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" # ================================================================================================= @spaces.GPU def GenerateAccompaniment(input_midi, input_num_tokens, input_conditioning_type, input_strip_notes): 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 = 707 # 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 = 2048, 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('Chords_Progressions_Transformer_Small_2048_Trained_Model_12947_steps_0.9316_loss_0.7386_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) fn = os.path.basename(input_midi.name) fn1 = fn.split('.')[0] input_num_tokens = max(4, min(128, input_num_tokens)) print('-' * 70) print('Input file name:', fn) print('Req num toks:', input_num_tokens) print('Conditioning type:', input_conditioning_type) print('Strip notes:', input_strip_notes) print('-' * 70) #=============================================================================== 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] no_drums_escore_notes = [e for e in escore_notes if e[6] < 80] if len(no_drums_escore_notes) > 0: #======================================================= # PRE-PROCESSING #=============================================================================== # Augmented enhanced score notes no_drums_escore_notes = TMIDIX.augment_enhanced_score_notes(no_drums_escore_notes) cscore = TMIDIX.chordify_score([1000, no_drums_escore_notes]) clean_cscore = [] for c in cscore: pitches = [] cho = [] for cc in c: if cc[4] not in pitches: cho.append(cc) pitches.append(cc[4]) clean_cscore.append(cho) #======================================================= # FINAL PROCESSING melody_chords = [] chords = [] times = [0] durs = [] #======================================================= # MAIN PROCESSING CYCLE #======================================================= pe = clean_cscore[0][0] first_chord = True for c in clean_cscore: # Chords c.sort(key=lambda x: x[4], reverse=True) tones_chord = sorted(set([cc[4] % 12 for cc in c])) try: chord_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord) except: checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord) chord_token = TMIDIX.ALL_CHORDS_SORTED.index(checked_tones_chord) melody_chords.extend([chord_token+384]) if input_strip_notes: if len(tones_chord) > 1: chords.extend([chord_token+384]) else: chords.extend([chord_token+384]) if first_chord: melody_chords.extend([0]) first_chord = False for e in c: #======================================================= # Timings... time = e[1]-pe[1] dur = e[2] if time != 0 and time % 2 != 0: time += 1 if dur % 2 != 0: dur += 1 delta_time = int(max(0, min(255, time)) / 2) # Durations dur = int(max(0, min(255, dur)) / 2) # Pitches ptc = max(1, min(127, e[4])) #======================================================= # FINAL NOTE SEQ # Writing final note asynchronously if delta_time != 0: melody_chords.extend([delta_time, dur+128, ptc+256]) if input_strip_notes: if len(c) > 1: times.append(delta_time) durs.append(dur+128) else: times.append(delta_time) durs.append(dur+128) else: melody_chords.extend([dur+128, ptc+256]) pe = e #================================================================== print('=' * 70) print('Sample output events', melody_chords[:5]) print('=' * 70) print('Generating...') output = [] max_chords_limit = 8 temperature=0.9 num_memory_tokens=4096 output = [] idx = 0 for c in chords[:input_num_tokens]: output.append(c) if input_conditioning_type == 'Chords-Times' or input_conditioning_type == 'Chords-Times-Durations': output.append(times[idx]) if input_conditioning_type == 'Chords-Times-Durations': output.append(durs[idx]) x = torch.tensor([output] * 1, dtype=torch.long, device='cuda') o = 0 ncount = 0 while o < 384 and ncount < max_chords_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 < 384: x = torch.cat((x, out), 1) outy = x.tolist()[0][len(output):] output.extend(outy) idx += 1 if idx == len(chords[:input_num_tokens])-1: break print('=' * 70) print('Done!') print('=' * 70) #=============================================================================== print('Rendering results...') print('=' * 70) print('Sample INTs', output[:12]) print('=' * 70) out1 = output if len(out1) != 0: song = out1 song_f = [] time = 0 dur = 0 vel = 90 pitch = 0 channel = 0 patches = [0] * 16 channel = 0 for ss in song: if 0 <= ss < 128: time += ss * 32 if 128 <= ss < 256: dur = (ss-128) * 32 if 256 <= ss < 384: pitch = (ss-256) vel = max(40, pitch) song_f.append(['note', time, dur, channel, pitch, vel, 0]) fn1 = "Chords-Progressions-Transformer-Composition" detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, output_signature = 'Chords Progressions 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("

Melody2Song Seq2Seq Music Transformer

") gr.Markdown("

Generate unique songs from melodies with se2seq music transformer

") gr.Markdown( "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Melody2Song-Seq2Seq-Music-Transformer&style=flat)\n\n") input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) input_num_tokens = gr.Slider(4, 128, value=32, step=1, label="Number of composition chords to generate progression for") input_conditioning_type = gr.Radio(["Chords", "Chords-Times", "Chords-Times-Durations"], label="Conditioning type") input_strip_notes = gr.Checkbox(label="Strip notes from the composition") 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(GenerateAccompaniment, [input_midi, input_num_tokens, input_conditioning_type, input_strip_notes], [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) app.queue().launch()