_Trained_Model_59896_steps_0.9055_loss_0.735_acc.pth' | |
| SOUDFONT_PATH = 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2' | |
| #================================================================================== | |
| def load_model(): | |
| 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 = 2048 | |
| if model_selector == 'with velocity - 3 epochs': | |
| PAD_IDX = 512 | |
| else: | |
| PAD_IDX = 384 | |
| 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_CHECKPOINTS[model_selector]) | |
| 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) | |
| return [model, ctx] | |
| #================================================================================== | |
| def load_midi(input_midi, model_selector=''): | |
| raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name) | |
| 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) | |
| zscore = TMIDIX.recalculate_score_timings(sp_escore_notes) | |
| cscore = TMIDIX.chordify_score([1000, zscore]) | |
| score = [] | |
| pc = cscore[0] | |
| for c in cscore: | |
| score.append(max(0, min(127, c[0][1]-pc[0][1]))) | |
| for n in c: | |
| if model_selector == 'with velocity - 3 epochs': | |
| score.extend([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256, max(1, min(127, n[5]))+384]) | |
| else: | |
| score.extend([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256]) | |
| pc = c | |
| return score | |
| #================================================================================== | |
| def save_midi(tokens, batch_number=None, model_selector=''): | |
| song = tokens | |
| song_f = [] | |
| time = 0 | |
| dur = 0 | |
| vel = 90 | |
| pitch = 0 | |
| channel = 0 | |
| patch = 0 | |
| patches = [0] * 16 | |
| for m in song: | |
| if 0 <= m < 128: | |
| time += m * 32 | |
| elif 128 < m < 256: | |
| dur = (m-128) * 32 | |
| elif 256 < m < 384: | |
| pitch = (m-256) | |
| if model_selector == 'without velocity - 3 epochs' or model_selector == 'without velocity - 7 epochs': | |
| song_f.append(['note', time, dur, 0, pitch, max(40, pitch), 0]) | |
| elif 384 < m < 512: | |
| vel = (m-384) | |
| if model_selector == 'with velocity - 3 epochs': | |
| song_f.append(['note', time, dur, 0, pitch, vel, 0]) | |
| if batch_number == None: | |
| fname = 'Guided-Accompaniment-Transformer-Music-Composition' | |
| else: | |
| fname = 'Guided-Accompaniment-Transformer-Music-Composition_'+str(batch_number) | |
| data = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, | |
| output_signature = 'Guided Accompaniment Transformer', | |
| output_file_name = fname, | |
| track_name='Project Los Angeles', | |
| list_of_MIDI_patches=patches, | |
| verbose=False | |
| ) | |
| return song_f | |
| #================================================================================== | |
| def Generate_Accompaniment(input_midi, | |
| num_gen_tokens, | |
| model_temperature | |
| ): | |
| #=============================================================================== | |
| 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('Input MIDI type:', input_midi_type) | |
| print('Conversion type:', input_conv_type) | |
| print('Number of prime notes:', input_number_prime_notes) | |
| print('Number of notes to convert:', input_number_conv_notes) | |
| print('Model durations sampling top value:', input_model_dur_top_k) | |
| print('Model durations temperature:', input_model_dur_temperature) | |
| print('Model velocities temperature:', input_model_vel_temperature) | |
| print('=' * 70) | |
| #================================================================== | |
| src_melody_chords_f = load_midi(input_midi.name) | |
| #================================================================== | |
| print('Sample output events', src_melody_chords_f[0][1][:3]) | |
| print('=' * 70) | |
| print('Generating...') | |
| model.to(DEVICE) | |
| model.eval() | |
| #================================================================== | |
| 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 = 60 | |
| channel = 0 | |
| patch = 0 | |
| patches = [0] * 16 | |
| for ss in final_song: | |
| if 0 <= ss < 256: | |
| time += ss * 16 | |
| if 256 <= ss < 384: | |
| pitch = ss-256 | |
| if 384 <= ss < 640: | |
| dur = (ss-384) * 16 | |
| if 640 <= ss < 768: | |
| vel = (ss-640) | |
| song_f.append(['note', time, dur, channel, pitch, vel, patch]) | |
| fn1 = "Score-2-Performance-Transformer-Composition" | |
| detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, | |
| output_signature = 'Score 2 Performance 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:', 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, output_audio, output_plot | |
| #================================================================================== | |
| 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 seed MIDI or click 'Generate' button for random output") | |
| input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) | |
| gr.Markdown("## Generate") | |
| num_gen_tokens = gr.Slider(15, 1024, value=1024, step=1, label="Number of tokens to generate") | |
| 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("## Results") | |
| 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"]) | |
| generate_btn.click(Generate_Accompaniment, | |
| [input_midi, | |
| num_gen_tokens, | |
| 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() | |
| #================================================================================== |