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import os |
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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 copy |
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from itertools import groupby |
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import tqdm |
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import spaces |
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import gradio as gr |
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import torch |
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from x_transformer_1_23_2 import * |
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import random |
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import TMIDIX |
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from midi_to_colab_audio import midi_to_colab_audio |
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@spaces.GPU |
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def Generate_Rock_Song(input_midi, |
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input_gen_type, |
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input_number_prime_chords, |
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input_number_gen_chords, |
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input_use_original_durations, |
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input_match_original_pitches_counts, |
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input_number_prime_tokens, |
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input_number_gen_tokens, |
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input_num_memory_tokens, |
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input_model_temperature, |
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input_model_top_k |
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): |
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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('=' * 70) |
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fn = os.path.basename(input_midi) |
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fn1 = fn.split('.')[0] |
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print('=' * 70) |
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print('Requested settings:') |
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print('=' * 70) |
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print('Input MIDI file name:', fn) |
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print('Generation type:', input_gen_type) |
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print('Number of prime chords:', input_number_prime_chords) |
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print('Number of chords to generate:', input_number_gen_chords) |
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print('Use original durations:', input_use_original_durations) |
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print('Match original pitches counts:', input_match_original_pitches_counts) |
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print('Number of prime tokens:', input_number_prime_tokens) |
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print('Number of tokens to generate:', input_number_gen_tokens) |
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print('Number of memory tokens:', input_num_memory_tokens) |
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print('Model temperature:', input_model_temperature) |
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print('Model sampling top k value:', input_model_top_k) |
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print('=' * 70) |
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print('Loading model...') |
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SEQ_LEN = 4096 |
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PAD_IDX = 673 |
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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 = 16, heads = 16, rotary_pos_emb=True, 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('Guided_Rpck_Music_Transformer_Trained_Model_12081_steps_0.4113_loss_0.8747_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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print('Loading MIDI...') |
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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[6] < 72 or e[6] == 128] |
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escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32, legacy_timings=True) |
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dscore = TMIDIX.enhanced_delta_score_notes(escore_notes) |
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cscore = TMIDIX.chordify_score(dscore) |
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score_toks = [] |
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control_toks = [] |
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prime_toks = [] |
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for c in cscore: |
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ctime = c[0][0] |
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chord = sorted(c, key=lambda x: -x[5]) |
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gnotes = [] |
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gdrums = [] |
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for k, v in groupby(chord, key=lambda x: x[5]): |
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if k == 128: |
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gdrums.extend(sorted(v, key=lambda x: x[3], reverse=True)) |
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else: |
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gnotes.append(sorted(v, key=lambda x: x[3], reverse=True)) |
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chord_toks = [] |
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ctoks = [] |
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ptoks = [] |
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chord_toks.append(ctime) |
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ptoks.append(ctime) |
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if gdrums: |
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chord_toks.extend([e[3]+128 for e in gdrums] + [128]) |
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ptoks.extend([e[3]+128 for e in gdrums] + [128]) |
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else: |
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chord_toks.append(128) |
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ptoks.append(128) |
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if gnotes: |
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for g in gnotes: |
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durs = [e[1] // 4 for e in g] |
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clipped_dur = max(1, min(31, min(durs))) |
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chan = max(0, min(8, g[0][5] // 8)) |
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chan_dur_tok = ((chan * 32) + clipped_dur) + 256 |
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ctoks.append([chan_dur_tok, len(g)]) |
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ptoks.append(chan_dur_tok) |
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ptoks.extend([e[3]+544 for e in g]) |
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score_toks.append(chord_toks) |
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control_toks.append(ctoks) |
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prime_toks.append(ptoks) |
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print('Done!') |
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print('=' * 70) |
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print('Sample output events', prime_toks[:16]) |
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print('=' * 70) |
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print('Generating...') |
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def generate_continuation(num_prime_tokens, num_gen_tokens): |
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x = torch.tensor(TMIDIX.flatten(prime_toks)[:num_prime_tokens], dtype=torch.long, device=DEVICE) |
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with ctx: |
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out = model.generate(x, |
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num_gen_tokens, |
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filter_logits_fn=top_k, |
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filter_kwargs={'k': input_model_top_k}, |
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temperature=input_model_temperature, |
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return_prime=True, |
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verbose=True) |
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y = out.tolist()[0] |
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return y |
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def generate_tokens(seq, max_num_ptcs=5, max_tries=10): |
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input = copy.deepcopy(seq) |
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pcount = 0 |
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y = 545 |
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tries = 0 |
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gen_tokens = [] |
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seen = False |
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if 256 < input[-1] < 544: |
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seen = True |
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while pcount < max_num_ptcs and y > 255 and tries < max_tries: |
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x = torch.tensor(input[-input_num_memory_tokens:], dtype=torch.long, device=DEVICE) |
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with ctx: |
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out = model.generate(x, |
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1, |
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filter_logits_fn=top_k, |
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filter_kwargs={'k': input_model_top_k}, |
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temperature=input_model_temperature, |
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return_prime=False, |
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verbose=False) |
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y = out[0].tolist()[0] |
