import os.path
import time as reqtime
import datetime
from pytz import timezone
import torch
import spaces
import gradio as gr
import random
import tqdm
from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX
import matplotlib.pyplot as plt
from inference import PianoTranscription
from config import sample_rate
from utilities import load_audio
in_space = os.getenv("SYSTEM") == "spaces"
# =================================================================================================
@spaces.GPU
def TranscribePianoAudio(input_audio):
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_tokens = 90 # @param {type:"slider", min:3, max:8190, step:3}
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
for i, m in enumerate(melody_chords):
cpatch = (melody_chords[i]-2304) // 129
if 2304 <= melody_chords[i] < 18945 and (cpatch) == inpaint_MIDI_patch:
nidx += 1
if nidx == input_num_of_notes+(number_of_prime_tokens // 3):
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("Inpaint Music Transformer
")
gr.Markdown("Inpaint pitches in any MIDI
")
gr.Markdown(
"\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")
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()