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import os.path | |
import copy | |
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 | |
# ================================================================================================= | |
def GenerateAccompaniment(input_midi, input_num_tokens, input_acc_type): | |
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 = 767 # Models pad index | |
DEVICE = 'cuda' # 'cpu' | |
# 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('Ultimate_Accompaniment_Transformer_Small_Improved_Trained_Model_13649_steps_0.3229_loss_0.898_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('Force acc:', input_acc_type) | |
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] | |
escore_notes = [e for e in escore_notes if e[3] != 9] | |
if len(escore_notes) > 0: | |
#======================================================= | |
# PRE-PROCESSING | |
#=============================================================================== | |
# Augmented enhanced score notes | |
escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32) | |
cscore = TMIDIX.chordify_score([1000, escore_notes]) | |
melody = TMIDIX.fix_monophonic_score_durations([sorted(e, key=lambda x: x[4], reverse=True)[0] for e in cscore]) | |
#======================================================= | |
# FINAL PROCESSING | |
melody_chords = [] | |
#======================================================= | |
# MAIN PROCESSING CYCLE | |
#======================================================= | |
pe = cscore[0][0] | |
mpe = melody[0] | |
midx = 1 | |
for i, c in enumerate(cscore): | |
c.sort(key=lambda x: (x[3], x[4]), reverse=True) | |
# Next melody note | |
if midx < len(melody): | |
# Time | |
mtime = melody[midx][1]-mpe[1] | |
mdur = melody[midx][2] | |
mdelta_time = max(0, min(127, mtime)) | |
# Durations | |
mdur = max(0, min(127, mdur)) | |
# Pitch | |
mptc = melody[midx][4] | |
else: | |
mtime = 127-mpe[1] | |
mdur = mpe[2] | |
mdelta_time = max(0, min(127, mtime)) | |
# Durations | |
mdur = max(0, min(127, mdur)) | |
# Pitch | |
mptc = mpe[4] | |
e = melody[i] | |
#======================================================= | |
# Timings... | |
time = e[1]-pe[1] | |
dur = e[2] | |
delta_time = max(0, min(127, time)) | |
# Durations | |
dur = max(0, min(127, dur)) | |
# Pitches | |
ptc = max(1, min(127, e[4])) | |
if ptc < 60: | |
ptc = 60 + (ptc % 12) | |
cha = e[3] | |
#======================================================= | |
# FINAL NOTE SEQ | |
if midx < len(melody): | |
melody_chords.append([delta_time, dur+128, ptc+384, mdelta_time+512, mptc+640]) | |
mpe = melody[midx] | |
midx += 1 | |
else: | |
melody_chords.append([delta_time, dur+128, ptc+384, mdelta_time+512, mptc+640]) | |
pe = e | |
#=============================================================================== | |
print('=' * 70) | |
print('Sample output events', melody_chords[:5]) | |
print('=' * 70) | |
print('Generating...') | |
output = [] | |
force_acc = input_acc_type | |
num_toks_per_note = 32 | |
temperature=0.9 | |
max_drums_limit=4 | |
num_memory_tokens=4096 | |
output1 = [] | |
output2 = [] | |
ctime = 0 | |
for m in melody_chords[:input_num_tokens]: | |
mel = copy.deepcopy(m) | |
mel[0] = mel[0]-ctime | |
output1.extend(mel) | |
input_seq = output1 | |
if force_acc: | |
x = torch.LongTensor([input_seq+[0]]).to(DEVICE) | |
else: | |
x = torch.LongTensor([input_seq]).to(DEVICE) | |
time = input_seq[-2]-512 | |
cur_time = 0 | |
ctime = 0 | |
for _ in range(num_toks_per_note): | |
with ctx: | |
out = model.generate(x[-num_memory_tokens:], | |
1, | |