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if 256 < y < 544: |
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if not seen: |
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input.append(y) |
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gen_tokens.append(y) |
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seen = True |
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else: |
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tries += 1 |
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if y > 544 and seen: |
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if pcount < max_num_ptcs and y not in gen_tokens: |
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input.append(y) |
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gen_tokens.append(y) |
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pcount += 1 |
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else: |
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tries += 1 |
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return gen_tokens |
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song = [] |
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if input_gen_type == 'Freestyle': |
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output = generate_continuation(input_number_prime_tokens, input_number_gen_tokens) |
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song.extend(output) |
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else: |
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for i in range(input_number_prime_chords): |
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song.extend(prime_toks[i]) |
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for i in tqdm.tqdm(range(input_number_prime_chords, input_number_prime_chords+input_number_gen_chords)): |
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song.extend(score_toks[i]) |
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if control_toks[i]: |
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for ct in control_toks[i]: |
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if input_use_original_durations: |
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song.append(ct[0]) |
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if input_match_original_pitches_counts: |
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out_seq = generate_tokens(song, ct[1]) |
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else: |
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out_seq = generate_tokens(song) |
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song.extend(out_seq) |
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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', song[:15]) |
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print('=' * 70) |
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if len(song) != 0: |
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song_f = [] |
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time = 0 |
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dur = 32 |
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channel = 0 |
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pitch = 60 |
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vel = 90 |
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patches = [0, 10, 19, 24, 35, 40, 52, 56, 65, 9, 0, 0, 0, 0, 0, 0] |
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velocities = [80, 100, 90, 100, 110, 100, 100, 100, 100, 110] |
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for ss in song: |
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if 0 <= ss < 128: |
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time += ss * 32 |
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if 128 < ss < 256: |
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song_f.append(['note', time, 32, 9, ss-128, velocities[9], 128]) |
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if 256 < ss < 544: |
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dur = ((ss-256) % 32) * 4 * 32 |
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channel = (ss-256) // 32 |
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if 544 < ss < 672: |
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patch = channel * 8 |
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pitch = ss-544 |
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song_f.append(['note', time, dur, channel, pitch, velocities[channel], patch]) |
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fn1 = "Guided-Rock-Music-Transformer-Composition" |
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detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, |
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output_signature = 'Guided Rock Music 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:', 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'>Guided Rock Music Transformer</h1>") |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique rock music compositions with source augmented RoPE music transformer</h1>") |
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gr.Markdown( |
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"\n\n") |
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gr.Markdown("## Upload your MIDI or select a sample example MIDI below") |
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gr.Markdown("### For best results use MIDIs with 1:2 notes to drums ratio") |
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input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) |
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gr.Markdown("## Select generation type") |
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input_gen_type = gr.Radio(["Controlled", "Freestyle"], value='Controlled', label="Generation type") |
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gr.Markdown("## Controlled generation options") |
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input_number_prime_chords = gr.Slider(0, 512, value=0, step=8, label="Number of prime chords") |
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input_number_gen_chords = gr.Slider(16, 512, value=256, step=8, label="Number of chords to generate") |
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input_use_original_durations = gr.Checkbox(label="Use original durations", value=True) |
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input_match_original_pitches_counts = gr.Checkbox(label="Match original pitches counts", value=True) |
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gr.Markdown("## Freestyle continuation options") |
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input_number_prime_tokens = gr.Slider(0, 1024, value=512, step=16, label="Number of prime tokens") |
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input_number_gen_tokens = gr.Slider(0, 3072, value=1024, step=16, label="Number of tokens to generate") |
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gr.Markdown("## Model options") |
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input_num_memory_tokens = gr.Slider(1024, 4096, value=2048, step=16, label="Number of memory tokens") |
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input_model_temperature = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Model temperature") |
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input_model_top_k = gr.Slider(1, 50, value=10, step=1, label="Model sampling top k value") |
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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(Generate_Rock_Song, [input_midi, |
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input_gen_type, |
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input_number_prime_chords, |
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input_number_gen_chords, |
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input_use_original_durations, |
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input_match_original_pitches_counts, |
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input_number_prime_tokens, |
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input_number_gen_tokens, |
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input_num_memory_tokens, |
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input_model_temperature, |
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input_model_top_k, |
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], |
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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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[["Rock Violin.mid", "Controlled", 0, 512, True, True, 512, 1024, 2048, 0.9, 10], |
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["Come To My Window.mid", "Controlled", 128, 256, False, False, 512, 1024, 2048, 0.9, 10], |
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["Sharing The Night Together.kar", "Controlled", 128, 256, True, True, 512, 1024, 2048, 0.9, 10], |
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["Hotel California.mid", "Controlled", 128, 256, True, True, 512, 1024, 2048, 0.9, 10], |
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["Nothing Else Matters.kar", "Controlled", 128, 256, True, True, 512, 1024, 2048, 0.9, 10], |
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], |
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[input_midi, |
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input_gen_type, |
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input_number_prime_chords, |
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input_number_gen_chords, |
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input_use_original_durations, |
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input_match_original_pitches_counts, |
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input_number_prime_tokens, |
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input_number_gen_tokens, |
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input_num_memory_tokens, |
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input_model_temperature, |
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input_model_top_k, |
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], |
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[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], |
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Generate_Rock_Song, |
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cache_examples=True, |
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) |
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app.queue().launch() |
