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# https://huggingface.co/spaces/asigalov61/Inpaint-Music-Transformer | |
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 | |
# ================================================================================================= | |
def InpaintPitches(input_midi, input_num_of_notes, input_patch_number): | |
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 = 19463 # 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 = 1024, depth = 32, heads = 32, 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('Giant_Music_Transformer_Large_Trained_Model_36074_steps_0.3067_loss_0.927_acc.pth', | |
map_location=DEVICE)) | |
print('=' * 70) | |
model.eval() | |
if DEVICE == 'cpu': | |
dtype = torch.bfloat16 | |
else: | |
dtype = torch.bfloat16 | |
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_of_notes = max(8, min(2048, input_num_of_notes)) | |
print('-' * 70) | |
print('Input file name:', fn) | |
print('Req num of notes:', input_num_of_notes) | |
print('Req patch number:', input_patch_number) | |
print('-' * 70) | |
#=============================================================================== | |
raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name) | |
#=============================================================================== | |
# Enhanced score notes | |
events_matrix1 = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0] | |
#======================================================= | |
# PRE-PROCESSING | |
# checking number of instruments in a composition | |
instruments_list_without_drums = list(set([y[3] for y in events_matrix1 if y[3] != 9])) | |
instruments_list = list(set([y[3] for y in events_matrix1])) | |
if len(events_matrix1) > 0 and len(instruments_list_without_drums) > 0: | |
#====================================== | |
events_matrix2 = [] | |
# Recalculating timings | |
for e in events_matrix1: | |
# Original timings | |
e[1] = int(e[1] / 16) | |
e[2] = int(e[2] / 16) | |
#=================================== | |
# ORIGINAL COMPOSITION | |
#=================================== | |
# Sorting by patch, pitch, then by start-time | |
events_matrix1.sort(key=lambda x: x[6]) | |
events_matrix1.sort(key=lambda x: x[4], reverse=True) | |
events_matrix1.sort(key=lambda x: x[1]) | |
#======================================================= | |
# FINAL PROCESSING | |
melody_chords = [] | |
melody_chords2 = [] | |
# Break between compositions / Intro seq | |
if 9 in instruments_list: | |
drums_present = 19331 # Yes | |
else: | |
drums_present = 19330 # No | |
if events_matrix1[0][3] != 9: | |
pat = events_matrix1[0][6] | |
else: | |
pat = 128 | |
melody_chords.extend([19461, drums_present, 19332+pat]) # Intro seq | |
#======================================================= | |
# MAIN PROCESSING CYCLE | |
#======================================================= | |
abs_time = 0 | |
pbar_time = 0 | |
pe = events_matrix1[0] | |
chords_counter = 1 | |
comp_chords_len = len(list(set([y[1] for y in events_matrix1]))) | |
for e in events_matrix1: | |
#======================================================= | |
# Timings... | |
# Cliping all values... | |
delta_time = max(0, min(255, e[1]-pe[1])) | |
# Durations and channels | |
dur = max(0, min(255, e[2])) | |
cha = max(0, min(15, e[3])) | |
# Patches | |
if cha == 9: # Drums patch will be == 128 | |
pat = 128 | |
else: | |
pat = e[6] | |
# Pitches | |
ptc = max(1, min(127, e[4])) | |
# Velocities | |
# Calculating octo-velocity | |
vel = max(8, min(127, e[5])) | |
velocity = round(vel / 15)-1 | |
#======================================================= | |
# FINAL NOTE SEQ | |
# Writing final note asynchronously | |
dur_vel = (8 * dur) + velocity | |
pat_ptc = (129 * pat) + ptc | |
melody_chords.extend([delta_time, dur_vel+256, pat_ptc+2304]) | |
melody_chords2.append([delta_time, dur_vel+256, pat_ptc+2304]) | |
pe = e | |
#================================================================== | |
print('=' * 70) | |
print('Number of tokens:', len(melody_chords)) | |
print('Number of notes:', len(melody_chords2)) | |
print('Sample output events', melody_chords[:5]) | |
print('=' * 70) | |
print('Generating...') | |
#@title Pitches/Instruments Inpainting | |
#@markdown You can stop the inpainting at any time to render partial results | |
#@markdown Inpainting settings | |
#@markdown Select MIDI patch present in the composition to inpaint | |
inpaint_MIDI_patch = input_patch_number | |
#@markdown Generation settings | |
number_of_prime_notes = 24 | |
number_of_memory_tokens = 1024 # @param {type:"slider", min:3, max:8190, step:3} | |
number_of_samples_per_inpainted_note = 1 #@param {type:"slider", min:1, max:16, step:1} | |
temperature = 0.85 | |
print('=' * 70) | |
print('Giant Music Transformer Inpainting Model Generator') | |
print('=' * 70) | |
#========================================================================== | |