temperature=temperature, | |
return_prime=False, | |
verbose=False) | |
o = out.tolist()[0][0] | |
if 0 <= o < 128: | |
cur_time += o | |
if cur_time < time and o < 384: | |
ctime = cur_time | |
out = torch.LongTensor([[o]]).to(DEVICE) | |
x = torch.cat((x, out), 1) | |
else: | |
break | |
outy = x.tolist()[0][len(input_seq):] | |
output1.extend(outy) | |
output2.append(outy) | |
print('=' * 70) | |
print('Done!') | |
print('=' * 70) | |
#=============================================================================== | |
print('Rendering results...') | |
print('=' * 70) | |
print('Sample INTs', output1[:12]) | |
print('=' * 70) | |
out1 = output2 | |
accompaniment_MIDI_patch_number = 0 | |
melody_MIDI_patch_number = 40 | |
if len(out1) != 0: | |
song = out1 | |
song_f = [] | |
time = 0 | |
ntime = 0 | |
ndur = 0 | |
vel = 90 | |
npitch = 0 | |
channel = 0 | |
patches = [0] * 16 | |
patches[0] = accompaniment_MIDI_patch_number | |
patches[3] = melody_MIDI_patch_number | |
for i, ss in enumerate(song): | |
ntime += melody_chords[i][0] * 32 | |
ndur = (melody_chords[i][1]-128) * 32 | |
nchannel = 1 | |
npitch = (melody_chords[i][2]-256) % 128 | |
vel = max(40, npitch)+20 | |
song_f.append(['note', ntime, ndur, 3, npitch, vel, melody_MIDI_patch_number ]) | |
time = ntime | |
for s in ss: | |
if 0 <= s < 128: | |
time += s * 32 | |
if 128 <= s < 256: | |
dur = (s-128) * 32 | |
if 256 <= s < 384: | |
pitch = (s-256) | |
vel = max(40, pitch) | |
song_f.append(['note', time, dur, 0, pitch, vel, accompaniment_MIDI_patch_number]) | |
fn1 = "Ultimate-Accompaniment-Transformer-Composition" | |
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, | |
output_signature = 'Ultimate Accompaniment 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("<h1 style='text-align: center; margin-bottom: 1rem'>Ultimate Accompaniment Transformer</h1>") | |
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique accompaniment for any melody</h1>") | |
gr.Markdown( | |
"![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Ultimate-Accompaniment-Transformer&style=flat)\n\n" | |
"Accompaniment generation for any monophonic melody\n\n" | |
"Check out [Ultimate Drums Transformer](https://github.com/asigalov61/Ultimate-Accompaniment-Transformer) on GitHub!\n\n" | |
"[Open In Colab]" | |
"(https://colab.research.google.com/github/asigalov61/Ultimate-Accompaniment-Transformer/blob/main/Ultimate_Accompaniment_Transformer.ipynb)" | |
" for faster execution and endless generation" | |
) | |
gr.Markdown("## Upload your MIDI or select a sample example MIDI") | |
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 accompaniment for") | |
input_acc_type = gr.Checkbox(label='Force accompaniment generation for each melody note') | |
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_acc_type], | |
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) | |
gr.Examples( | |
[["Ultimate-Accompaniment-Transformer-Melody-Seed-1.mid", 128, True], | |
["Ultimate-Accompaniment-Transformer-Melody-Seed-2.mid", 128, False], | |
["Ultimate-Accompaniment-Transformer-Melody-Seed-3.mid", 128, True], | |
["Ultimate-Accompaniment-Transformer-Melody-Seed-4.mid", 128, False], | |
["Ultimate-Accompaniment-Transformer-Melody-Seed-5.mid", 128, True], | |
["Ultimate-Accompaniment-Transformer-Melody-Seed-6.mid", 128, False], | |
["Ultimate-Accompaniment-Transformer-Melody-Seed-7.mid", 128, True]], | |
[input_midi, input_num_tokens, input_acc_type], | |
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], | |
GenerateAccompaniment, | |
cache_examples=True, | |
) | |
app.queue().launch() |