#==================================================================================
# https://huggingface.co/spaces/asigalov61/Guided-Accompaniment-Transformer
#==================================================================================
print('=' * 70)
print('Guided Accompaniment Transformer Gradio App')
print('=' * 70)
print('Loading core Guided Accompaniment Transformer modules...')
import os
import copy
import time as reqtime
import datetime
from pytz import timezone
print('=' * 70)
print('Loading main Guided Accompaniment Transformer modules...')
os.environ['USE_FLASH_ATTENTION'] = '1'
import torch
torch.set_float32_matmul_precision('high')
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
torch.backends.cuda.enable_mem_efficient_sdp(True)
torch.backends.cuda.enable_math_sdp(True)
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_cudnn_sdp(True)
from huggingface_hub import hf_hub_download
import TMIDIX
from midi_to_colab_audio import midi_to_colab_audio
from x_transformer_1_23_2 import *
import random
import tqdm
print('=' * 70)
print('Loading aux Guided Accompaniment Transformer modules...')
import matplotlib.pyplot as plt
import gradio as gr
import spaces
print('=' * 70)
print('PyTorch version:', torch.__version__)
print('=' * 70)
print('Done!')
print('Enjoy! :)')
print('=' * 70)
#==================================================================================
MODEL_CHECKPOINT = 'Guided_Accompaniment_Transformer_Trained_Model_36457_steps_0.5384_loss_0.8417_acc.pth'
SOUDFONT_PATH = 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2'
#==================================================================================
print('=' * 70)
print('Instantiating model...')
device_type = 'cuda'
dtype = 'bfloat16'
ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)
SEQ_LEN = 4096
PAD_IDX = 1794
model = TransformerWrapper(
num_tokens = PAD_IDX+1,
max_seq_len = SEQ_LEN,
attn_layers = Decoder(dim = 2048,
depth = 4,
heads = 32,
rotary_pos_emb = True,
attn_flash = True
)
)
model = AutoregressiveWrapper(model, ignore_index=PAD_IDX, pad_value=PAD_IDX)
print('=' * 70)
print('Loading model checkpoint...')
model_checkpoint = hf_hub_download(repo_id='asigalov61/Guided-Accompaniment-Transformer', filename=MODEL_CHECKPOINT)
model.load_state_dict(torch.load(model_checkpoint, map_location='cpu', weights_only=True))
model = torch.compile(model, mode='max-autotune')
print('=' * 70)
print('Done!')
print('=' * 70)
print('Model will use', dtype, 'precision...')
print('=' * 70)
#==================================================================================
def load_midi(input_midi, melody_patch=-1):
raw_score = TMIDIX.midi2single_track_ms_score(input_midi)
escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32)
sp_escore_notes = TMIDIX.solo_piano_escore_notes(escore_notes, keep_drums=False)
if melody_patch == -1:
zscore = TMIDIX.recalculate_score_timings(sp_escore_notes)
else:
mel_score = [e for e in sp_escore_notes if e[6] == melody_patch]
if mel_score:
zscore = TMIDIX.recalculate_score_timings(mel_score)
else:
zscore = TMIDIX.recalculate_score_timings(sp_escore_notes)
cscore = TMIDIX.chordify_score([1000, zscore])
score = []
score_list = []
pc = cscore[0]
for c in cscore:
score.append(max(0, min(127, c[0][1]-pc[0][1])))
scl = [[max(0, min(127, c[0][1]-pc[0][1]))]]
n = c[0]
score.extend([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256])
scl.append([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256])
score_list.append(scl)
pc = c
score_list.append(scl)
return score, score_list
#==================================================================================
@spaces.GPU
def Generate_Accompaniment(input_midi,
generation_type,
melody_patch,
model_temperature
):
#===============================================================================
def generate_full_seq(input_seq, temperature=0.9, verbose=True):
seq_abs_run_time = sum([t for t in input_seq if t < 128])
cur_time = 0
full_seq = copy.deepcopy(input_seq)
toks_counter = 0
while cur_time <= seq_abs_run_time:
if verbose:
if toks_counter % 128 == 0:
print('Generated', toks_counter, 'tokens')
x = torch.LongTensor(full_seq).cuda()
with ctx:
out = model.generate(x,
1,
temperature=temperature,
return_prime=False,
verbose=False)
y = out.tolist()[0][0]
if y < 128:
cur_time += y
full_seq.append(y)
toks_counter += 1
return full_seq
#===============================================================================
def generate_block_seq(input_seq, trg_dtime, temperature=0.9):
inp_seq = copy.deepcopy(input_seq)
block_seq = []
cur_time = 0
while cur_time < trg_dtime:
x = torch.LongTensor(inp_seq).cuda()
with ctx:
out = model.generate(x,
1,
temperature=temperature,
return_prime=False,
verbose=False)
y = out.tolist()[0][0]
if y < 128:
cur_time += y
inp_seq.append(y)
block_seq.append(y)
if cur_time != trg_dtime:
return []
else:
return block_seq
#===============================================================================
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)
fn1 = fn.split('.')[0]
print('=' * 70)
print('Requested settings:')
print('=' * 70)
print('Input MIDI file name:', fn)
print('Generation type:', generation_type)
print('Source melody patch:', melody_patch)
print('Model temperature:', model_temperature)
print('=' * 70)
#==================================================================
score, score_list = load_midi(input_midi.name)
print('Sample score events', score[:12])
#==================================================================
print('=' * 70)
print('Generating...')
