#=======================================================================
# https://huggingface.co/spaces/asigalov61/Guided-Rock-Music-Transformer
#=======================================================================
import os
import time as reqtime
import datetime
from pytz import timezone
import copy
from itertools import groupby
import tqdm
import spaces
import gradio as gr
import torch
from x_transformer_1_23_2 import *
import random
import TMIDIX
from midi_to_colab_audio import midi_to_colab_audio
# =================================================================================================
@spaces.GPU
def Generate_Rock_Song(input_midi,
input_gen_type,
input_number_prime_chords,
input_number_gen_chords,
input_use_original_durations,
input_match_original_pitches_counts,
input_number_prime_tokens,
input_number_gen_tokens,
input_num_memory_tokens,
input_model_temperature,
input_model_top_k
):
#===============================================================================
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:', input_gen_type)
print('Number of prime chords:', input_number_prime_chords)
print('Number of chords to generate:', input_number_gen_chords)
print('Use original durations:', input_use_original_durations)
print('Match original pitches counts:', input_match_original_pitches_counts)
print('Number of prime tokens:', input_number_prime_tokens)
print('Number of tokens to generate:', input_number_gen_tokens)
print('Number of memory tokens:', input_num_memory_tokens)
print('Model temperature:', input_model_temperature)
print('Model sampling top k value:', input_model_top_k)
print('=' * 70)
#===============================================================================
print('Loading model...')
SEQ_LEN = 4096
PAD_IDX = 673
DEVICE = 'cuda' # 'cpu'
# instantiate the model
model = TransformerWrapper(
num_tokens = PAD_IDX+1,
max_seq_len = SEQ_LEN,
attn_layers = Decoder(dim = 1024, depth = 16, heads = 16, rotary_pos_emb=True, 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('Guided_Rpck_Music_Transformer_Trained_Model_12081_steps_0.4113_loss_0.8747_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]
escore_notes = [e for e in escore_notes if e[6] < 72 or e[6] == 128]
#=======================================================
# PRE-PROCESSING
#===============================================================================
# Augmented enhanced score notes
escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32, legacy_timings=True)
#===============================================================================
dscore = TMIDIX.enhanced_delta_score_notes(escore_notes)
cscore = TMIDIX.chordify_score(dscore)
#===============================================================================
score_toks = []
control_toks = []
prime_toks = []
for c in cscore:
ctime = c[0][0]
#=================================================================
chord = sorted(c, key=lambda x: -x[5])
gnotes = []
gdrums = []
for k, v in groupby(chord, key=lambda x: x[5]):
if k == 128:
gdrums.extend(sorted(v, key=lambda x: x[3], reverse=True))
else:
gnotes.append(sorted(v, key=lambda x: x[3], reverse=True))
#=================================================================
chord_toks = []
ctoks = []
ptoks = []
chord_toks.append(ctime)
ptoks.append(ctime)
if gdrums:
chord_toks.extend([e[3]+128 for e in gdrums] + [128])
ptoks.extend([e[3]+128 for e in gdrums] + [128])
else:
chord_toks.append(128)
ptoks.append(128)
if gnotes:
for g in gnotes:
durs = [e[1] // 4 for e in g]
clipped_dur = max(1, min(31, min(durs)))
chan = max(0, min(8, g[0][5] // 8))
chan_dur_tok = ((chan * 32) + clipped_dur) + 256
ctoks.append([chan_dur_tok, len(g)])
ptoks.append(chan_dur_tok)
ptoks.extend([e[3]+544 for e in g])
score_toks.append(chord_toks)
control_toks.append(ctoks)
prime_toks.append(ptoks)
print('Done!')
print('=' * 70)
#==================================================================
print('Sample output events', prime_toks[:16])
print('=' * 70)
print('Generating...')
