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# https://huggingface.co/spaces/asigalov61/Bridge-Music-Transformer | |
import os | |
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 GenerateBridge(input_midi, input_start_note): | |
print('=' * 70) | |
print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT))) | |
start_time = reqtime.time() | |
print('=' * 70) | |
fn = os.path.basename(input_midi.name) | |
fn1 = fn.split('.')[0] | |
print('-' * 70) | |
print('Input file name:', fn) | |
print('Start note', input_start_note) | |
print('-' * 70) | |
print('Loading model...') | |
SEQ_LEN = 3074 | |
PAD_IDX = 653 | |
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 = 16, attn_flash = True) | |
) | |
model = AutoregressiveWrapper(model, ignore_index = PAD_IDX, pad_value=PAD_IDX) | |
model.to(DEVICE) | |
print('=' * 70) | |
print('Loading model checkpoint...') | |
model.load_state_dict( | |
torch.load('Bridge_Music_Transformer_Trained_Model_30023_steps_0.482_loss_0.8523_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) | |
print('Loading MIDI...') | |
#=============================================================================== | |
# Raw single-track ms score | |
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] | |
#=============================================================================== | |
# Augmented enhanced score notes | |
escore_notes = TMIDIX.recalculate_score_timings(TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32)) | |
#======================================================= | |
# FINAL PROCESSING | |
melody_chords = [] | |
#======================================================= | |
# MAIN PROCESSING CYCLE | |
#======================================================= | |
pe = escore_notes[0] | |
for e in escore_notes: | |
#======================================================= | |
# Timings... | |
delta_time = max(0, min(127, e[1]-pe[1])) | |
# Durations and channels | |
dur = max(0, min(127, e[2])) | |
cha = max(0, min(15, e[3])) | |
# Patches | |
pat = max(0, min(128, e[6])) | |
# Pitches | |
if cha != 9: | |
ptc = max(1, min(127, e[4])) | |
else: | |
ptc = max(1, min(127, e[4]))+128 | |
# Velocities | |
# Calculating octo-velocity | |
velocity = max(8, min(127, e[5])) | |
vel = round(velocity / 15)-1 | |
#======================================================= | |
# FINAL NOTE SEQ | |
# Writing final note synchronously | |
melody_chords.extend([delta_time, dur+128, pat+256, ptc+384, vel+640]) | |
pe = e | |
#======================================================= | |
melody_chords = melody_chords[input_start_note*5:] | |
SEQ_L = 3060 | |
STEP = SEQ_L // 3 | |
score_chunk = melody_chords[:SEQ_L] | |
td = [649] | |
td.extend(score_chunk[:STEP]) | |
td += [650] | |
td.extend(score_chunk[-STEP:]) | |
td += [651] | |
start_note = score_chunk[:STEP][-5:] | |
end_note = score_chunk[-STEP:][:5] | |
print('Done!') | |
print('=' * 70) | |
print('Start note', start_note) | |
print('Etart note', end_note) | |
print('=' * 70) | |
print('Generating...') | |
x = (torch.tensor(td, dtype=torch.long, device=DEVICE)[None, ...]) | |
with ctx: | |
out = model.generate(x, | |
1032, | |
temperature=0.9, | |
return_prime=False, | |
verbose=False) | |
y = out.tolist() | |
output = [] | |
for i in range(0, len(y[0]), 5): | |
if len(y[0][i:i+5]) == 5: | |
output.append(y[0][i:i+5]) | |
print('=' * 70) | |
print('Done!') | |
print('=' * 70) | |
start_note_idx = output.index(start_note) | |
end_note_idx = len(output)-output[::-1].index(end_note)-1 | |
print('Start note check:', start_note in output, '---', start_note_idx) | |
print('End note check:',end_note in output, '---', end_note_idx) | |
#=============================================================================== | |
print('Rendering results...') | |
data = score_chunk[:STEP] + TMIDIX.flatten(output[:end_note_idx]) + score_chunk[-STEP:] | |
print('=' * 70) | |
print('Sample INTs', data[:15]) | |
print('=' * 70) | |
if len(data) != 0: | |
song = data | |
song_f = [] | |
time = 0 | |
dur = 0 | |
vel = 90 | |
pitch = 0 | |
pat = 0 | |
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: | |
pat = (ss-256) | |
channel = pat // 8 | |
if channel == 9: | |
channel = 15 | |
if channel == 16: | |
channel = 9 | |
if 384 < ss < 640: | |
pitch = (ss-384) % 128 | |
if 640 <= ss < 648: | |
vel = ((ss-640)+1) * 15 | |
song_f.append(['note', time, dur, channel, pitch, vel, pat]) | |
song_f, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f) | |
fn1 = "Bridge-Music-Transformer-Composition" | |
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, | |
output_signature = 'Bridge 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'>Bridge Music Transformer</h1>") | |
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate a seamless bridge between two parts of any composition</h1>") | |
gr.Markdown( | |
"![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Bridge-Music-Transformer&style=flat)\n\n") | |
gr.Markdown("## Upload your MIDI or select a sample example MIDI below") | |
gr.Markdown("### Please note that the MIDI must have at least 615 notes for this demo to work properly") | |
input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"]) | |
input_start_note = gr.Slider(0, 205, value=0, step=1, label="Start note number") | |
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(GenerateBridge, [input_midi, input_start_note], | |
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot]) | |
gr.Examples( | |
[["Sharing The Night Together.kar", 0], | |
["Sharing The Night Together.kar", 100], | |
["Deep Relaxation Melody #6.mid", 0], | |
["Deep Relaxation Melody #6.mid", 100] | |
], | |
[input_midi, input_start_note], | |
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot], | |
GenerateBridge, | |
cache_examples=True, | |
) | |
app.queue().launch() |