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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" | |
# ================================================================================================= | |
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...') | |
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=DEVICE) | |
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:', 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'>Chords Progressions Transformer</h1>") | |
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Chords-conditioned music transformer</h1>") | |
gr.Markdown( | |
"![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Chords-Progressions-Transformer&style=flat)\n\n" | |
"Generate music based on chords progressions\n\n" | |
"Check out [Chords Progressions Transformer](https://github.com/asigalov61/Chords-Progressions-Transformer) on GitHub!\n\n" | |
"[Open In Colab]" | |
"(https://colab.research.google.com/github/asigalov61/Chords-Progressions-Transformer/blob/main/Chords_Progressions_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_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("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(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, 0], | |
["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() |