model.to(device_type)
model.eval()
#==================================================================
start_score_seq = [1792] + score + [1793]
#==================================================================
if generation_type == 'Guided':
input_seq = []
input_seq.extend(start_score_seq)
input_seq.extend(score_list[0][0])
block_seq_lens = []
idx = 0
max_retries = 3
mrt = 0
while idx < len(score_list)-1:
if idx % 10 == 0:
print('Generating', idx, 'block')
input_seq.extend(score_list[idx][1])
block_seq = []
for _ in range(max_retries):
block_seq = generate_block_seq(input_seq, score_list[idx+1][0][0])
if block_seq:
break
if block_seq:
input_seq.extend(block_seq)
block_seq_lens.append(len(block_seq))
idx += 1
mrt = 0
else:
if block_seq_lens:
input_seq = input_seq[:-(block_seq_lens[-1]+2)]
block_seq_lens.pop()
idx -= 1
mrt += 1
else:
break
if mrt == max_retries:
break
else:
input_seq = generate_full_seq(start_score_seq, temperature=model_temperature)
final_song = input_seq[len(start_score_seq):]
print('=' * 70)
print('Done!')
print('=' * 70)
#===============================================================================
print('Rendering results...')
print('=' * 70)
print('Sample INTs', final_song[:15])
print('=' * 70)
song_f = []
if len(final_song) != 0:
time = 0
dur = 0
vel = 90
pitch = 0
channel = 0
patch = 0
channels_map = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 9, 12, 13, 14, 15]
patches_map = [40, 0, 10, 19, 24, 35, 40, 52, 56, 9, 65, 73, 0, 0, 0, 0]
velocities_map = [125, 80, 100, 80, 90, 100, 100, 80, 110, 110, 110, 110, 80, 80, 80, 80]
for m in final_song:
if 0 <= m < 128:
time += m * 32
elif 128 < m < 256:
dur = (m-128) * 32
elif 256 < m < 1792:
cha = (m-256) // 128
pitch = (m-256) % 128
channel = channels_map[cha]
patch = patches_map[channel]
vel = velocities_map[channel]
song_f.append(['note', time, dur, channel, pitch, vel, patch])
fn1 = "Guided-Accompaniment-Transformer-Composition"
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
output_signature = 'Guided Accompaniment Transformer',
output_file_name = fn1,
track_name='Project Los Angeles',
list_of_MIDI_patches=patches_map
)
new_fn = fn1+'.mid'
audio = midi_to_colab_audio(new_fn,
soundfont_path=SOUDFONT_PATH,
sample_rate=16000,
volume_scale=10,
output_for_gradio=True
)
print('Done!')
print('=' * 70)
#========================================================
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('=' * 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_audio, output_plot, output_midi
#==================================================================================
PDT = timezone('US/Pacific')
print('=' * 70)
print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
print('=' * 70)
#==================================================================================
with gr.Blocks() as demo:
#==================================================================================
gr.Markdown("Guided Accompaniment Transformer
")
gr.Markdown("Guided melody accompaniment generation with transformers
")
gr.HTML("""
for faster execution and endless generation!
""")
#==================================================================================
gr.Markdown("## Upload source melody MIDI or select an example MIDI below")
input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
gr.Markdown("## Generation options")
generation_type = gr.Radio(["Guided", "Freestyle"], value="Guided", label="Generation type")
melody_patch = gr.Slider(-1, 127, value=-1, step=1, label="Source melody MIDI patch")
model_temperature = gr.Slider(0.1, 1, value=0.9, step=0.01, label="Model temperature")
generate_btn = gr.Button("Generate", variant="primary")
gr.Markdown("## Generation results")
output_audio = gr.Audio(label="MIDI audio", format="wav", elem_id="midi_audio")
output_plot = gr.Plot(label="MIDI score plot")
output_midi = gr.File(label="MIDI file", file_types=[".mid"])
generate_btn.click(Generate_Accompaniment,
[input_midi,
generation_type,
melody_patch,
model_temperature
],
[output_audio,
output_plot,
output_midi
]
)
gr.Examples(
[["USSR-National-Anthem-Seed-Melody.mid", "Guided", -1, 0.9],
["Hotel-California-Seed-Melody.mid", "Guided", -1, 0.9],
["Sparks-Fly-Seed-Melody.mid", "Guided", -1, 0.9]
],
[input_midi,
generation_type,
melody_patch,
model_temperature
],
[output_audio,
output_plot,
output_midi
],
Generate_Accompaniment
)
#==================================================================================
demo.launch()
#==================================================================================