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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"
# =================================================================================================
@spaces.GPU
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()