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 GenerateDrums(input_midi, input_num_tokens):
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 = 393 # 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 = 4, heads = 8, 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('Ultimate_Drums_Transformer_Small_Trained_Model_VER3_VEL_11222_steps_0.5749_loss_0.8085_acc.pth',
map_location=DEVICE))
print('=' * 70)
model.eval()
if DEVICE == 'cpu':
dtype = torch.bfloat16
else:
dtype = torch.float16
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_tokens = max(4, min(256, input_num_tokens))
print('-' * 70)
print('Input file name:', fn)
print('Req num toks:', input_num_tokens)
print('-' * 70)
#===============================================================================
# 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]
#=======================================================
# PRE-PROCESSING
#===============================================================================
# Augmented enhanced score notes
escore_notes = [e for e in escore_notes if e[3] != 9]
escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes)
patches = TMIDIX.patch_list_from_enhanced_score_notes(escore_notes)
dscore = TMIDIX.delta_score_notes(escore_notes, compress_timings=True, even_timings=True)
cscore = TMIDIX.chordify_score([d[1:] for d in dscore])
cscore_melody = [c[0] for c in cscore]
comp_times = [0] + [t[1] for t in dscore if t[1] != 0]
#===============================================================================
print('=' * 70)
print('Sample output events', escore_notes[:5])
print('=' * 70)
print('Generating...')
output = []
temperature=0.9
max_drums_limit=8
num_memory_tokens=4096
for c in comp_times[:input_num_tokens]:
output.append(c)
x = torch.tensor([output] * 1, dtype=torch.long, device=DEVICE)
o = 128
ncount = 0
while o > 127 and ncount < max_drums_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 > 127:
x = torch.cat((x, out), 1)
x_output = x.tolist()[0][len(output):]
output.extend(x_output)
print('=' * 70)
print('Done!')
print('=' * 70)
#===============================================================================
print('Rendering results...')
print('=' * 70)
print('Sample INTs', output[:12])
print('=' * 70)
if len(output) != 0:
song = output
song_f = []
time = 0
dtime = 0
ntime = 0
dur = 32
vel = 90
vels = [100, 120]
pitch = 0
channel = 0
idx = 0
for ss in song:
if 0 <= ss < 128:
time += cscore[idx][0][0] * 32
for c in cscore[idx]:
song_f.append(['note', time, c[1] * 32, c[2], c[3], c[4], c[5]])
dtime = time
idx += 1
if 128 <= ss < 256:
dtime += (ss-128) * 32
if 256 <= ss < 384:
pitch = (ss-256)
if 384 <= ss < 393:
vel = ((ss-384)+1) * 15
if dtime == time:
song_f.append(['note', time, dur, 9, pitch, vel, 128])
else:
song_f.append(['note', dtime, dur, 9, pitch, vel, 128])
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
output_signature = 'Ultimate Drums 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:', '')
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')
print([output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
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("Ultimate Drums Transformer
")
gr.Markdown("Generate unique drums track for any MIDI
")
gr.Markdown(
"\n\n"
"SOTA pure drums transformer which is capable of drums track generation for any source composition\n\n"
"Check out [Ultimate Drums Transformer](https://github.com/asigalov61/Ultimate-Drums-Transformer) on GitHub!\n\n"
"[Open In Colab]"
"(https://colab.research.google.com/github/asigalov61/Ultimate-Drums-Transformer/blob/main/Ultimate_Drums_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_tokens = gr.Slider(4, 32, value=16, label="Number of Tokens", info="Number of tokens to generate")
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(GenerateDrums, [input_midi, input_num_tokens],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
gr.Examples(
[["Ultimate-Drums-Transformer-Melody-Seed-1.mid", 128],
["Ultimate-Drums-Transformer-Melody-Seed-2.mid", 128],
["Ultimate-Drums-Transformer-Melody-Seed-3.mid", 128],
["Ultimate-Drums-Transformer-Melody-Seed-4.mid", 128],
["Ultimate-Drums-Transformer-Melody-Seed-5.mid", 128],
["Ultimate-Drums-Transformer-Melody-Seed-6.mid", 128],
["Ultimate-Drums-Transformer-MI-Seed-1.mid", 128],
["Ultimate-Drums-Transformer-MI-Seed-2.mid", 128],
["Ultimate-Drums-Transformer-MI-Seed-3.mid", 128],
["Ultimate-Drums-Transformer-MI-Seed-4.mid", 128]],
[input_midi, input_num_tokens],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
GenerateDrums,
cache_examples=True,
)
app.queue().launch()