#==================================================================
def generate_continuation(num_prime_tokens, num_gen_tokens):
x = torch.tensor(prime_toks[:num_prime_tokens], dtype=torch.long, device=DEVICE)
with ctx:
out = model.generate(x,
num_gen_tokens,
filter_logits_fn=top_k,
filter_kwargs={'k': input_model_top_k},
temperature=input_model_temperature,
return_prime=True,
verbose=True)
y = out.tolist()[0]
return y
#==================================================================
def generate_tokens(seq, max_num_ptcs=10):
input = copy.deepcopy(seq)
input = input[-input_num_memory_tokens:]
pcount = 0
y = 545
gen_tokens = []
while pcount < max_num_ptcs and y > 255:
x = torch.tensor(input, dtype=torch.long, device=DEVICE)
with ctx:
out = model.generate(x,
1,
filter_logits_fn=top_k,
filter_kwargs={'k': input_model_top_k},
temperature=input_model_temperature,
return_prime=False,
verbose=False)
y = out[0].tolist()[0]
if pcount < max_num_ptcs and y > 255:
input.append(y)
gen_tokens.append(y)
if y > 544:
pcount += 1
return gen_tokens
#==================================================================
song = []
if input_gen_type == 'Freestyle':
output = generate_continuation(input_number_prime_tokens, input_number_gen_tokens)
song.extend(output)
else:
for i in range(input_number_prime_chords):
song.extend(prime_toks[i])
for i in tqdm.tqdm(range(input_number_prime_chords, input_number_prime_chords+input_number_gen_chords)):
song.extend(score_toks[i])
if control_toks[i]:
for ct in control_toks[i]:
if input_use_original_durations:
song.append(ct[0])
if input_match_original_pitches_counts:
out_seq = generate_tokens(song, ct[1])
else:
out_seq = generate_tokens(song)
song.extend(out_seq)
print('=' * 70)
print('Done!')
print('=' * 70)
#===============================================================================
print('Rendering results...')
print('=' * 70)
print('Sample INTs', song[:15])
print('=' * 70)
if len(song) != 0:
song_f = []
time = 0
dur = 32
channel = 0
pitch = 60
vel = 90
patches = [0, 10, 19, 24, 35, 40, 52, 56, 65, 9, 0, 0, 0, 0, 0, 0]
velocities = [80, 100, 90, 100, 110, 100, 100, 100, 100, 110]
for ss in song:
if 0 <= ss < 128:
time += ss * 32
if 128 < ss < 256:
song_f.append(['note', time, 32, 9, ss-128, velocities[9], 128])
if 256 < ss < 544:
dur = ((ss-256) % 32) * 4 * 32
channel = (ss-256) // 32
if 544 < ss < 672:
patch = channel * 8
pitch = ss-544
song_f.append(['note', time, dur, channel, pitch, velocities[channel], patch])
fn1 = "Guided-Rock-Music-Transformer-Composition"
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
output_signature = 'Guided Rock 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("Guided Rock Music Transformer
")
gr.Markdown("Generate unique rock music compositions with source augmented RoPE music transformer
")
gr.Markdown(
"\n\n")
gr.Markdown("## Upload your MIDI or select a sample example MIDI below")
gr.Markdown("### For best results use MIDIs with 1:2 notes to drums ratio")
input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
gr.Markdown("## Select generation type")
input_gen_type = gr.Radio(["Controlled", "Freestyle"], label="Generation type")
gr.Markdown("## Controlled generation options")
input_number_prime_chords = gr.Slider(0, 512, value=128, step=8, label="Number of prime chords")
input_number_gen_chords = gr.Slider(16, 1024, value=512, step=8, label="Number of chords to generate")
input_use_original_durations = gr.Checkbox(label="Use original durations", value=True)
input_match_original_pitches_counts = gr.Checkbox(label="Match original pitches counts", value=True)
gr.Markdown("## Freestyle continuation options")
input_number_prime_tokens = gr.Slider(0, 1024, value=512, step=16, label="Number of prime tokens")
input_number_gen_tokens = gr.Slider(0, 3072, value=1024, step=16, label="Number of tokens to generate")
gr.Markdown("## Model options")
input_num_memory_tokens = gr.Slider(1024, 4096, value=2048, step=16, label="Number of memory tokens")
input_model_temperature = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Model temperature")
input_model_top_k = gr.Slider(1, 50, value=10, step=1, label="Model sampling top k value")
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(Generate_Rock_Song, [input_midi,
input_gen_type,
input_number_prime_chords,
input_number_gen_chords,
input_use_original_durations,
input_match_original_pitches_counts,
input_number_prime_tokens,
input_number_gen_tokens,
input_num_memory_tokens,
input_model_temperature,
input_model_top_k,
],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
gr.Examples(
[["Come To My Window.mid", "Controlled", 256, 512, False, False, 512, 1024, 2048, 0.9, 10],
["Sharing The Night Together.kar", "Controlled", 256, 512, True, True, 512, 1024, 2048, 0.9, 10],
["Hotel California.mid", "Controlled", 256, 512, True, True, 512, 1024, 2048, 0.9, 10],
["Nothing Else Matters.kar", "Controlled", 256, 512, True, True, 512, 1024, 2048, 0.9, 10],
],
[input_midi,
input_gen_type,
input_number_prime_chords,
input_number_gen_chords,
input_use_original_durations,
input_match_original_pitches_counts,
input_number_prime_tokens,
input_number_gen_tokens,
input_num_memory_tokens,
input_model_temperature,
input_model_top_k,
],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
Generate_Rock_Song,
cache_examples=True,
)
app.queue().launch()