nidx = 0 | |
first_inote = True | |
fidx = 0 | |
number_of_prime_tokens = number_of_prime_notes * 3 | |
for i, m in enumerate(melody_chords): | |
if 2304 <= melody_chords[i] < 18945: | |
cpatch = (melody_chords[i]-2304) // 129 | |
if cpatch == inpaint_MIDI_patch: | |
nidx += 1 | |
if first_inote: | |
fidx += 1 | |
if first_inote and fidx == number_of_prime_notes: | |
number_of_prime_tokens = i | |
first_inote = False | |
if nidx == input_num_of_notes: | |
break | |
nidx = i | |
#========================================================================== | |
out2 = [] | |
for m in melody_chords[:number_of_prime_tokens]: | |
out2.append(m) | |
for i in range(number_of_prime_tokens, len(melody_chords[:nidx])): | |
cpatch = (melody_chords[i]-2304) // 129 | |
if 2304 <= melody_chords[i] < 18945 and (cpatch) == inpaint_MIDI_patch: | |
samples = [] | |
for j in range(number_of_samples_per_inpainted_note): | |
inp = torch.LongTensor(out2[-number_of_memory_tokens:]).cuda() | |
with ctx: | |
out1 = model.generate(inp, | |
1, | |
temperature=temperature, | |
return_prime=True, | |
verbose=False) | |
with torch.no_grad(): | |
test_loss, test_acc = model(out1) | |
samples.append([out1.tolist()[0][-1], test_acc.tolist()]) | |
accs = [y[1] for y in samples] | |
max_acc = max(accs) | |
max_acc_sample = samples[accs.index(max_acc)][0] | |
cpitch = (max_acc_sample-2304) % 129 | |
out2.extend([((cpatch * 129) + cpitch)+2304]) | |
else: | |
out2.append(melody_chords[i]) | |
print('=' * 70) | |
print('Done!') | |
print('=' * 70) | |
#=============================================================================== | |
print('Rendering results...') | |
print('=' * 70) | |
print('Sample INTs', out2[:12]) | |
print('=' * 70) | |
if len(out2) != 0: | |
song = out2 | |
song_f = [] | |
time = 0 | |
dur = 0 | |
vel = 90 | |
pitch = 0 | |
channel = 0 | |
patches = [-1] * 16 | |
channels = [0] * 16 | |
channels[9] = 1 | |
for ss in song: | |
if 0 <= ss < 256: | |
time += ss * 16 | |
if 256 <= ss < 2304: | |
dur = ((ss-256) // 8) * 16 | |
vel = (((ss-256) % 8)+1) * 15 | |
if 2304 <= ss < 18945: | |
patch = (ss-2304) // 129 | |
if patch < 128: | |
if patch not in patches: | |
if 0 in channels: | |
cha = channels.index(0) | |
channels[cha] = 1 | |
else: | |
cha = 15 | |
patches[cha] = patch | |
channel = patches.index(patch) | |
else: | |
channel = patches.index(patch) | |
if patch == 128: | |
channel = 9 | |
pitch = (ss-2304) % 129 | |
song_f.append(['note', time, dur, channel, pitch, vel, patch ]) | |
patches = [0 if x==-1 else x for x in patches] | |
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, | |
output_signature = 'Giant Music 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_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'>Inpaint Music Transformer</h1>") | |
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Inpaint pitches in any MIDI</h1>") | |
gr.Markdown( | |
"![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Inpaint-Music-Transformer&style=flat)\n\n" | |
"This is a demo of the Giant Music Transformer pitches inpainting feature\n\n" | |
"Check out [Giant Music Transformer](https://github.com/asigalov61/Giant-Music-Transformer) on GitHub!\n\n" | |
"[Open In Colab]" | |
"(https://colab.research.google.com/github/asigalov61/Giant-Music-Transformer/blob/main/Giant_Music_Transformer.ipynb)" | |
" for all features, faster execution and endless generation" | |
) | |
gr.Markdown("## Upload your MIDI or select a sample example MIDI below") | |
input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) | |
input_num_of_notes = gr.Slider(8, 2048, value=128, step=8, label="Number of composition notes to inpaint") | |
input_patch_number = gr.Slider(0, 127, value=0, step=1, label="Composition MIDI patch to inpaint") | |
run_btn = gr.Button("inpaint", variant="primary") | |
gr.Markdown("## Inpainting 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(InpaintPitches, [input_midi, input_num_of_notes, input_patch_number], | |
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) | |
gr.Examples( | |
[["Giant-Music-Transformer-Piano-Seed-1.mid", 128, 0], | |
["Giant-Music-Transformer-Piano-Seed-2.mid", 128, 0], | |
["Giant-Music-Transformer-Piano-Seed-3.mid", 128, 0], | |
["Giant-Music-Transformer-Piano-Seed-4.mid", 128, 0], | |
["Giant-Music-Transformer-Piano-Seed-5.mid", 128, 2], | |
["Giant-Music-Transformer-Piano-Seed-6.mid", 128, 0], | |
["Giant-Music-Transformer-MI-Seed-1.mid", 128, 71], | |
["Giant-Music-Transformer-MI-Seed-2.mid", 128, 40], | |
["Giant-Music-Transformer-MI-Seed-3.mid", 128, 40], | |
["Giant-Music-Transformer-MI-Seed-4.mid", 128, 40], | |
["Giant-Music-Transformer-MI-Seed-5.mid", 128, 40], | |
["Giant-Music-Transformer-MI-Seed-6.mid", 128, 0] | |
], | |
[input_midi, input_num_of_notes, input_patch_number], | |
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], | |
InpaintPitches, | |
cache_examples=True, | |
) | |
app.queue().launch() |